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The 10 Research Topics in the Internet of Things

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Top 10+ IoT Research Topics for 2024 [With Source Code]

Home Blog others Top 10+ IoT Research Topics for 2024 [With Source Code]

With new applications being created every day, the Internet of Things (IoT) is one of the technologies that is expanding the fastest in the world right now. The Internet of Things (IoT) is a network of physical objects like cars, appliances, and other household things that are equipped with connectivity, software, and sensors to collect and share data. IoT is revolutionizing the way we live and work, creating new opportunities for businesses, governments, and individuals alike.

In this blog, we will discuss the top 10 Internet of Things research topics and ideas for 2024. We will also provide a comprehensive guide on how to choose the best IoT research topic and discuss some of the challenges and ethical considerations in IoT research.

Meanwhile, if you’ve always been fascinated by the world of coding and design and looking for the ideal place to get started, then Web Development Certificate online is one of the best certifications course you can consider.

IoT: Overview

IoT has numerous applications in various sectors such as healthcare, agriculture, transportation, manufacturing, and smart cities. The data collected from IoT devices can be used to improve decision-making, optimize processes, and enhance customer experiences. If you want to know more about IoT, check out online IoT training .

IoT Research Topics in 2024

Come let’s discuss the top X IoT-based research topics and ideas for 2024.

1. Smart Homes

The idea of a smart home is gaining popularity, and with IoT technology, it has become possible to control and automate various devices in a house. Some of the popular smart home projects include smart lighting, smart security, smart thermostat, and smart appliances.

Smart Lighting: Smart lighting refers to the use of IoT technology to control the lighting of a house. This can be done by using sensors that detect the presence of people in a room and adjust the lighting accordingly. For example, when someone enters a room, the lights automatically turn on, and when the person leaves, the lights turn off. This can aid in energy conservation and lower electricity costs.
Smart Security: Smart security refers to the use of IoT technology to enhance the security of a house. This can be done by using sensors and cameras that detect any suspicious activity and alert the homeowners. Smart security can also include features such as remote access control, automatic locking, and real-time monitoring.
Smart Thermostat: Smart thermostat refers to the use of IoT technology to control the temperature of a house. This can be done by using sensors that detect the temperature of each room and adjust the thermostat accordingly. The ability to remotely operate a smart thermostat can aid in energy conservation and lower electricity costs.

2. Wearable Devices

Wearable devices such as smartwatches, fitness trackers, and medical devices are becoming increasingly popular. IoT technology can be used to develop wearable devices that can collect and analyze data, monitor health parameters, and provide real-time feedback to the user.

Smartwatches: Smartwatches refer to the use of IoT technology to develop watches that can perform various functions such as making phone calls, sending messages, and tracking fitness. Smartwatches can also be integrated with other devices such as smartphones and laptops.
Fitness Trackers: Fitness trackers refer to the use of IoT technology to develop devices that can track physical activity, monitor heart rate, and measure calories burned. Fitness trackers can be used to improve health and fitness and can also be integrated with other devices such as smartphones and laptops.
Medical Devices: Medical devices refer to the use of IoT technology to develop devices that can monitor and track various health parameters such as blood pressure, glucose levels, and oxygen saturation. Medical devices can be used to improve patient care and can also be integrated with other devices such as smartphones and laptops.

3. Smart Agriculture

IoT technology can be used to develop smart agriculture solutions that can improve crop yields, reduce water consumption, and increase efficiency. Some of the popular smart agriculture projects include precision farming, soil monitoring, and crop monitoring.

Precision Farming: Precision farming refers to the use of IoT technology to develop farming techniques that can help farmers optimize their crop yields. This can be done by using sensors that detect soil moisture, temperature, and nutrient levels, and adjusting the amount of water and fertilizer used accordingly.
Soil Monitoring: Soil monitoring refers to the use of IoT technology to develop devices that can monitor soil conditions such as pH levels, temperature, and moisture content. Soil monitoring can help farmers make informed decisions about crop management and reduce the amount of water and fertilizer used.
Crop Monitoring: Crop monitoring refers to the use of IoT technology to develop devices that can monitor crop growth and health. This can be done by using sensors that detect the amount of sunlight, temperature, and humidity, and provide real-time feedback to farmers. Crop monitoring can help farmers identify and address any issues that may affect crop growth and yield.

4. Smart Cities

Smart cities refer to the use of IoT technology to develop cities that are more efficient, sustainable, and livable. Some of the popular smart city projects include smart transportation, smart energy, and smart waste management.

Smart Transportation: Smart transportation refers to the use of IoT technology to develop transportation solutions that are more efficient and sustainable. This can include features such as real-time traffic monitoring, intelligent traffic routing, and smart parking.
Smart Energy: Smart energy refers to the use of IoT technology to develop energy solutions that are more efficient and sustainable. This can include features such as smart grids, renewable energy sources, and energy-efficient buildings.
Smart Waste Management: Smart waste management refers to the use of IoT technology to develop waste management solutions that are more efficient and sustainable. This can include features such as smart bins that detect when they are full and automatically alert waste collection services.

5. Industrial IoT

Industrial IoT refers to the use of IoT technology to develop solutions that can improve efficiency and productivity in industries such as manufacturing, transportation, and logistics. Some of the popular industrial IoT projects include predictive maintenance, asset tracking, and supply chain optimization.

Predictive Maintenance: Predictive maintenance refers to the use of IoT technology to develop maintenance solutions that can detect and address issues before they become major problems. This can include features such as real-time monitoring of machinery and equipment, and predictive analytics that can identify potential issues.
Asset Tracking: Asset tracking refers to the use of IoT technology to develop solutions that can track the location and status of assets such as machinery and vehicles. This can include features such as real-time tracking, geofencing, and alert notifications.
Supply Chain Optimization: Supply chain optimization refers to the use of IoT technology to develop solutions that can optimize supply chain operations such as inventory management, logistics, and shipping. This can include features such as real-time tracking of shipments, predictive analytics, and automated inventory management.

6. Smart Health

Smart health refers to the use of IoT technology to develop solutions that can improve patient care, reduce costs, and enhance overall health outcomes. Some of the popular smart health projects include remote patient monitoring, medication management, and personalized health tracking.

Remote Patient Monitoring: Remote patient monitoring refers to the use of IoT technology to monitor patients remotely and provide real-time feedback to healthcare providers. This can include features such as wearable devices that monitor vital signs and alert healthcare providers if any issues arise.
Medication Management: Medication management refers to the use of IoT technology to develop solutions that can help patients manage their medications more effectively. This can include features such as smart pillboxes that remind patients to take their medications and alert healthcare providers if medications are missed.
Personalized Health Tracking: Personalized health tracking refers to the use of IoT technology to develop solutions that can track and analyze individual health data such as activity levels, sleep patterns, and dietary habits. This can help individuals make informed decisions about their health and well-being.

7. Smart Retail

Smart retail is an emerging application of IoT technology that is changing the way we shop. The goal of smart retail is to provide customers with a more personalized and efficient shopping experience while also improving the efficiency and profitability of retailers. Here are some more details on some popular smart retail applications:

Smart Shelves: Smart shelves are shelves equipped with IoT sensors that detect when products are running low or out of stock. This data is sent to the retailer's inventory management system, which can then automatically order more inventory. Smart shelves can also be used to display product information, promotions, and customer recommendations.
Smart Inventory Management: Smart inventory management refers to the use of IoT technology to track inventory levels in real time. This can help retailers to optimise their inventory levels, reduce waste, and avoid stockouts. Smart inventory management can also help retailers to automate their ordering and fulfilment processes.
Personalized Shopping Experiences: Personalized shopping experiences refer to the use of IoT technology to provide customers with tailored product recommendations and promotions. This can be done by analyzing customer data, such as purchase history and browsing behavior, and using machine learning algorithms to generate personalized recommendations.

8. Energy IoT

The energy industry is also poised for transformation through the use of IoT technology. Energy IoT solutions can help companies optimize energy usage, reduce waste, and improve sustainability. Some project ideas for energy IoT include:

Smart Grids: A system that uses sensors and data analytics to optimize the distribution of energy, reducing waste and improving efficiency.
Energy Management: A system that uses sensors to monitor energy usage in buildings, identifying areas where energy usage can be reduced and optimizing the energy usage of appliances and lighting.
Renewable Energy Monitoring: A system that uses sensors to monitor the performance of renewable energy systems, optimizing energy production and reducing downtime.

9. Transportation IoT

IoT technology is also transforming the way we move people and goods. Transportation IoT solutions can help optimize transportation networks, reduce traffic congestion, and improve safety. Some project ideas for transportation IoT include:

Connected Vehicles: Vehicles that are equipped with sensors and connectivity, allowing them to communicate with each other and with infrastructure to optimize traffic flow and improve safety.
Intelligent Transportation Systems: A system that uses sensors and data analytics to optimize traffic flow, reducing congestion and improving safety.
Smart Parking: A system that uses sensors and data analytics to optimize parking availability, reducing search times and improving the parking experience for drivers.

10. Hospitality IoT

IoT technology can help hotels and other hospitality businesses improve the guest experience, increase efficiency, and reduce costs. Some project ideas for hospitality IoT include:

Smart Room Controls: A system that uses sensors and connectivity to allow guests to control lighting, temperature, and other room features from their smartphones or other devices.
Asset Tracking: A system that uses sensors to track the location and condition of hotel assets, improving supply chain visibility and reducing the risk of theft or loss.
Guest Analytics: A system that uses sensors to track guest behavior and preferences, allowing hotels to offer personalized recommendations and improve the guest experience.

11. Aerospace IoT

IoT technology can help aerospace companies improve safety, increase efficiency, and reduce costs. Some project ideas for aerospace IoT include:

Predictive Maintenance: A system that uses sensors and data analytics to predict when aircraft equipment is likely to fail, allowing for maintenance to be performed before a breakdown occurs.
Fuel Optimization: A system that uses sensors and data analytics to optimize fuel usage, reducing waste and increasing efficiency.
Air Traffic Management: A system that uses sensors and data analytics to optimize air traffic flow, reducing congestion and improving safety.

Top Futuristic IoT Research Ideas

Human-Computer Interaction: Develop interfaces that can interpret human behavior and emotions to enhance IoT systems' responsiveness and personalization.
Augmented Reality and IoT: Combine IoT with augmented reality to create immersive experiences in areas such as education, entertainment, and marketing.
Quantum Computing and IoT: Investigate how quantum computing can enhance IoT systems' performance, security, and scalability.
Swarm Intelligence and IoT: Explore how swarm intelligence can be applied to IoT systems to enable self-organizing and self-healing networks.
IoT and 5G: Investigate how 5G networks can enhance IoT systems' performance, reliability, and scalability.
Smart Cities and IoT: Develop smart city solutions that can improve urban planning, transportation, energy efficiency, and citizen engagement.

How to Choose the Best IoT Research Topic?

Choosing the best IoT research topic can be a challenging task. Here are some tips to help you choose the best IoT research topic:

Think on how feasible and useful the research is: Choose a topic that aligns with your interests and passions to stay motivated and engaged throughout the research process.
Identify emerging trends and challenges: Choose a topic that addresses emerging trends and challenges in the IoT industry to make a significant contribution to the field.
Consider the feasibility and practicality of the research: Choose a topic that is feasible and practical to research given the available resources, expertise, and time constraints.
Seek input from experts and mentors: Consult with experts and mentors in the field to get feedback and guidance on potential research topics.
Evaluate the potential impact of the research: Choose a topic that has the potential to make a significant impact on the IoT industry or society as a whole.

Things to Consider While Choosing IoT Research Topics

Here are some additional things to consider while choosing IoT topics for research:

Ethical considerations: Consider the ethical implications of the research, such as data privacy, security, and transparency.
Interdisciplinary nature: Consider the interdisciplinary nature of IoT research and seek to collaborate with experts from different fields to broaden the scope of the research.
Data management: Consider how to manage the massive amount of data generated by IoT devices and ensure the accuracy, reliability, and integrity of the data.
Scalability: Consider how to design IoT systems that can scale up to accommodate the increasing number of devices and data.

IoT is a rapidly growing field that offers numerous opportunities for research and innovation. In this blog, we discussed the top 10 research topics on IoT for 2024, as well as some futuristic IoT research ideas. We also provided a comprehensive guide on how to choose the best IoT research topic and discussed some of the challenges and ethical considerations in IoT research. By choosing the right research topic and addressing emerging trends and challenges, you can make a significant contribution to the IoT industry and society as a whole. In addition to the project, you can also take advantage of KnowledgeHut Software Development Certification training to learn multiple programming languages and enhance your value in the job market.

Frequently Asked Questions (FAQs)

IoT research involves studying the technologies, applications, and challenges related to the Internet of Things (IoT) to develop new solutions and improve existing ones.

Some current trends in IoT research include edge computing, machine learning and artificial intelligence (AI), security and privacy, and smart cities.

IoT research can be used in industry to develop and improve products and services, optimize processes, and enhance customer experiences. It can also help companies to reduce costs, increase efficiency, and improve safety.

Some ethical considerations in IoT research include privacy, data security, transparency, consent, and the potential for bias or discrimination.

Some challenges in IoT research include interoperability, scalability, data management and analysis, energy efficiency, and the need for standardization and regulation.

Geetika Mathur

Geetika Mathur is a recent Graduate with specialization in Computer Science Engineering having a keen interest in exploring entirety around. She have a strong passion for reading novels, writing and building web apps. She has published one review and one research paper in International Journal. She has also been declared as a topper in NPTEL examination by IIT – Kharagpur.

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IoT Research Topics 2024

Research ideas in the Internet of Things 2024

Top 20 internet of things iot research ideas 2024, iot enabling technologies, iot applications, services, and real implementations, end-user and human-centric iot, including iot multimedia, societal impacts and sustainable development, iot security, privacy, and data protection, iot pilots, testbeds, and experimentation results, top 50 iot project topics and research ideas, iot implementation and testing requirements, top 10 internet of things(iot) journals.

IoT Research Topics: The Internet of Things (IoT) is the network of physical objects—devices, vehicles, buildings, and other items embedded with electronics, software, sensors, and network connectivity—that enables these objects to collect and exchange data. Here things are uniquely identifiable nodes, primarily sensors that communicate without human interaction using IP connectivity.

A thing in IoT can be anything with sensors and internet connections. (Wrist Watch, Sunglass, TV, Car-Key, etc.) All these devices collect an enormous amount of data from every person and store it.

Once data is collected, all the data from IoT devices are transferred to data analysis, as a result of this process some useful in-depth personal recommendations will be generated also which will lead to a smart and efficient life.

In this article, ilovephd provides open research areas in Internet of Things research topics.

Since IoT is in the initial stage of development there are plenty of research opportunities available. The following are some of the key research issues in IoT

Naming and Addressing: Advertising, Searching, and Discovery
Service Orchestration and Routing
Power, Energy, Efficient resource management, and Energy Harvesting
Things to Cloud: Computation and Communication Gateways
Miniaturization: Sensors, CPU, and network
Big Data Analytics: 35 ZB of data $2B in value by 2024
Semantic technologies: Information and data models for interoperability
Virtualization: Multiple sensors aggregated, or a sensor shared by multiple users
Privacy/Security/Trust/Identity/Anonymity Target Pregnancy Prediction
Heterogeneity/Dynamics/Scale is an open research topic in IoT.
Investigating the Potential of AI-Based Automation for IoT-Enabled Smart Homes
Exploring the Impact of 5G Networks on the Internet of Things
Developing Secure and Scalable IoT-Based Solutions for Smart Cities
Researching the Potential of Blockchain Technology for Securing IoT Networks
Investigating the Role of Edge Computing in the Internet of Things
Examining the Impact of IoT on Wearable Technology
Exploring the Use of IoT for Automated Vehicle Management
Investigating the Potential of IoT for Smart Agriculture
Examining the Use of IoT for Smart Manufacturing
Investigating the Potential of IoT for Smart Healthcare Solutions
Exploring the Role of IoT in Smart Energy Management
Investigating the Use of IoT for Smart Retail Solutions
Examining the Impact of IoT on Smart Home Security
Exploring the Role of IoT in Smart Logistics
Investigating the Use of IoT for Smart Waste Management
Examining the Potential of IoT for Smart Water Management
Exploring the Role of IoT in Smart Mobility Solutions
Investigating the Use of IoT for Smart Education Solutions
Examining the Impact of IoT on Smart Tourism Solutions
Exploring the Role of IoT in Smart Air Quality Monitoring

Generalized Open IoT Research Topics

5G Networks and IoT
IoT Security and Trust
IoT and Personal Data Protection
Artificial Intelligence and IoT
IoT Large-Scale Pilots and Portability
IoT Interoperability and Multi-Platform Integration
Software-Defined Networks (SDN), Network Function Virtualization (NFV) and IoT
Sensor and Actuator Networks
IoT Protocols and Standards (IPv6, 6LoWPAN, RPL, 6TiSCH, WoT, oneM2M, etc.)
Ultra-low-power IoT Technologies and Embedded Systems Architectures
Wearables , Body Sensor Networks, Smart Portable Devices
Design Space Exploration Techniques for IoT Devices and Systems
Heterogeneous Networks, Web of Things, Web of Everything
Named Data Networking for IoT
Internet of Nano Things
Sensors Data Management, IoT Mining and Analytics
Adaptive Systems and Models at Runtime
Distributed Storage, Data Fusion
Routing and Control Protocols
Resource Management, Access Control
Mobility, Localization, and Management Aspects
Identity Management and Object Recognition
Localization Technologies
Edge Computing, Fog Computing and IoT
Machine to Machine (M2M)/Devices-to-Devices communications and IoT
Industrial IoT and Factory of Things and the Internet of Things
Smart Cities, Smart Public Places, and Smart Environments
Smart Home, and IoT-based Building Automation
Smart Agriculture and Water Management
Smart Factories and Industry 4.0
e-Health, Assisted Living and e-Wellness
Automotive, Intelligent Transport
IoT-based Supply Chains
Smart Grid, Energy Management
Cyber-physical systems, Context Awareness, Situation Awareness, Ambient Intelligence
Collaborative Applications and Systems
Service Experiences and Analysis
Consumer Electronics, Assisted Living, Rural Services and Production
Industrial IoT Service Creation and Management Aspects
Crowd-sensing, human-centric sensing
Big Data and IoT Data Analytics
IoT and AI techniques
Internet Applications Naming and Identifiers
Semantic Technologies, Collective Intelligence
Cognitive and Reasoning about Things and Smart Objects
Mobile Cloud Computing (MCC) and IoT
Horizontal application development for IoT
Design principals and best practices for IoT application development

Human Role in the IoT, Social Aspects, and Services
Value Chain Analysis and Evolution Aspects
New Human-Device Interactions for IoT, Do-It-Yourself
Social Models and Networks
IoT and Arts
Green IoT: Sustainable Design and Technologies
Urban Dynamics and crowdsourcing services
Metrics, Measurements, and Evaluation of IoT Sustainability and Roi
IoT and blockchain
Artificial Intelligence-based security and data protection
IoT Privacy, data protection, and Security Concerns
IoT Privacy and Security Tests, Certification, and Labelling
Security and Data protection risk analysis and mitigation
Identification and Authentication Issues
Wireless Sensor Network for IoT Security
Intrusion Detection in IoT
Cryptography, Key Management, Authentication and Authorization for IoT
Physical/MAC/Network Attacks on the Internet of Things
Cross-layer Attacks in IoT
Security with QoS Optimization in IoT
Privacy based Channel Access in IoT
IoT Forensic Science
Big Data and Information Integrity in IoT
Communication Security in IoT
Security Standards in IoT

Large-scale pilots on IoT
IoT testbeds and testing tools
Closing the Gap Between Research and Implementation
Experimental prototypes , Testbed, and Field Trial Experiences
Multi-Objective IoT System Modeling and Analysis—Performance, Energy, Reliability, Robustness
IoT Interconnections Analysis—QoS, Scalability, Performance, Interference
Real-case deployment scenarios and results
IoT Deployment at Government and ISPs
IoT Deployment in Agriculture, Retail, Smart Cities, etc.
IoT Interconnections among ISPs Analysis—QoS, Scalability, Performance, Interference
Gaps Analysis for Real Deployment
IoT and Future Internet Architectures
Standardization and Regulation
Security and Privacy in the Internet of Things
Machine Learning for IoT Applications
Edge Computing for IoT
Connected Devices and Smart Homes
Autonomous Vehicles and the IoT
Big Data Analytics for IoT
Cloud Computing for IoT
Wearable Technology and IoT
Real-Time Monitoring of IoT Devices
5G Networks and the IoT
Artificial Intelligence and the IoT
Blockchain and the IoT
Network Protocols for IoT
Low-Power Networks for IoT
Wireless Sensor Networks and IoT
Smart City Applications and IoT
Industrial Internet of Things
Augmented Reality and IoT
Smart Grid and IoT
Predictive Maintenance and IoT
Location-Based Services and IoT
Voice-Enabled IoT Applications
Home Automation and IoT
Smart Retail and IoT
Connected Health and IoT
Smart Farming and IoT
Autonomous Robotics and IoT
Smart Manufacturing and IoT
Connected Cars and IoT
Smart Energy and IoT
Smart Water Management and IoT
Smart Buildings and IoT
Smart Waste Management and IoT
Smart Lighting and IoT
Smart Education and IoT
Smart Tourism and IoT
Smart Transportation and IoT
Smart Logistics and IoT
Smart Environment and IoT
Smart Government and IoT
Smart Agriculture and IoT
Smart Security and IoT
Smart Metering and IoT
Smart Textiles and IoT
Smart Retail Analytics and IoT
Smart Healthcare Analytics and IoT
Smart City Analytics and IoT
Smart Home Analytics and IoT
Smart Grid Analytics and IoT
Smart Manufacturing Analytics and IoT

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The 10 research topics in the Internet of Things

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Research output : Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Since the term first coined in 1999 by Kevin Ashton, the Internet of Things (IoT) has gained significant momentum as a technology to connect physical objects to the Internet and to facilitate machine-to-human and machine-to-machine communications. Over the past two decades, IoT has been an active area of research and development endeavors by many technical and commercial communities. Yet, IoT technology is still not mature and many issues need to be addressed. In this paper, we identify 10 key research topics and discuss the research problems and opportunities within these topics.

Publication series

Internet of Things
Energy Harvesting
Recommendation
Summarization
Conversational IoT
IoT Service Discovery

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10.1109/CIC50333.2020.00015

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Internet of things Engineering & Materials Science 100%
Machine-to-machine communication Engineering & Materials Science 54%
Momentum Engineering & Materials Science 36%
Internet Engineering & Materials Science 27%

Projects per year

Efficient Management of Things for the Future World Wide Web

Sheng, M. & Mans, B.

1/01/17 → …

Project : Research

What Can You Trust in the Large and Noisy Web?

Sheng, M. , Yang, J. , Zhang, W. & Dustdar, S.

1/05/20 → 30/04/23

A Large-Scale Distributed Experimental Facility for the Internet of Things

Sheng, M. , Bouguettaya, A., Loke, S., Li, X., Liang, W., Benattalah, B., Ali Babar, M., Yang, J. , Zomaya, A. Y., Wang, Y. , Zhou, W., Yao, L., Taylor, K. & Bergmann, N.

1/01/18 → 31/12/20

T1 - The 10 research topics in the Internet of Things

AU - Zhang, Wei Emma

AU - Sheng, Quan Z.

AU - Mahmood, Adnan

AU - Tran, Dai Hoang

AU - Zaib, Munazza

AU - Hamad, Salma Abdalla

AU - Aljubairy, Abdulwahab

AU - Alhazmi, Ahoud Abdulrahmn F.

AU - Sagar, Subhash

AU - Ma, Congbo

N2 - Since the term first coined in 1999 by Kevin Ashton, the Internet of Things (IoT) has gained significant momentum as a technology to connect physical objects to the Internet and to facilitate machine-to-human and machine-to-machine communications. Over the past two decades, IoT has been an active area of research and development endeavors by many technical and commercial communities. Yet, IoT technology is still not mature and many issues need to be addressed. In this paper, we identify 10 key research topics and discuss the research problems and opportunities within these topics.

AB - Since the term first coined in 1999 by Kevin Ashton, the Internet of Things (IoT) has gained significant momentum as a technology to connect physical objects to the Internet and to facilitate machine-to-human and machine-to-machine communications. Over the past two decades, IoT has been an active area of research and development endeavors by many technical and commercial communities. Yet, IoT technology is still not mature and many issues need to be addressed. In this paper, we identify 10 key research topics and discuss the research problems and opportunities within these topics.

KW - Internet of Things

KW - Energy Harvesting

KW - Recommendation

KW - Search

KW - Summarization

KW - Conversational IoT

KW - IoT Service Discovery

UR - http://www.scopus.com/inward/record.url?scp=85100752198&partnerID=8YFLogxK

UR - http://purl.org/au-research/grants/arc/DP200102298

UR - http://purl.org/au-research/grants/arc/LE180100158

UR - http://purl.org/au-research/grants/arc/FT140101247

U2 - 10.1109/CIC50333.2020.00015

DO - 10.1109/CIC50333.2020.00015

M3 - Conference proceeding contribution

AN - SCOPUS:85100752198

T3 - Proceedings - 2020 IEEE 6th International Conference on Collaboration and Internet Computing, CIC 2020

BT - Proceedings - 2020 IEEE 6th International Conference on Collaboration and Internet Computing, CIC 2020

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 6th IEEE International Conference on Collaboration and Internet Computing, CIC 2020

Y2 - 1 December 2020 through 3 December 2020

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Charting an integrated future: IoT and 5G research papers

The fifth-generation cellular network (5G) represents a major step forward for technology. In particular, it offers benefits for the network of interrelated devices reliant on wireless technology for communication and data transfer, otherwise known as the Internet of Things (IoT).

The 5G wireless network uses Internet Protocol (IP) for all communications, including voice and short message service (SMS) data. Compared to earlier networks, such as 3G and 4G, it will have higher response speeds (lower latency), greater bandwidth, and support for many more devices.

Every sector is using some form of wireless-enabled technology. Low latency plays a critical role in many IoT applications where a lag in data transfer to an IoT device can mean a disruption in the manufacturing process, a crashed car, or a disrupted power grid. Increased capacity to support IoT devices means more of the world’s population will be able to access the global digital economy.

Yet with more capability comes more complexity, and there are challenges to making 5G connection a full reality. There is global interest in realizing the potential of 5G and IoT integration. Research papers on a wide array of topics are helping to advance the field and bring the vision of 5G technology and IoT connectivity into focus.

Realizing the potential of 5G and IoT through research

The 5G network represents the best chance for an ever-growing array of wirelessly connected devices to realize their full potential .

Making the case for 5G technology

Using millimeter wave technology, 5G connectivity offers increased speed, bandwidth, and reliability of data transfers. These improvements mean that more computing power can be pushed to the cloud, clearing the way for smaller, cheaper, and simpler devices that can do more. Smartphones are a great example of how increased wireless network capacity has allowed devices to get smaller while increasing the range of a user’s cloud-based activities.

The 5G mobile network also has social justice implications. As Brookings Institute senior fellow Nicol Turner Lee discusses in her research paper “ Enabling Opportunities: 5G, the Internet of Things, and Communities of Color ,” the development of wireless networks will factor heavily in whether mobile-only users can fully participate in the global digital economy.

Universal benefits, inspired innovations

The 5G network could spur additional IoT innovations such as the following:

Advancements in edge computing
Creation of smart cities, smart power grids, and expanded functionality of smart homes
Improvements in health-care monitoring and delivery of services
Retail improvements
Real-time remote control of robots that could improve farming efficiency
Automated manufacturing
Supply chain improvements
Improved transportation and self-driving cars
Expanded use of artificial intelligence reliant on machine learning
More cloud computing
Expansion of virtual reality and augmented reality

While work to build out 5G has begun, many of the challenges and logistics of completing this vast network still need to be resolved. Some of the challenges include the following:

Managing disruption to the radio transmission
Network and wireless security
Connectivity issues from the network to the internet (known as “backhaul”)
Assuaging concerns over health impacts of increased high-speed electromagnetic energy
Cost and logistics of building a vast network of towers across different governmental jurisdictions

Those with a stake in making 5G a reality are investing in researching solutions that explore the possibilities and challenges of 5G deployment and IoT integration. Research is also emerging on how 5G and IoT technology can be utilized to respond and fight the COVID-19 pandemic.

Two halves of a whole—the relationship between IoT and 5G

5G is revolutionary in that it replaces hardware components of wireless networks with software components that offer increased system flexibility. In doing so, it delivers more power to wireless devices that rely upon fast, uninterrupted data transmission.

Making IoT smarter

Artificial intelligence (AI) technology, which plays heavily in many IoT applications, relies on smooth and frequent transmission of data. Every disruption in the data transfer process interrupts the feedback loop that facilitates machine learning. 5G’s lower latency eliminates these data hiccups, which translates to better performance over time.

The 2019 paper “ AI Management System to Prevent Accidents in Construction Zones Using 4K Cameras Based on 5G Network ,” published in the IEEE Xplore digital library, examines how workplace safety can be improved through AI technologies running on the 5G wireless platform.

Critical and massive IoT

There are two types of IoT devices: Critical IoT devices offer low latency, high uptime benefits. They facilitate bandwidth-hungry applications that include telemedicine, first responder applications, and factory automation. Massive IoT refers to a network of lots of devices using little bandwidth or speed. These devices find use in applications such as wearables, smart agriculture, smart homes, and smart cities.

5G technology also allows a service provider to dedicate portions of their networks for specific IoT applications. Known as network slicing, the ability to segment a set of optimized resources further improves the ability of 5G to respond to the varying data and bandwidth needs of critical and massive IoT applications.

The recent paper “ Secure Healthcare: 5G-enabled Network Slicing for Elderly Care,” published in the IEEE Xplore digital library, provides insight into the existing limitations in elder care and discusses a solution that encompasses 5G network slicing techniques and innovations.

Cybersecurity on the 5G

One fundamental difference between 5G and its predecessors is the shift from a hardware-based system to a software-based system. This shift presents new security challenges as software is more vulnerable to hacking—the same wireless pathways over the 5G that enable IoT can be used to breach it, whereas to hack hardware you need direct physical access.

Technical solutions to expanding capacity while increasing IoT security, such as those that the IEEE paper “ Wideband Antennas and Phased Arrays for Enhancing Cybersecurity in 5G Mobile Wireless ” discusses, are being researched and discussed worldwide. In addition, the Brookings Institute’s 2019 research paper “ Why 5G Requires a New Approach to Cybersecurity ,” discusses why developing coordinated cybersecurity public policies is of paramount importance.

Investing in the future—top research projects on IoT and 5G integration

Governments and the private sector, including trade associations, service providers, and major tech players are funding research at academic institutions. For example, the University of Texas at Austin’s Wireless Network and Communications Group has an Industrial Affiliates Program that allows companies like Huawei to become stakeholders in the center and to participate in the growth and direction of its research on millimeter waves. Similarly, New York University’s Brooklyn engineering program partners with Nokia, Intel, and AT&T to support its research.

In the US, the National Science Foundation is supporting advanced wireless research. Research England’s UK Research Partnership Investment Fund (UKRPIF) supports 5G research, including that being done at the University of Surrey’s 5G Innovation Centre . Nonprofit organizations, such as the Brookings Institute , are also conducting research on the logistics and impacts of 5G and IoT.

Universities, companies, and organizations such as IEEE regularly team up to host conferences around the world that showcase all aspects of 5G. IEEE’s Future Networks is dedicated to enabling 5G and regularly calls for papers related to 5G.

Opportunities for 5G and IoT—building a sustainable future

The ultimate goal of 5G and IoT integration is for everything to be connected more simply on smaller, less expensive devices. The 5G network has the potential to drive advancements in IoT and to fundamentally change the way humankind operates around the globe with long-term positive impacts possible with respect to sustainability.

In practical terms, the 5G network provides better efficiency through increased control. At the local level, a smart city would be better able to monitor, through IoT applications, public safety and utilities. This would mean greater conservation and a reduction in their overall carbon impact while improving the lives of its residents.

As Darrel M. West examines in his paper “ Achieving Sustainability in a 5G World ,” IoT innovation in the energy, manufacturing, agriculture and land use, buildings, and transportation sectors coupled with full 5G deployment could allow the global community to meet our long-term sustainability goals.

Want to learn more about the latest IoT and 5G research? Participate in the 2020 IEEE 3rd 5G World Forum (5GWF'20). The virtual conference, which will be available from September 10–12, aims to bring together experts from industry, academia, and research to exchange their vision as well as their achieved advances towards 5G. In addition, it aims to encourage innovative cross-domain studies, research, early deployment, and large-scale pilot showcases that address the challenges of 5G.

Interested in becoming an IEEE member ? Joining this community of over 420,000 technology and engineering professionals will give you access to the resources and opportunities you need to keep on top of changes in technology, as well as help you get involved in standards development, network with other professionals in your local area or within a specific technical interest, mentor the next generation of engineers and technologists, and so much more.

Top Internet of Things Research Frontiers of the Leaders

Over the past years, the Internet has redefined Business to Business (B2B) industries. The evolution of technology will dramatically alter manufacturing, energy, agriculture, transportation and other industrial sectors of the economy. It is already transforming how people work through new interactions between humans and machines. Dubbed the Industrial Internet of Things (IIoT), this latest wave of technological change will bring opportunities, along with many risks, to business and society. Universities are already challenged to adopt a new way of penetrating the market and researching the latest trends, that is why I think is important to know what are the top Internet of Things research frontiers topics at this moment and how companies can leverage them.

IoT Research Topics Overview

In March 2014, five big companies cofounded the Industrial Internet Consortium . As it is specified on IIConsortium website, the main objective of this entity is to bring together the organizations and technologies necessary to accelerate the growth of the Industrial Internet by identifying, assembling and promoting best practices. Membership includes small and large technology innovators, vertical market leaders, IoT researchers and top AI leaders , universities and government organizations.

From my perspective, it is imperative that in this relatively young research field there are involved not only companies but IoT researchers, leaders and universities too because, all these entities, working together, are the primary drivers of innovation and evolution.

As businesses are trying to leverage every opportunity regarding IoT by trying to find ways to partner with top universities and research centers, here is a list of the Top 20 co-occurring topics of the Top 500 Internet of Things Authors in the academic field. This gives an idea of the IoT research frontiers of the leaders.

Almost one in three IoT researchers are interested in cloud computing and wireless sensor networks. O nly one in 13 are interested in mobile computing, Artificial Intelligence, and Machine Learning; one in 14 – in Cybersecurity and even less, one in 20 are interested in smart cities (this data represents explicit interests expressed via keywords used within the published research to date of the top500 researchers & leaders in IOT).

Is that Newborn an IoT or an IIoT – How to Decide?

Introduction During the past decades, we learned about Information Technology (IT), Industrial Control Systems (ICS), Operation Technology (OT), and Supervisory Control and Data Acquisition (SCADA) systems, which manage the operation of a broad range of consumer, commercial industrial and more

Compliance versus Security: Managing Risk in an Evolving Digital World

At the heart of the S4x24 conference in Miami, Jeff Brown, Regional Director, Operational Technology, Fortinet, shared his enthusiasm not only for the event but also for his upcoming participation in a panel at IIoT World Energy Day. This discussion,

Bridging Cybersecurity Frontiers: From S4 Insights to IIoT World Energy Day Discussions

At the recent S4x24 conference, Mark Toussaint, Sr. Product Manager Cyber Security at OPSWAT shared his insights about the cybersecurity landscape's evolving challenges, particularly in operational technology (OT) and the Industrial Internet of Things (IIoT) environments. OPSWAT's engagement at S4,

100+ IoT Research Topics for Final Year Projects

The Internet of Things (IoT) is transforming various domains through interconnection of physical devices and objects with sensors, software and connectivity. For final year engineering students, IoT offers a rich space for innovation. This article provides 100+ IoT research topics and project ideas for final year students across electronics, computer science, IT and communications engineering.

The list covers various aspects of IoT including protocols, architectures, embedded systems, wireless sensor networks, data analytics, fog/edge computing, security, applications and more. Each research topic is concisely described within 150 characters to stimulate ideas for your final year IoT research or project.

IoT Protocols and Architectures

Performance evaluation of MQTT, CoAP, AMQP protocols for IoT systems
Distributed ledger technologies for secure IoT data exchange and transactions
Architectures for IoT gateways - capabilities, protocols and performance
Microservices based IoT platform architecture using containers and orchestration
Analysis of 3GPP narrowband IoT for low power wide area applications
Information centric networking for efficient IoT data distribution
Lightweight signaling protocols optimized for massive IoT deployments
Blockchain smart contracts for decentralized IoT application development
Named data networking for IoT - concepts, use cases and research challenges
Knowledge defined networking architecture for intent based management of IoT

Embedded Systems for IoT

Ultra low power MCUs and SoC for battery operated IoT devices
IoT operating systems - capabilities, performance and selection criteria
Real-time embedded systems for time critical industrial IoT applications
Hardware-software co-design methodology for IoT edge nodes
Embedded AI and tinyML for resource constrained IoT devices
Analog and mixed signal design considerations for IoT sensor nodes
Additive manufacturing of enclosures and packages for IoT products
Printed electronics manufacturing techniques for flexible IoT systems
Loose coupling and modularity principles in embedded firmware design
Approaches for reliable software development for IoT edge devices

Sensors, Actuators and Connectivity

MEMS based microhotplate gas sensors using silicon micromachining
Fabrication and characterization of flexible pressure sensors using screen printing
Resistive humidity sensors using graphene oxide thin films
3D printed soft sensors using conductive thermoplastic elastomers
Low power capacitive touch sensors and haptic actuators for wearables
Inkjet printed temperature sensor arrays on flexible substrates
Long range IoT connectivity using LoRa and Sigfox technologies
Visible light communication for IoT using LEDs and photodiodes
Characterization of radio signal propagation for indoor IoT deployments
Sound based sensing for unobtrusive activity recognition using IoT devices

IoT Wireless Sensor Networks

Cross layer protocols for energy efficient routing in IoT wireless sensor networks
Distributed algorithms for formation control of mobile sensor networks
Real-time MAC protocols for reliable and timely communication in IoT
Hybrid satellite-terrestrial connectivity for remote IoT sensor networks
Mobility enabled wireless sensor networks for IoT - architecture and protocols
Resilient topology control in wireless sensor networks under failures and attacks
Machine learning methods for predictive maintenance using industrial IoT sensors
Secure localization techniques for IoT wireless sensor networks
Blockchain for distributed coordination among IoT wireless sensor nodes
Battery-free sensors for IoT - design, prototyping and applications

IoT Data Management

Distributed SQL query engines for IoT data analytics
Complex event processing techniques for streaming IoT data
Privacy-aware access control for secure IoT data sharing between entities
Anomaly detection in IoT time-series sensory data using deep learning
Blockchain based provenance tracking of IoT data from sensors to cloud
Compressive sensing techniques for energy efficient IoT data gathering
Big data analytics for predictive maintenance in Industry 4.0
Change detection in multisensor IoT data streams using statistical methods
Ontology-based modeling for semantic interoperability in IoT systems
Digital twin models integrating historical and real-time IoT data

Fog and Edge Computing

Distributed automation using fog computing in Industrial IoT
Machine learning model training at IoT edge nodes using federated learning
Dynamic resource allocation in fog computing for IoT applications
Latency and reliability analysis of fog-cloud infrastructure for IoT
Container based microservices deployment at fog nodes
Real-time video analytics using deep learning in edge computing
Serverless computing at IoT edge - feasibility, benefits and research issues
Secure service orchestration across fog, edge and cloud computing
Energy efficient task offloading from IoT devices to fog resources
Vehicular fog computing using connected cars - architectures and use cases

IoT System Design and Testing

Rapid IoT prototyping using mbed and Arduino platforms
3D printing customized IoT enclosures using CAD modeling
Modern PCB design techniques for compact IoT circuits
EMI/EMC considerations for reliable IoT product design
IoT network modeling and simulation tools - comparative evaluation
IoT device energy profiling and battery life estimation techniques
Hardware-in-loop testing solutions for IoT systems
GUI design and HMI considerations for local control of IoT devices
Continuous user experience testing framework for IoT applications
Rapid IoT device deployment and configuration at scale

IoT Security and Privacy

Distributed ledger technologies for secure firmware updates in IoT devices
Lightweight authentication protocols optimized for resource constrained IoT nodes
Federated learning for user privacy preservation in IoT applications
Blockchain smart contracts for access control in IoT data exchange
Intrusion detection systems tailored for IoT networks and edge devices
Cryptographic engineering for IoT - efficient crypto schemes for sensors
Fingerprinting techniques for IoT device identification and authentication
Differential privacy mechanisms to anonymize sensitive IoT data
Remote attestation techniques for trusted execution of IoT workloads
Zero trust security model for enterprise IoT deployments

IoT Applications

Digital agriculture and smart farming solutions using IoT
IoT for Industry 4.0 and industrial automation
IoT in healthcare - remote patient monitoring and connected medical devices
Smart homes and buildings using IoT and digital twins
IoT in retail and logistics for tracking goods and monitoring inventory
Smart grids and renewable energy integration using IoT
Intelligent transportation systems using vehicular IoT
Environmental monitoring IoT systems for air/water quality sensing
IoT applications for infrastructure monitoring in civil engineering
IoT in defense equipment and battlefield awareness systems

Miscellaneous IoT Research Topics

IoT standards and regulations - survey and critical analysis
Techno-economic analysis and feasibility studies for enterprise IoT
IoT solution engineering - from concept to commissioning
IoT APIs, SDKs and cloud services - comparative analysis
Emerging IoT development boards, devices and tools overview
UX and UI design principles for IoT dashboards and analytics
IoT adoption case studies across application domains
IoT startup ecosystem analysis and business models
IoT datasets - survey, characteristics and research opportunities
Role of IoT in smart cities - case studies and impact assessment

This compilation covers over 100 IoT research topics for final year engineering students to choose from. Identify an interesting topic that matches your expertise and practical skills. IoT is a rich interdisciplinary area for innovation with immense potential for research. Discuss tentative ideas with faculty advisors and industry mentors during your topic selection phase. Starting early and planning your project methodology thoroughly is key for success.

Q1. How do I select a good final year IoT research topic?

Tips for choosing a good final year IoT research topic:

Select a focused problem in IoT that interests you
Ensure topic has real-world relevance and applications
Align with your specialization, skills and capabilities
Explore leading IoT conferences and journals for new ideas
Leverage industry collaborations and mentorships
Identify technology gaps and scope for innovation
Choose a topic with defined goals, scope and deliverables
Discuss ideas with faculty guide for constructive feedback

Q2. What are some good sources to find IoT research topics?

Some fruitful sources to find IoT research topics:

Recent advances and trends in IoT technology
IoT publications - journals, magazines, conference papers
Challenges and problems faced by industry seeking IoT solutions
Government funded IoT research programs
Faculty research areas and experts at your university
IoT research labs and university collaborations with industry
IoT blogs, enthusiasts forums, tech news sites
Technology reports from leading consulting firms
IEEE, ACM articles and publications
Product releases, demos from IoT technology vendors

Q3. How should the IoT research report be structured?

The final year IoT research project report should contain:

Title, student details, abstract
Introduction - topic background, problem statement, objectives
Literature review - summarize previous work and background concepts
Proposed solution and methodology - detailed design, simulations, experiments
Results - data, graphs, tables, performance metrics, models developed
Discussion - analyze results, compare with state of art
Conclusion - key outcomes, limitations, future work
References - cite sources properly

Include relevant charts, graphs, figures, photos, code, schematics etc. in the appendix. Follow your university style guide.

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Iot Technology – 21 Hot Research Topics in Internet of Things

Iot technology – hot research topics in internet of things.

IoT Technology – Hot Research Topics in Internet of Things – There is a variety of IoT domains to do research for Ph.D. Thesis, M.Tech, B.Tech, M.Sc and MCA dissertations, etc. IoT has a huge domain for the researchers.

Some important IoT technology research areas are suggested here to facilitate the students of Ph.D., and postgraduation. Research areas and research topics are suggested here.

In addition to this, what you can do under the research topic is also mentioned for your help. An idea is given here. You can take these topics and also can take similar topics for your research work

Research Area- Internet of things for smart cities

Various research areas are there to make the cities, villages, and campuses IoT enabled and smart to provide a quality culture. Some of the research areas in this domain are as below-

Research Topic Example – “ Impact of Internet of Things Applications and Smart Villages”

In this study, the researcher can study the IoT applications and their impact on today’s life and how these IoT applications can be implemented in villages to make them smart and comfortable.

Research Topic Example – “ IoT enabled home appliances managing and monitoring system “

The research topic focuses on IoT technology enabled home appliances managing and monitoring system, using AI algorithms to take care and manage home appliances to provide secure and effective smart homes to the people.

Research Topic Example – “ Optimizing IoT enabled services in the Smart Cities “.

The topic focuses optimization of IoT technology enabled services in smart cities to remove present challenges and provide better life culture.

Research Area – Internet of things in business

Due to recent advancements in technology every business is today more or less based on the Internet of things. There is a need for huge research in the domain to make the country a developed country. Some of the research areas are here to facilitate the new researchers.

Research Topic Example – “ Internet Of Things In Cross-Domain Integration And Smart Business Model”

The study focuses on the development and implementation of the Internet of Things Modeling architecture for Optimizing Operational Efficiency and Employee Productivity in Indian Private and Public Sector.

Research Topic Example – “ Design And Development Of Automation System For Industry Specific Process Parameters Through Internet Of Things “

The study focuses on designing and development of IoT technology based Industry automation through implementing some industry-specific parameters in order to optimize industry functioning and productivity in terms of product and employee both. The parameters may vary from industry to industry.

Research Topic Example – “ Optimizing Real-Time Internet Of Things Data Using Big Data Computing Platform”

Recently, Big Data and the Internet of Things ( IoT technology ) are two accepted technical terms in the IT industry. The computing of IoT applications produced data consumes more energy due to high velocity in real-time.

The research study focuses on energy issues and response time of IoT applications to optimize them to use the data more efficiently and effectively.

Research Area – Internet of things temperature sensor

Internet of things temperature sensors, soil sensors, and climate testing devices can be used to manage and monitor natural resources. Some of the areas of research in this domain are as below-

Research Topic Example – “ Development of IoT AND GIS Based Information System to Manage Natural Resources”

This study focuses on IoT technology and GIS-based information system to manage all the natural resources such as rain, water, soil, etc. in order to improve the condition of farming, agriculture, and village conditions. etc.

Research Area – Attacks on IoT devices and IoT security threats

As the IoT applications are growing day by day in the same intensity IoT threats and IoT attacks are increasing. There is a need to design and develop more secure algorithms to prevent attacks on IoT devices and IoT security threats to secure data accessed from IoT devices and also to prevent attacks on IoT devices .

Research Topic “ Optimizing Privacy and Security of Internet of Things to Find Integrated Solutions”

Recently, IoT created a worldwide network of interconnected entities and this is increasing day by day to make the whole world smarter in all the ways.

According to recent researches, by 2020 there will be more than 50 billion devices will be connected to the internet, and therefore, the number of applications and services are expected to be huge.

To make the internet of things to be widely adopted by the public there should be a trust in the IoT technology security and privacy. It is very important to do more research to define how IoT things could efficiently and securely exchange information throughout the network.

Research Topic Example – “ Processing and securing IoT generated data “

Internet of things is a rapidly growing collection of internet-connected devices embedded in wide-ranging physical entities to develop smart campuses, cities, villages, etc.

IoT connected entities generate huge structured or unstructured, real-time data, and form big data.

To make use this huge data Innovative IoT data processing models are required for better handling this data.

Among the different challenges that the present IoT technology is facing, the three prime concerns are –

A requirement of an efficient framework to store IoT data,
A requirement of a new scalable real-time indexing techniques
Securing IoT generated data at all the phases

Hence, new models are needed to research, design, and develop to use the IoT data more securely, and effectively.

Research Area – IoT solutions for the pharmaceutical industry

There is a huge need for IoT technology and its solutions for the pharmaceutical industry, health care, and other medicine-related domains. There is a need to research and find solutions for all the problems of the domain to provide better facilities to the people and make their life better.

Research Topic Example – “ A novel IOT Enabled Health Diagnosis and Monitoring Model using Machine Learning”

IoT technology based health issues diagnosis and monitoring system can be developed using machine learning and AI algorithm to assist people online.

Research Topic Example – “ An Optimized Data Aggregation Model for Energy Efficiency in Wireless Sensor Applications “

The fast-growing Internet of Things ( IoT technology ) enabled communications with smart sensors operable with resource constraint nodes in various areas of interest.

A typical Wireless Sensor Network consists of a group of distributed independent sensor nodes that are capable of monitoring some specific activities of human concern consisting of network monitoring, sensing different physical attributes such as temperature, pressure, etc.

Wireless Sensor Network node does sensing, computing, and communication with a limited amount of resources which exhibits its potential limitations in terms of extending the network life by conserving energy.

The main aim of Wireless Sensor Network includes collecting data from a certain environment and forward that to the sink node in order to optimize the communication route. In studies, it is found out that energy is the main obstruction that affects the network lifetime and performance of Wireless Sensor Network in terms of data aggregation and growing performance.

The aim of the study can be to explore a better possibility of using hierarchical routing protocols for ensuring optimization of energy conservation in wireless sensor networks using AI algorithms.

Research Area – Blockchain Internet of things

Blockchain is a recent area that is growing rapidly that is completely based on the Internet of things. There is a need to do research in the domain to create trust in the blockchain applications in the public. Some related research topics are here for the help of researchers.

Research Topic Example – “ IoT Based New Secure Blockchain Model to minimize the overhead of Blockchain”

A new secure, private, and lightweight architecture can be proposed for IoT technology , based on Blockchain technology that eliminates the overhead of Blockchain while maintaining its security and privacy benefits.

Research Topic Example – “ Blockchain in Internet of Things: Challenges and Solutions “

Research Topics in Internet of Things

Some other research topics in internet of things are given below for the help of the researchers –

blockchain internet of things
internet of things solutions
internet of things healthcare
internet of things analytics
IoT and cybersecurity
IoT waste management
IoT artificial intelligence
smart farming IoT
Smart Agriculture
Smart cities
Smart Campus
Smart Villages, etc.

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1 thought on “Iot Technology – 21 Hot Research Topics in Internet of Things”

very good compilation. Suggest to identify the research areas of soil moisture sensor.

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24 Exciting IoT Project Ideas & Topics For Beginners 2024 [Latest]

In this article, you will learn the 24 Exciting IoT Project Ideas & Topics . Take a glimpse at the project ideas listed below.

Smart Agriculture System
Weather Reporting System
Home Automation System
Face Recognition Bot
Smart Garage Door
Smart Alarm Clock
Air Pollution Monitoring System
Smart Parking System
Smart Traffic Management System
Smart Cradle System
Smart Gas Leakage Detector Bot
Streetlight Monitoring System
Smart Anti-Theft System
Liquid Level Monitoring System
Night Patrol Robot
Health Monitoring System
Smart Irrigation System
Flood Detection System
Mining Worker Safety Helmet
Smart Energy Grid
Contactless Doorbell
Virtual Doctor Robot
Smart Waste Management System
Forest Fire Alarm System

Read the full article to know more in detail.

IoT Project Ideas

We live in an exciting age of technological and digital revolution. In just a decade, we’ve witnessed a radical change in the world around us. Thanks to the recent advancements in Data Science, today, we have at our disposal things like AI-powered smart assistants, autonomous cars , surgical bots, intelligent cancer detection systems, and of course, the Internet of Things (IoT). So, if you are a beginner, the best thing you can do is work on some real-time IoT project ideas.

The world currently has around 15.14 billion IoT devices. And due to advancements in technologies like 5G, this number is projected to nearly double to 29.42 billion IoT devices by 2030. This indicates the IoT ecosystem is continuously expanding and evolving.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting IoT project ideas which beginners can work on to put their knowledge to test. In this article, you will find top IoT project ideas for beginners to get hands-on experience.

You can also check out our free courses offered by upGrad under machine learning and IT technology.

Why Build IoT-Based Projects ?

But first, let’s address the more pertinent question that must be lurking in your mind: why build IoT projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. The more you experiment with different IoT projects, the more knowledge you gain.

The Internet of Things is a major sensation of the 21st century. After all, who would have thought that someday we’d have access to a technology that would allow us to connect everyday objects – like thermostats, kitchen appliances, door lock systems, baby monitors, and electrical appliances – over a centralized and integrated network and control them from anywhere in the world!

Learn Advanced Certification in Cyber Security from IIITB

Essentially, IoT describes a connected network comprising multiple physical objects that have sensors and smart software embedded in them to facilitate the exchange of data among them via the Internet. However, IoT isn’t just limited to everyday household objects – you can even connect sophisticated industrial objects and systems over an IoT network. As of now, there are over 7 billion IoT devices, and this number is expected to grow to 22 billion by 2025 !

An IoT network leverages a combination of mobile, cloud, and Big Data technologies along with data analytics and low-cost computing to enable the collection and exchange of data among physical objects connected within the network. And what’s impressive is that all of this is accomplished with minimal human intervention.

As you start working on IoT project ideas, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. Working on IoT simulation projects and IoT projects for engineering students is a fantastic way to improve efficiency and productivity. In this tutorial, you will find interesting IoT project ideas for beginners to get hands-on experience.

As the IoT technology continues to gain momentum in the modern industry, researchers and tech enthusiasts are readily investing in the development of pioneering IoT projects. In this post, we’ll talk about some of the best IoT project ideas.

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What are the benefits of IoT Projects Ideas for beginners?

The Internet of Things (IoT) has emerged as a transformative force, connecting physical devices and everyday objects to the digital world. IoT projects encompass various applications across various sectors, from healthcare and agriculture to manufacturing and transportation. These IoT project ideas bring many benefits, revolutionizing industries and unprecedentedly enhancing lives.

1. Improved Efficiency and Productivity

One of the primary advantages of IoT projects is the ability to streamline processes and optimize resource usage. Businesses can monitor and manage operations in real time by deploying IoT-enabled sensors and devices. This leads to enhanced efficiency, reduced downtime, and improved overall productivity. For instance, in manufacturing, IoT sensors can track production lines, identifying bottlenecks and potential failures, allowing for timely maintenance and minimal disruptions.

2. Enhanced Data Collection and Analysis

IoT projects generate vast amounts of data from connected devices and sensors. This data offers valuable insights into operations, customer behavior, and equipment performance. Businesses can make informed decisions, identify trends, and predict outcomes through data analysis, leading to better planning and resource allocation.

3. Cost Savings and Resource Management

Optimizing resource usage not only improves efficiency but also leads to cost savings. IoT projects help organizations monitor energy consumption, water usage, and other resources, allowing for better control and conservation. Smart grids, for instance, can adjust energy distribution based on real-time demand, reducing waste and cutting costs for both providers and consumers.

4. Remote Monitoring and Control

IoT projects enable remote monitoring and control of devices and systems, offering convenience and safety. For example, IoT-enabled medical devices can transmit patient data to healthcare providers, enabling remote monitoring and timely intervention. Similarly, farmers can remotely monitor crops and irrigation systems in agriculture, optimizing agricultural practices and minimizing manual labor.

5. Enhanced Customer Experience

IoT applications can potentially revolutionize the customer experience by providing personalized and connected services. Smart homes with IoT devices offer seamless automation and control, enhancing comfort and convenience for residents. Retailers can leverage IoT data to offer personalized recommendations and targeted marketing, increasing customer satisfaction and loyalty.

Looking to challenge yourself or expand your portfolio? Check out our curated list of computer science project ideas to inspire your next groundbreaking project.

6. Predictive Maintenance

One of the most significant advantages of IoT projects is predictive maintenance. By continuously monitoring the condition of equipment and machinery, businesses can predict when maintenance is needed before a breakdown occurs. This approach reduces downtime, extends the lifespan of assets, and minimizes maintenance costs.

7. Safety and Security

IoT projects idea s can significantly improve safety in various environments. In industrial settings, IoT sensors can monitor workplace conditions, detect potential hazards, and ensure safety regulations compliance. Smart cities can use IoT to monitor traffic and public spaces, enhancing security and emergency response capabilities.

8. Sustainable and Eco-Friendly Solutions

IoT projects contribute to sustainability efforts by promoting smart and eco-friendly practices. Smart buildings can optimize energy consumption based on occupancy levels, reducing carbon footprints. IoT-enabled waste management systems can also improve recycling efforts and reduce waste generation.

9. Innovation and Competitiveness

Organizations that embrace IoT projects ideas gain a competitive edge by offering innovative solutions and services. IoT-driven insights and data analytics open new opportunities for businesses to differentiate themselves in the market and adapt to evolving customer needs.

10. Transforming Industries and Creating Smart Cities

They are instrumental in transforming industries and creating smart cities. IoT enables remote patient monitoring and telemedicine in healthcare, revolutionizing healthcare delivery. IoT-based precision farming techniques enhance crop yields while minimizing resource usage in agriculture. For transportation, IoT applications improve logistics and public transportation efficiency, reducing congestion and carbon emissions in smart cities.

So, here are a few IoT Project ideas that beginners can work on:

Top 24 Best IoT Projects Ideas

This list of IoT project ideas for students is suited for beginners and those just starting out with IoT in general. These IoT project ideas will get you going with all the practicalities you need to succeed in your career. With a goal to keep up with advancing technologies, IoT projects for engineering students serve to be the blueprint to explore technological possibilities, a chance to produce, improve, and recreate technology capable of working on minimal human intervention.

IoT research topics can help aspirants work on their practical skills and extend their subject knowledge further through consistent practice on IoT projects for engineering students. Further, this list should get you going if you’re looking for IoT project ideas for the final year. So, without further ado, let’s jump straight into some IoT project ideas that will strengthen your base and allow you to climb up the ladder.

1. Smart Agriculture System

One of the best ideas to start experimenting you hands-on IoT projects for students is working on a smart agriculture system. As the name suggests, this IoT-based project focuses on developing a smart agricultural system that can perform and even monitor a host of farming tasks. For instance, you can schedule the system to irrigate a piece of land automatically, or you can spray fertilizers/pesticides on the crops wirelessly through your smartphone.

Not just that, this IoT-based project can also successfully monitor soil moisture through a moisture sensing system, which can work to detect dry soil . Such an advanced system can handle routine agricultural tasks, thereby allowing farmers and cultivators to focus on more manual-intensive agricultural tasks. Learners can implement a similar IoT simulation project or IoT research topics to monitor house gardens or indoor plants that often go untended.

Benefits of smart agriculture system-

Real-time update
Increased productivity
Remote management
Timely monitoring
Data-centric
Lowered operation costs
Time effective
Easy to use

Factors of smart agriculture-

Smart contracts
Supply Chain
Soil factors

Also, Check out online degree programs at upGrad.

2. Weather Reporting System

This is one of the excellent IoT project ideas for beginners. This IoT-based weather reporting system is specifically designed to facilitate the reporting of weather parameters over the Internet. This is one of the best IoT projects where the system is embedded with temperature, humidity, and rain sensors that can monitor weather conditions and provide live reports of weather statistics.

It is an always-on, automated system that sends data via a microcontroller to the web server using a WIFI connection. This data is updated live on the online server system. So, you can directly check the weather stats online without having to rely on the reports of weather forecasting agencies. The system also allows you to set threshold values and alerts for specific instances and notifies users every time the weather parameters cross the threshold value.

A few IoT projects for final year are aiming to evolve efficient usage of devices to reduce carbon footprint, which is a need of the hour. From consistent monitoring of carbon emissions to enforcing standard equipment and energy usage to operate under restricted levels, IoT’s role is evolving. Developers are leveraging smart technologies to maintain a consistent balance between nature and technology.

Benefits of Weather Reporting System-

Easy access to the weather report
Remote access
Compatible with various applications such as iOS, Android, etc.
Allows to take preventive measures
Allows the users to plan their activities
Can be carried anywhere
User friendly

Usage of Weather Reporting System-

Mountaineering
Agriculture
Flood prediction

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3. home automation system.

Home automation is perhaps the most talked about IoT projects. IoT-based home automation project aims to automate the functioning of household appliances and objects over the Internet. All the household objects that are connected over the IoT network can be controlled and operated through your smartphone.

This is not only convenient but also gives more power to the user to control and manage household appliances from any location in the world.

This IoT-based project uses a touch-based home automation system. The components of this project include a WiFi connection, an AVR family microcontroller, and inbuilt touch-sensing input pins. While the microcontroller is integrated with the WiFi modem to obtain commands from the user via the Internet, an LCD screen displays the system status. When the microcontroller receives a command, it processes the instructions to operate the load accordingly and shows the system status on an LCD screen.

However, also Blockchain IoT allows homeowners to manage their home security system remotely from their smartphone. Mentioning IoT projects can help your resume look much more interesting than others.

Benefits of Home Automation System-

Energy efficient
Safe and secure
Time efficient
Centralised managing point
Cost-effective
Constant monitoring
Customisable according to the requirements

Usage of Home Automation System-

Electricity monitoring
Lawn management
The air quality of home
Home appliances of home
Smart assistants- Speech automated
Smart Locks
Smart Watches
Smart energy meters

In-demand Machine Learning Skills

4. face recognition bot.

This IoT project involves building a smart AI bot equipped with advanced facial recognition capabilities. This is one of the best IoT Projects where the intelligent AI bot is designed to recognize the faces of different people or a single person and also their unique voice.

The system includes facial recognition features like face detection (perceives faces and attributes the same in an image), personal identification (matches an individual in your private repository containing hundreds and thousands of people), and also emotion recognition (detects a range of facial expressions including happiness, contempt, neutrality, and fear).

This combination of advanced recognition features makes for a robust security system. The system also includes a camera that lets users preview live streams through face recognition.

Benefits of Face Recognition Bot-

Identification of missing individuals
Identification of criminals/ perpetrators
Protection from identity theft
Protection from business theft
Better photo organisation
Medical treatment

Significant aspects of facial recognition-

Biometric techniques
Deep learning
Face representation
Face detection
Face recognition

5. Smart Garage Door

Yes, you can use IoT technology to control and operate your garage door! The IoT-based smart garage door eliminates the need for carrying bulky keychains. All you need is to configure and integrate your smartphone with the home IoT network, and you can effortlessly open or close your garage door with just a few clicks of a button.

This smart garage door system incorporates laser and voice commands and smart notifications for monitoring purposes, and also IFTT integration that allows you to create custom commands for Google Assistant. The smart notification option can trigger alerts in real-time to notify as and when the garage door opens or closes, which is a nifty addition. This is one of the most straightforward IoT project ideas for you to work on.

Benefits of Smart Garage Door-

Protect deliveries
Schedule option
Easy to install
Can be accessed through various devices

6. Smart Alarm Clock

This is one of the interesting IoT project ideas. This IoT-based alarm clock functions not only as an alarm clock to wake you up every morning, but it can convert into a fully-functional device capable of performing other tasks as well. The features of this smart alarm clock include:

Voice command option to execute standard commands and also to initiate a video chat.
A text-to-speech synthesizer
Automatic display brightness adjustment
Audio amplifier volume control
Alphanumeric screen for displaying text

Apart from these features, you can also add customizable features to the smart alarm clock. Interestingly enough, the alarm clock offers three ways of waking you up – by playing local mp3 files, by playing tunes from the radio station, and by playing the latest news updates as podcasts.

Benefits of Smart Alarm Clock-

Helps in timeline management
Improves sleep quality
Increases productivity
It can be connected to various devices
Allows the users to integrate with the playlist

Components of Smart Alarm Clock-

Text-to-speech synthesiser
Audio Amplifier
Button
Resistors

7. Air Pollution Monitoring System

One of the best ideas to start experimenting your hands-on IoT projects for students is working on an Air pollution monitoring system. Air pollution is a menace in all parts of the world, and monitoring air pollution levels is a challenge that we’re facing. While traditional air pollution monitoring systems fail to monitor air pollution levels successfully and the contaminants, IoT-based air pollution monitoring systems can both monitor the level of air pollution in cities and save the data on web servers for future use.

This smart air pollution monitoring system promotes a cost-efficient technique for determining air quality. The system is embedded with sensors that specially monitor five components of the Environmental Protection Agency’s Air Quality Index – ozone, carbon monoxide, sulfur dioxide, nitrous oxide, and particulate matter. Plus, the system also includes a gas sensor that can alert users in case of gas leaks or the presence of flammable gases. Apart from this, there’s also a temperature and humidity sensor.

Benefits of Air Pollution Monitoring System-

It helps to monitor the pollutants
Allows the decision-makers to take preventive and corrective measures
Helps in improving the environment
It helps to reduce the chances of health imbalance

Parameters to measure Air Pollution Monitoring System-

Radiation
Temperature
Wind direction
Barometric pressure

8. Smart Parking System

With cities and urban areas getting crowded by the minute, finding a parking space is nothing short of a challenge. It is not only time-consuming but also quite frustrating. Thanks to IoT, there’s a solution for solving the parking problem crisis. This IoT-based smart parking system is designed to avoid unnecessary traveling and harassment in the search for an appropriate parking area. This is an excellent IoT project for beginners.

So, if you are in a parking space, this system uses an IR sensor to monitor the entire area during the run time and provide you with an image for the same. This allows you to see any free spaces in the parking lot and drive straight to it without wasting any time looking for a parking space. Also, the system is tuned to open the car gate n only if there are empty slots available in a parking space.

Benefits of Smart Parking System-

Less fuel consumption
Cost efficient
Productivity
Optimised Parking
Real-time monitoring
Inclusive to disabled
Parking guided systems
Online payments
The place to recharge electric vehicle
Space for special permits

9. Smart Traffic Management System

As the population increases, the number of vehicles plying on the road also increases inevitably. Due to the ever-increasing number of both public and private cars in cities and metropolitan areas, traffic congestion has become an everyday problem. One of the needed and best IoT projects. To combat this problem, this IoT-based project creates a smart traffic management system that can effectively manage traffic on roads, and offer free pathways to emergency vehicles like ambulances and fire trucks.

Emergency vehicles can connect to this smart system and find signals and pathways where the traffic flow can be controlled dynamically. It flashes a green notification light for emergency vehicles. Also, this intelligent traffic management system can identify and monitor traffic violators even at night.

Benefits of Smart Traffic Management System-

Real-Time Management of Traffic
Safety from road accidents
Preventive measures
Traffic monitoring
Better time management
Environmental impacts

Factors of Smart Traffic Management System-

Video Traffic Detection
Edge Processing Capabilities
Pollution Analytics
Predictive Planning
Shareable data

10. Smart Cradle System

The whole concept behind creating the smart cradle is to enable parents to check up on their infants and monitor their activities from afar (remote locations).

This is one of the interesting IoT project ideas. The IoT-based smart cradle system includes a cry-detecting mechanism and live-video surveillance along with a user interface (for mobile or web). The cradle is equipped with multiple sensors that can check and monitor the humidity and temperature of the bed. On the other hand, the surveillance camera attached to the cradle will continue to send footage of the infant to the parents.

The data generated by the sensors is stored in the cloud. Additionally, the system includes a health algorithm that feeds on the sensor data to continually check the health condition of the infant and alert the parents if it senses anything unusual in the baby’s health stats.

Benefits of Smart Cradle System-

Allows the parents to monitor their child.
Instant messages on ongoings.
Noise detection of the baby
Alerts on phone

Features of a Smart Cradle System-

PIR sensor for child monitoring
Noise Detection
Swings on the cradle

11. Smart Gas Leakage Detector Bot

Gas pipes are an indispensable component of both homes and industrial companies. Any leakage in gas pipes can lead to fire accidents and also contaminate the air with pollutants, thereby causing a disastrous effect on the air and the soil. This IoT-based project is explicitly built to combat the issue of gas leakage.

And this is the perfect idea for your next IoT project!

This tiny bot includes a gas sensor that can detect any gas leaks in a building. All you have to do is insert the bot into a pipe, and it will monitor the condition of the pipe as it moves forward. This is one of the most important and best IoT projects. In case the bot detects any gas leak in the pipeline, it will transmit the location of the leakage in the pipe via an interface GPS sensor over the IoT network. The bot uses IOTgecko to receive and display any gas leakage alert and its location over the IoT network.

Benefits of Smart Gas Leakage Detector Bot-

Early detection of toxic gases
Avoid unwanted leakages
Prevention from unwanted leakages

Features of Smart Gas Leakage Detector Bot-

LCD Display

12. Streetlight Monitoring System

Streetlights are a significant source of energy consumption. Often, streetlights continue to remain on even when there’s no one in the street. With the help of this IoT-based streetlight monitoring system, we can efficiently monitor and optimize the energy consumption of streetlights.

In this IoT-based project, street lights are fitted with LDR sensors that can monitor the movement of humans or vehicles in the street. If the sensor can catch any movement in the street, it signals the microcontroller, which then turns on the street light. Similarly, if there’s movement in the street, the microcontroller switches the lights off. This way, a substantial amount of energy can be saved. This is one of the best IoT projects for safety.

Not just that, the smart light system also allows users to monitor the estimated power consumption based on the current intensity of a streetlight. It is incorporated with a load-sensing functionality that can detect any fault in the lights. If the system detects an error, it automatically flags a particular light as faulty and sends the data over to the IoT monitoring system so that it can be fixed promptly.

Benefits of Streetlight Monitoring System-

Lower maintenance
Reduce carbon emissions
Improved infrastructure

Features of Streetlight Monitoring System-

Digitally display signs
Detect weather conditions
Monitor traffic
Wifi hosting
Parking management

13. Smart Anti-Theft System

Security is one of the primary choices for homes, businesses, and corporations. Having a robust security system helps to keep unwanted intruders at bay. The IoT-based anti-theft system is the perfect solution for safeguarding homes as well as industrial enterprises.

This IoT-based security system is programmed to monitor the entire floor of the building for tracking any kind of unusual movement. When turned on, a single movement could trigger an alarm, thereby alerting the owners of the property about unwanted visitors. It works something like this – whenever you vacate a house or a building, the Piezo sensor is turned on for tracking any movement in and around the property. This is one of the best IoT projects to practice.

So if an intruder were to enter the property, the sensor would send the data to the microcontroller, which then converts it into a signal for the camera to snap a picture of the intruder. This picture is then automatically sent to the users on their smartphones. Mentioning IoT projects can help your resume look much more interesting than others.

Benefits of Smart Anti-Theft System-

Helps in the protection of belongings
Integrates alert system
Allows the users to access it from any device
Alarm system

Factors of Smart Anti-Theft System-

Data capturing
Data storage
Data analysis
Alert
Door and Window Contacts
Motion Detectors
System Interruption Errors

14. Liquid Level Monitoring System

This IoT-based project involves building a liquid-level monitoring system that can remotely monitor a particular liquid’s level and prevent it from overflowing. This project holds immense value for the industrial sector that uses large volumes of fluids in its day-to-day operations. Apart from detecting a liquid’s level, this monitoring system can also be used to track the usage of specific chemicals and to detect leaks in pipelines.

The system is fitted with ultrasonic, conductive, and float sensors. A WiFi module helps connect the system to the Internet and facilitates data transmission. Four ultrasonic sensors help transmit the data on the liquid level and alert the user on the same.

Benefits of Liquid Level Monitoring System-

Allows to access fluid level
Temperature monitoring
Updates
Automatic On/ OFF pumps
Level Control

Features of Liquid Level Monitoring System-

Remotely monitor liquid levels
Access fluid level information
Buzzer/ Trigger Alarms
Wi-Fi Modem
Display levels of liquid

15. Night Patrol Robot

This is one of the best IoT project ideas. It is a well-established fact that a majority of crimes occur in the dark, at night. This IoT project aims to develop a patrolling robot that can guard your home and property at night to prevent and reduce the possibilities of crimes.

The patrol robot is equipped with a night vision camera with the help of which it can perform a 360-degree scan of a predefined path. It will scan a particular area, and if it detects human faces and movements, it will trigger an alarm to alert the user. The camera of the patrol robot can capture an intruder’s image and send the data to the user. The robot can function in a self-sufficient manner, without requiring you to hire security guards to protect your home.

Benefits of Night Patrol Robot-

Increases safety
Helps in reducing the crime rates
Allows the government to track or trace criminals
Increases women’s safety
Strengthen surveillance efforts

Features of Night Patrol Robot-

Night vision
Motion Sensor
Display monitor
Wi-fi setup
Camera Capture
Speech recognition
Remote Access

16. Health Monitoring System

This is one of the interesting IoT project ideas to create. This IoT-powered health monitoring system is designed to allow patients to take charge of their own health actively. The system will enable users to monitor their body vitals and send the data to qualified doctors and healthcare professionals. The doctors can then provide patients with immediate solutions and guidance based on their health condition. The sensors in the application can monitor patient vitals like blood pressure, sugar level, and heartbeat. If the vital stats are higher/lower than usual, the system will immediately alert the doctor.

The idea behind creating this system is to allow patients and doctors to connect remotely for the exchange of medical data and expert supervision. You can use this application from any location in the world. It is an Arduino-based project – the communication occurs between the Arduino platform and an Android app via Bluetooth.

Benefits of Health Monitoring System

Easy access
Prompt diagnosis
Health monitoring

Features of Health Monitoring System-

Sensor Module
Data Acquisition
Data Monitoring
Data Processing
Wi-fi module

17. Smart Irrigation System

Often, farmers have to irrigate the land manually. Not only is this a time-intensive task, but it is also labor-intensive. After all, it is quite challenging for farmers to continuously monitor the moisture level of the whole field and sprinkle the pieces of land that require water. This IoT project is a smart irrigation system that can analyze the moisture level of the soil and the climatic conditions and automatically water the field as and when required.

You can use the smart irrigation system to check the moisture level, and set a predefined threshold for an optimum moisture level of soil, on reaching which the power supply will get cut off. An Arduino/328p microcontroller controls the motor that supplies water, and there’s an on/off switch with which you can start or stop the motor. The smart irrigation system will automatically stop if it starts raining.

Benefits of Smart Irrigation System-

Water conservation
Remotely control sprinklers
Increased soil quality
Sensors (Rain, Freeze, Wind, etc.)
Soil moisture sensor

Features of Smart Irrigation System-

Soil Moisture Sensor
Processing unit
Water Schedule Setup

18. Flood Detection System

Floods are a common natural disaster that occurs almost every year in our country. Floods not only destroy agricultural fields and produce, but they also cause significant damage to vast stretches of area and property. This is why early flood detection is extremely vital to prevent the loss of life and valuable assets.

This IoT-based flood detection system is built to monitor and track different natural factors (humidity, temperature, water level, etc.) to predict a flood, thereby allowing us to take the necessary measures to minimize the damage caused. This IoT project uses sensors to collect data for all the relevant natural factors. For instance, a digital temperature humidity sensor detects fluctuations in humidity and temperature. On the other hand, a float sensor continually monitors the water level.

Besides providing a system equipped with temperature sensors and float sensors to gauge the possible flood conditions, comprehending the geographical features of the space can help create shelters and collect required amenities beforehand. At the same time, flood detection systems are capable enough to gauge the time a fresh wave of the flood could take to reach a particular location. Systems like these are significant to maintaining the well-being of communities. Advanced detection systems created through IoT projects for final year can alert residents in time, allowing for early evacuation planning.

Benefits of Flood Detection System

Risk Management
Helps in saving lives
Allows the stakeholders to save infrastructure
Real-time data
Flood forecasting
Mapping using GIS

Components of Flood Detection System-

Water Sensor
Wind Sensor
Data management
Ultrasonic sensor
Power Supply
Microcontrollers

19. Mining Worker Safety Helmet

This is one of the interesting IoT project ideas. Mining workers work under extremely hazardous and dangerous conditions. Underground environments are full of risks, so there is always a fear of unpleasant accidents for miners. This mining worker safety helmet uses a microcontroller-based circuit to track the mining site’s environment and evaluate the safety of the workers.

The safety helmet is equipped with an RF-based tracking system that helps transmit the data over the IoT network. An atmega microcontroller-based RF tracker circuit receives the data that is sent by the helmet nodes. Based on this data, the system maps the current location of workers in real time as they move through the mining site.

The helmet also includes a panic (emergency) button. If you press this button, an emergency sign will show up over the IoT web interface. This will alert the management to take the necessary steps for ensuring the workers’ safety.

Benefits of Mining Worker Safety Helmet-

Identification of the worker’s last location
Alarm in case of hazardous situation
Safety
Safeguarding of lives
Infrastructure management

Features of Mining Worker Safety Helmet-

Flexible button to untie
Sensors to send alarm
Location tracker
Mini camera if required

20. Smart Energy Grid

At present, energy grids are not optimized. Often when the electricity grid of a given region fails, the entire area suffers a blackout. This usually hinders the daily activities of people. This is one of the best IoT project ideas which proposes a solution to rectify this issue by creating a smart electricity grid.

This IoT-based smart energy grid uses an ATmega family controller to monitor and control the system’s activities. It uses WiFi technology to communicate over the Internet via the IoTGecko webpage. This smart grid’s primary task is to facilitate the transmission line’s re-connection to an active grid in case a particular grid fails.

So, if an energy grid becomes faulty, the system will switch to the transmission lines of another energy grid, thus, maintaining an uninterrupted electricity supply to the specific region whose energy grid failed. The system uses two bulbs to indicate valid and invalid users. Registered personnel can log in to the IoTGecko webpage and view updates on which grid is active and faulty. This is one of the best IoT Projects to add to your resume.

The smart energy grid can also monitor energy consumption and detect incidents of electricity theft.

Benefits of Smart Energy Grid-

Resourceful
Improved reliability
Enhanced power quality
Reduce greenhouse gas emissions
Digitalisation
Decarbonisation

21. Contactless Doorbell

All the systems around have become digitalised and automated. Covid on other hand has given a new perspective to contactless interaction.

The machine uses the raspberry pi controller. The machine also uses a camera and speaker for the process.

Benefits of Contactless Doorbell-

Increased security
Prevention from thefts
Alert the owners
Voice assistance
Alarm
Camera capture
Can be connected through various devices

Features of Contactless Doorbell-

Automatic visitor recognition
LAN/ Ethernet
Vision Sensor

22. Virtual Doctor Robot

Doctors are highly required in the medical field. Their expertise saves lives every day, and they are seen as one of the most integral parts of our society. But with the rising cases and mishaps, especially in the case of emergencies and remote locations, it becomes difficult for doctors to be present everywhere.

Virtual doctors play an important role to provide medical expertise even in remote locations. They could interact with the patients and provide medical advice just like a human.

Benefits of Virtual Doctor Robot-

Inclusive to all types of locations
They could move around different locations
Assess medical reports over video call
Provide medical treatment at the earliest

23. Smart Waste Management System

The cities are smarter and are keeping up with the technology. It is time to do away with the age-old practice of waste disposal and adapt to the smart waste management system.

Municipal professionals can make great use of this technology. Whenever the dustbin is about to be filled up totally, it sends an alarm or an alert to the team that they could fetch the waste in time.

It also helps in segregating the waste into dry or wet garbage. Moreover, they could also help them to save energy and time.

Benefits of Smart Waste Management System-

Reduction of cost of collection
In time pickups
Stop overflowing of garbage
Environment friendly
CO2 Emission Reduction

Components of Smart Waste Management System-

IoT platform
Integrated to various applications
Wi-fi
Alarm/ Alert

24. Forest Fire Alarm System

The machine helps to identify the causes of fire threats and take immediate measures to prevent those. This satellite and optical system can detect large landscapes. The alerts can be sent in time in order to take necessary actions in time.

Benefits of Forest Fire Alarm System-

Safeguards environment
Helps to protect the environment, lives, infrastructure, and more.
Allows to gauge temperature, humidity, pressure, and wind
Geographical mapping of the location

25. Smart Baggage Tracker

The Smart Baggage Tracker is one of the brilliant IoT project topics aimed at making traveling more convenient and stress-free. This project involves placing a small, lightweight device in your luggage that tracks its location in real-time. Using a smartphone app, you can quickly determine the exact whereabouts of your baggage at any time.

Benefits of Smart Baggage Tracker-

Reduce the instances of lost or misplaced luggage.
Secured tagging in case of lost/stolen luggage.
Weight monitoring
Temperature control

26. Lavatory Vacant/Occupied System

The Lavatory Vacant/Occupied System is a cutting-edge IoT project that offers a real-time solution for monitoring the occupancy of public and private restrooms. By using sensors and indicators, it provides instant updates on whether the restroom is available or in use. The system aims to optimize restroom management and enhance the overall user experience.

Benefits of Lavatory Vacant/Occupied System-

Improve privacy and user comfort.
Increase efficiency in cleaning and maintenance.
Reduce waiting times and manage queues effectively.
Enhance the utilization of facilities, particularly in high-traffic areas.
Foster sanitary conditions by preventing overcrowding.

27. Smart Pet Tracker

The Smart Pet Tracker is a cutting-edge IoT-based project that aims to keep our beloved pets secure and healthy. Employing advanced tracking systems, this device can be easily attached to your pet’s collar, allowing you to monitor their whereabouts in real-time and guarantee their safety and well-being.

Benefits of the Smart Pet Tracker-

Know exactly where your pet is at any given time.
Get notified immediately if your pet leaves a pre-defined ‘safe zone’.
Understand your pet’s patterns and behaviors better.
Monitor your pet’s health and activity levels to ensure they’re staying active and healthy.
Adjust the tracking and alert parameters to suit your specific needs.
The Smart Pet Tracker is user-friendly and can be set up within minutes.

28. Plant Watering System

The IoT Plant Watering System is an interesting IoT-based mini project combining technology with nature to ensure plants are properly hydrated. This system uses sensors to check how moist the soil is and waters the plants automatically when the soil gets too dry.

It’s a huge improvement for home gardeners because it reduces the chances of plants not getting enough or getting too much water.

Benefits include-

The system only waters plants when necessary, optimizing water usage.
The automated nature of the system relieves individuals from the need to manually water plants.
By maintaining appropriate moisture levels, the system promotes healthier and more productive plants.
This automation frees up time that can otherwise be used elsewhere.
The system can be scaled to suit everything from small household gardens to large agricultural fields.

29. Home Energy Monitoring and Management

IoT has brought an exciting transformation in the Home Energy Monitoring and Management landscape. This great IoT project idea for beginners aims to provide homeowners with real-time data on their energy usage, allowing them to make informed decisions to minimize waste and reduce their energy bills.

Promotes conscious energy consumption, reducing waste, and promoting sustainability.
Homeowners can cut down their electricity bills by identifying and reducing unnecessary power usage.
The system is user-friendly, with a simple interface that doesn’t require technical expertise to operate.

30. Health and Fitness Monitoring Device

The Health and Fitness Monitoring Device is an innovative solution that stands out among IoT projects. This device employs the principles of the Internet of Things (IoT) to monitor and track fitness metrics in real-time.

The benefits –

Allows integration of various technologies, offering students a practical understanding of IoT project ideas.
Facilitates real-time monitoring of health and fitness data, demonstrating the potential and utility of IoT in healthcare.

31. Smart Pet Feeder

The IoT-based Smart Pet Feeder is an exciting and invaluable project idea for engineering students looking to delve into the world of IoT projects. This project is a perfect blend of technology and utility, designed to automatically feed pets at predetermined times.

The Smart Pet Feeder uses an IoT device to trigger the release of pet food from a dispenser into a feeding bowl. The owner can customize feeding schedules and portion sizes through a smartphone application, ensuring that pets follow a balanced diet even when the owner is not around.

Benefits of the Smart Pet Feeder-

Ensures that pets are fed at regular intervals without manual intervention.
Allows pet owners to customize feeding times and portion sizes based on their pets’ needs.
Offers owners the ability to monitor feeding schedules remotely, offering peace of mind.
Helps in maintaining a balanced diet for pets, contributing to their overall well-being.

32. Water Quality Monitoring System

The Water Quality Monitoring System uses the IoT to monitor water quality in real-time. Such IoT-based projects are instrumental in addressing significant environmental issues, pushing them beyond the periphery of just IoT project ideas to something more impactful.

This project is incredibly important as it helps maintain the health and well-being of communities by ensuring clean and safe drinking water.

Benefits of the Water Quality Monitoring System-

Real-time monitoring and instant feedback on water quality.
Automation of the water monitoring process, reducing human error.
Potential for early detection of water contaminants, preventing health hazards.

33. Safety Monitoring System for Manual Wheelchairs

The Safety Monitoring System for Manual Wheelchairs is a good example of IoT-based projects that offer life-improving solutions. This practical and vital IoT project idea uses a series of sensors and alarms to monitor the safety of wheelchair users.

Here are some key benefits-

Ensures user safety with real-time monitoring and hazard detection.
Increases user independence by enabling more confident navigation.
Provides peace of mind to caregivers with immediate alert systems.

34. Gesture-Controlled Contactless Switch for Smart Home

The Gesture-Controlled Contactless Switch for Smart Homes is a cutting-edge IoT project for engineering students. This project uses gesture-recognition technology to operate switches without physical contact, contributing significantly to the development of smart homes.

Benefits of the Gesture-Controlled Contactless Switch for Smart Homes-

Enhance user convenience with easy and intuitive controls.
Improve safety by eliminating the need for physical contact with switches.
Facilitate energy efficiency through smart control of home appliances.

35. Automatic Emotion Journal

The Automatic Emotion Journal is an excellent example that demonstrates how IoT can be integrated into our day-to-day lives, enhancing our emotional well-being.

This unique project uses IoT technology to capture an individual’s emotional state throughout the day. Using sensors and data analysis, it can record mood changes, providing a comprehensive emotional journal without the user having to manually input any information.

Benefits of the Automatic Emotion Journal-

Offers valuable insights into emotional patterns.
Encourages users to pay attention to their emotional well-being.

36. Cryptocurrency Alert System

The Cryptocurrency Alert System is an innovative IoT project idea that brings together the worlds of technology and finance. This system monitors the volatile cryptocurrency market and sends real-time alerts based on specific conditions set by the user.

Benefits of the Cryptocurrency Alert System-

Empowers users with real-time data, enabling informed decision-making.
Encourages learning about both IoT and the burgeoning field of cryptocurrency.

37. Night Patrol Robot

The Night Patrol Robot is an exceptional example of IoT projects that engineering students can undertake. Using advanced technology, this robotic device performs security patrols during nighttime hours, effectively providing a layer of safety and security wherever it’s deployed.

38. Smart Banking System

The Smart Banking System is a prime example of IoT-based projects that students can take up to understand the practical applications of IoT. In this project, IoT technology is employed to enhance banking services like money transfer, making them more efficient and customer-friendly.

Benefits of the Smart Banking System-

Enhances understanding of how IoT can be employed to improve banking services continuously.

39. Prison Break Monitoring And Alerting System

The Prison Break Monitoring and Alerting System is one of the innovative IoT projects that harness the power of IoT technology. This IoT project idea uses various sensors and alarms fused with IoT to monitor prison cells and alert the relevant authorities in case of any suspicious activities or breaches.

Key benefits-

Real-time monitoring provides comprehensive surveillance.
Automated alerts
Potentially save considerable amounts of money spent on traditional security measures.

40. Customised Gaming Controller

The Customized Gaming Controller is an exciting IoT project that empowers students to design and build their own gaming gear.

Key benefits of Customised Gaming Controller-

Provides a hands-on approach to IoT concepts.
Students learn about circuitry, programming, and IoT technology, enhancing their tech-savvy skills.
The project encourages unique ideas and designs, fostering creativity amongst students.

Future for IoT

With the ever-growing need for improvement and better accessibility, IoT estimates a dynamic future globally. Introduction to 5G and Metaverse are proof of the oncoming bright future for IoT’s flexible and improved variants. Assimilating the virtual world with reality through Metaverse is on its way, and IoT-based projects with source code are only a step away from joining hands to bring in digitally-driven physical devices. Cellular IoT’s growth is another aspect market expects to see in the coming years to adopt remote monitoring across diverse fields, including agriculture and smart cities.

Extended IoT simulation projects are gaining popularity as a way to prepare young minds for the upcoming IoT trends. But perks are not the only thing accompanying IoT in the near future.

Experts also predict heightened security threats for IoT-driven areas. A significant number of evolving IoT sectors are under the threat of botnets. In early 2021, sources reported a 35% to 51% spike in botnet attacks across individual devices and organizations through sophisticated instruments. As technological advancements improve, so do intrusion methods. Fortunately, constant improvements in security intelligence through IoT-based projects with source code are keeping such intrusions in check and aim to strengthen network and application firewalls further.

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Wrapping up .

In this article, we have covered 24 IoT project ideas. These IoT-based projects are just a few examples of how IoT technology can be used and implemented to create innovative products. With further advancements in technology, it is highly likely that more such radical and groundbreaking IoT-based projects will enter the canvas of our everyday lives.

If you wish to improve your IoT skills, you need to get your hands on these IoT project ideas . Now go ahead and put to test all the knowledge that you’ve gathered through our IoT project ideas guide to building your very own IoT Projects!

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Frequently Asked Questions (FAQs)

These projects are very basic, someone with a good knowledge of IoT can easily manage to pick and finish any of these projects.

Yes, as mentioned, these project ideas are basically for Students or Beginners. There is a high possibility that you get to work on any of these project ideas during your internship.

IoT is an integral part of our daily lives now; we all use IoT either knowingly or unknowingly. The best example of IoT in our day-to-day lives is home automation applications. Smart lights and smart blinds are becoming increasingly common today in modern smart homes. Then, our smartwatches that can track our heartbeat, count steps, etc., are also another brilliant application of IoT. Most of our smartphones come with biometric locks nowadays. These are again applications of IoT in real life. The barcode scanners we find in shopping malls are also IoT applications connected to computers and billing machines, which are all a part of the IoT network.

IoT devices or the hardware that we see are built up of several components, of which the IoT software needs to be programmed using computer languages. So IoT engineers have to write code using programming languages for IoT software to function. Several programming languages go into creating successful IoT applications, each with its own unique features and benefits. Some of the most commonly employed programming languages used are Python, Java, C++, MySQL, and C, among others. These programming languages are used to write the instructions contained in IoT software, which is embedded in the IoT hardware.

If you aspire to become an IoT developer, then first and foremost, you need to have some basic familiarity with programming languages that are needed for IoT software development. Knowing Python and JavaScript can be an added advantage. Having an understanding of the role of data is vital in IoT. Trying your hands-on practice IoT projects is a brilliant way to gain confidence. Along with technical skills, soft skills are also indispensable in becoming a successful IoT developer.

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IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

Open access
Published: 12 March 2024
Volume 1 , article number 2 , ( 2024 )

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Hossein Omrany 1 ,
Karam M. Al-Obaidi 2 ,
Mohataz Hossain 2 ,
Nayef A. M. Alduais 3 ,
Husam S. Al-Duais 4 &
Amirhosein Ghaffarianhoseini 5

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Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IoT-enabled smart cities (IESCs). As a result, 843 publications were selected for detailed examination between 2010 to 2022. The findings identified four research areas in IESCs that received the highest attention and constituted the conceptual structure of the field. These include (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection. Further, the current body of knowledge related to these areas was critically analysed. The review singled out seven major challenges associated with the implementation of IESCs that should be addressed by future studies, including energy consumption and environmental issues, data analysis, issues of privacy and security, interoperability, ethical issues, scalability and adaptability as well as the incorporation of IoT systems into future development plans of cities. Finally, the study revealed some recommendations for those interconnected challenges in implementing IESCs and effective integrations within policies to support net-zero futures.

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Smart tourism: foundations and developments

Ulrike Gretzel, Marianna Sigala, … Chulmo Koo

Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework

Elvira Ismagilova, Laurie Hughes, … Yogesh K. Dwivedi

Avoid common mistakes on your manuscript.

1 Introduction

Cities are a critical constituent of modern civilisation due to their environmental and socio-economic impacts on citizens’ lives [ 1 , 2 , 3 ]. Based on a recent report issued by the United Nations (UN), 55% of the world’s population currently lives in cities. However, the projections made by the UN shed light on the possibility of 6.5 billion people living in urban areas by 2050, equivalent to 68% of the world population [ 4 ]. This is triggered by urbanisation, a gradual shift in the residence paradigm of the human population from rural to urban areas, in tandem with the overall increase in the global population. As such, cities are expected to face daunting challenges since their resources and infrastructures are predicted to undergo an ever-increasing strain in the impending future [ 1 ]. In response, the concept of smart cities has emerged strongly over the recent decades owing to its potential for tackling these challenges through the deployment of Information and Communication Technology (ICT). Many cities around the globe have invested in becoming “smart”, aiming to improve city operations and the quality of services provided for citizens and the environment [ 2 , 5 ] (Table 1 ). In a comprehensive definition presented by Kondepudi et al. [ 6 ], smart cities are characterised as cities that utilise ICT and other advanced technologies to increase the quality of life for citizens, promote competitiveness, and improve the efficacy of urban services while assuring the perseverance of resources for present and future generations. In this regard, technologies such as the Internet of Things (IoT) play a crucial role in enabling cities to transition toward the smart city paradigm. IoT can be defined as a global infrastructure offering advanced services by interconnecting various physical and virtual “things” using interoperable ICTs [ 5 ]. The employment of IoT in the built environment enables devices to communicate with each other using different methods, such as ubiquitous and pervasive computing, sensor networks, and embedded devices [ 7 , 8 , 9 ]. The concept of integration was initially introduced as smart city testbeds that offered a platform for researchers to investigate new methods before implementing them as robust solutions. Two large projects SmartSantander and OrganiCity were introduced as smart city testbeds using IoT experimentations at an urban scale in Europe [ 10 , 11 , 12 ].

In recent years, increasing attention has been given to the deployment of IoT technologies to support smart cities to meet specific goals within Sustainable Development Goals (SDGs) such as Good Health and Wellbeing (SDG3), Industry Innovation and Infrastructure (SDG9), Sustainable Cities and Communities (SDG11) and Responsible Consumption and Production (SDG12). For instance, several applications of IoT in smart cities that are in line with SDGs including smart buildings, smart energy management, smart water management, health monitoring, environmental monitoring, intelligent traffic management, smart parking solutions, connected public transportation, smart waste management, public safety and surveillance. While emerging IoT technologies significantly contribute to smart cities aligning with SDG9, they also have an impact on the global economy. In this context, statistical information from several organisations such as ‘IoT Analytics’ on the global IoT enterprise spending dashboard indicated that the IoT enterprise market size steadily increased at a compound annual growth rate (CAGR) of 14% in 2019 to 22% in 2023 [ 18 ]. Allied Market Research [ 19 ] stated that smart cities and applications based on IoT are expected to reach $5.4 Trillion in 2030. The market was valued at $648.36 billion in 2020 and is projected to reach $6,061.00 billion by 2030 [ 19 ]. Statista indicated that approximately 50 billion IoT devices will be used around the world by 2030 [ 20 ], which has influenced the development of smart cities to increase from 118 cities in 2021 to 141 cities in 2023, as reflected in a report by IMD World Competitive Centre (WCC) [ 21 ].

Since 2010, various studies discussed the concept of the Internet of Things for smart cities. Initial database searching was conducted using the Web of Science (WoS) with the keywords “Internet of Things”, “IoT”, “and/for”, and “Smart Cities”. The search pointed out that the number of publications between 2010 and 2013 was limited. Subsequently, the number of publications in this field has increased, recording more than 100 publications in 2014 while it reached more than 1000 in 2022 in different fields. By targeting “highly cited papers”, the search returned less than 200 papers from the Web of Science Core Collection in different fields. Interestingly, the findings revealed three papers that received the highest number of citations above 1000, including studies by Zanella et al. [ 22 ], Botta et al. [ 23 ] and Lin et al. [ 24 ].

As mentioned earlier, a plethora of research has been developed investigating various aspects of IoT-enabled smart cities (IESCs). In response to the increasing number of publications in the field of IESCs, many review articles have been published aiming to solidify the flourishing knowledge in the field. The focus of these papers has mainly been limited to particular aspects of IoT in smart cities. Amongst them are studies that provided an overview of IESCs’ concept [ 3 , 7 , 9 , 25 , 26 ], studies that investigated the key IoT technologies and infrastructures for smart cities [ 8 , 27 , 28 ], and those that reviewed key features and applications of the IoT technologies to support the development of smart cities [ 2 , 29 ].

Nonetheless, the rapid advancements in the field are outstripping the possibility of addressing various aspects of IESCs in a single literature review article, and this most likely can be the main reason for the absence of a comprehensive literature review in this field. In addition, performing a holistic literature review of IESCs can be challenging due to the multi-faceted nature of this research area in which the current body of literature often spans across multiple disciplines [ 5 ]. This may further point out the diverse, yet fairly fragmented intellectual base of IESCs.

Therefore, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IESCs. To the best of the authors’ knowledge, this is the first study of its kind that investigates the IESCs literature using such a comprehensive review approach. The objectives of this study can be summarised as (i) to identify the key research topics in the field of IESCs, (ii) to critically analyse the most popular realms of IESCs research identified via bibliometric analysis, and (iii) to provide recommendations for future development of IESCs. The outcomes of this research offer a status-quo understanding of IESCs literature to the interested communities, providing them with a view of the most popular research streams as well as emerging research themes in the field. This can be particularly useful for the scientific community as the findings of this study shall furnish them with an understanding of research areas that require further investigations.

2 Methodology

The overall methodological approach of this research consists of three major stages, as illustrated in Fig. 1 . The following sections provide further details on each of these stages.

The overall research approach of this research

2.1 Database development

The choice of a database for performing scientific reviews is utterly important due to its impact on the quality of results [ 30 , 31 ]. To date, several databases have been developed to assist scholars with conducting advanced searches through various bibliometric sources such as Medline, Google Scholar, ScienceDirect, Scopus, and Web of Science (WoS). The difference between these databases resides in their coverage when it comes to research disciplines [ 31 ]. Among all, the WoS is one of the most widely utilised databases for the purpose of review analysis owing to its distinguished features in enabling researchers to gain access to more than 171 million scholarly records available via 34,000 journals, allowing users to carry out advanced searches, and offering access to over 1.9 billion cited references across various disciplines [ 32 , 33 ]. As such, this paper has selected WoS as the primary database for the retrieval of publication materials owing to its comprehensive coverage and scientific soundness.

The first step involved constructing a comprehensive search syntax consisting of terms related to the concept of IoT-enabled smart cities. To this end, a search string was formulated using keywords such as “Internet of Things” OR “IoT”. These keywords were thence combined with “Smart Cit*” OR “Cit*” OR “Urban” OR “Built Environment” via Booleans (“AND”) and deployed as the search query for retrieval of relevant data in the WoS database. It is also noteworthy to mention that the scope of the current paper is limited to the investigation of IoT applications in the context of urban environments.

The constructed search string was applied in the Web of Science Core Collection (including Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Emerging Sources Citation Index (ESCI), Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH), Conference Proceedings Citation Index-Science (CPCI-S), and Arts & Humanities Citation Index (A&HCI)) database indexed since 1900 using the “titles, abstracts, and keywords” of scholarly materials. The search returned 4223 documents on the 2nd of October 2022 including 2018 articles, 1811 proceeding papers, 203 review articles, 81 early access, 66 book chapters, 36 editorial materials, 2 books, 2 datasets, and 4 miscellaneous.

Further, this paper considered several inclusion criteria to filter out materials irrelevant to the defined objectives. First, the “ Document Types ” filter was used to retain only documents classified as “ articles ”, “ review articles ”, “ books ” and “ book chapters ” since these materials are considered “certified knowledge” because of their reputability and comprehensiveness [ 34 ]. Second, documents written in non-English languages were also excluded. Third, resources that were not related to the IESCs (e.g., law, medical science, agriculture, nursing, parasitology, and fisheries) were filtered out using the filtering functions of the WoS.

This ensured that only documents directly relevant to the concept of IESCs were retained for further analysis. Thereupon, a peer-review check was conducted to ensure that the selected articles underwent a rigorous peer-review process. This was done by cross-referencing the publication sources of the shortlisted articles with recognised databases and directories of peer-reviewed journals, such as Ulrich's Periodicals Directory or the Directory of Open Access Journals (DOAJ). This thorough verification process ensured that only articles from sources with established peer-review processes were included in the analysis. In addition, the titles, abstracts, and conclusions of shortlisted articles were scanned to ensure their alignment with the scope and objectives of this paper. In this stage, studies must have investigated the IoT implementation in the context of smart cities to be included in the final analysis. Hence, materials solely focused on IoT implementation or exploring IoT in unrelated contexts (e.g., manufacturing) were excluded. It is also noteworthy to mention that studies with incomplete information, both in terms of methodology and reporting of findings, were excluded.

As a result, the application of these filters led to a downsizing of the initial search results to 843 documents. These materials were then exported using the ‘ Tab-delimited ’ file format to be processed and analysed via VOSviewer.

2.2 Identification of key research themes

The keywords co-occurrence analysis is commonly utilised for mapping out the theoretical and empirical knowledge in research disciplines [ 30 , 31 ]. The application of this method enables researchers to demonstrate the cumulative knowledge of the target literature, unveiling the conceptual and thematic structure of the research and identifying key areas within a given research domain [ 31 ]. In this approach, the calculation is done based on the frequency of co-occurring keywords in publications and their corresponding strength of associations. The current paper constructed the network of keywords co-occurrence via VOSviewer software using all keywords of selected studies, including “author’s keywords” and “keywords plus”, e.g., those indexed by publishing journals.

VOSviewer is one of the bibliometric tools being widely employed across many research disciplines to assess the literature, such as smart cities [ 35 ], innovation in the construction industry [ 36 ], or construction waste management [ 37 ]. This software offers a user-friendly interface, allowing researchers to develop, visualise, and explore the bibliometric nexus of various entities related to a given research area [ 31 ]. One of the attributes of this software relates to the function of data cleaning via using a thesaurus file [ 38 ], providing the possibility of polishing the dataset to enhance the accuracy of analyses by merging different variations that may exist for one term. Hence, a thesaurus file has been developed to ensure the precision of analyses (e.g., IoT, internet of things, and internet of thing refer to one term, thus they have been merged and represented as IoT). Further information regarding data analysis and science mapping can be found in the software manual [ 38 ].

Further, this study applied text mining analysis via VOSviewer software. This technique is aimed at extracting information from a massive corpus of documents in texts [ 39 , 40 ]. The use of text mining enables researchers to adequately capture semantic structures and prevalent patterns of phrases that characterise a large amount of data in text format [ 39 , 40 ]. In this paper, text mining analysis was employed using the term co-occurrence algorithm to analyse the concatenation of titles and abstracts of 843 publications. The adoption of this technique, in combination with keywords co-occurrence analysis facilitated identifying research areas with the highest interest that shape the conceptual structure of IESCs.

2.3 Content analysis

Upon identifying the major thematic research themes, the selected materials were imported into an Excel Spreadsheet in order to be classified based on the results of previous steps. To this end, the titles, abstracts, and conclusions of all 843 studies were thoroughly read to allocate each study to its corresponding research theme category. Thereafter, studies in each category were critically analysed. The results of these analyses are provided in Sect. 3 .

3 Results and discussions

3.1 an overview of results.

Figure 2 a illustrates the overall publication trend per year, focusing on IoT implementation in smart cities. Starting from 2017, a significant surge in publications can be observed with 75 articles published in that year alone which is equivalent to nearly 60% of all materials published in the previous 6 years combined. This notable increase can be attributed to multiple factors, such as increasing investment in innovative technologies, rising awareness of IoT's role in addressing urban challenges, and advancements in IoT technology and infrastructure [ 35 , 41 ]. The impact of policies aimed at stimulating innovative ICT-based solutions for enhancing city governance can also be highlighted as one of the key contributors to the growing popularity of this topic, as many countries have begun supporting smart city projects by adjusting their policies over recent years [ 42 , 43 ]. The support provided by policies can potentially drive further research and innovation in the field. Furthermore, the publication trend has shown a consistent upward trajectory in recent years, with 53% of all materials (i.e., 445) published between 2020 and 2022. Considering this steady rise in publications across these periods, alongside the escalating interest in ICT-based technologies, the interest in IESCs is expected to continue growing in the foreseeable future.

a Publication trend of IoT implementation in smart cities. Note that the limited number of documents identified for 2023 is attributed to the search that was conducted in late 2022. Hence, we expect an increase in publications for 2023, indicating an upward trend. b Top 10 journals with the highest publications

The findings also revealed that a total of 127 journals collectively published 843 articles spanning the years 2010 to 2022. Figure 2 b illustrates the top ten prominent journals that have significantly contributed to the advancement of the field, accounting for nearly 51% of the published materials. Notably, IEEE Access emerged as the leading publisher, with 14% of the selected materials included in this review, followed by Sensors–MDPI (13%), IEEE Internet of Things Journal (8%), and Smart Cities–MDPI (4%). A common characteristic shared among these top journals is their multidisciplinary nature, covering a diverse array of topics related to "smart cities" and "IoT." Yet, there is a particular focus in the scope of these journals that resonates with the main research themes (i.e., data analysis, network and communication management and technologies, security and privacy management and data collection) identified in this research (See the next Section).

3.2 Identification of key research areas using bibliometric analysis

Figure 3 shows the results of this analysis performed for a minimum threshold of 25 keywords. In this figure, the frequency of co-occurring keywords in the target literature is represented by the sizes of the nodes while the strength of associations between keywords is represented by the thickness of the connecting links. The location of the nodes can also be an important point of reference, indicating that the keywords with proximity would most likely have strong relationships with each other.

Illustration of keywords’ co-occurrence analysis

Thirty keywords with the highest values of co-occurrence, along with their respective link strength are shown in Table 2 , indicating that these keywords have received high attention in the literature and are strongly associated with other keywords. In this regard, the high values of “IoT” and “smart cities” keywords were expected because they were included in the search string used for the retrieval of primary data. Nevertheless, these terms were kept in the analysis since their removal would have led to omitting other keywords linked to them. As shown in Fig. 3 , the results of the keywords co-occurrence analysis identified five major clusters. The findings suggest that keywords such as “smart buildings”, “artificial intelligence”, “big data analysis”, and “challenges” from cluster 1; “IoT”, “urban areas”, and “energy efficiency” from cluster 2; “cloud computing”, “edge computing”, “energy consumption”, “communication technologies”, and “5G networks” from cluster 3; “blockchain technology”, “trust management”, and “authentication protocols” from cluster 4, and “machine learning”, “deep learning”, “time-series analysis”, and “intrusion detection” from cluster 5 are located at the proximity of the clusters’ boundaries, implying that these domains of research are cross-cutting with solid associations with different clusters.

Table 3 presents the results of the text-mining analysis, introducing four key research themes within the domain of IESCs that have attacked the highest interest in terms of publications. These areas include (i) data analysis approaches, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection approaches. The outcomes of the text-mining analysis are, to a large extent, consistent with the results of the keywords co-occurrence analysis shown in Fig. 3 and Table 2 .

The next step for this paper is to critically discuss these four areas. Such an analysis can provide the target audiences with a state-of-the-art understanding of the recent developments in IESCs and create a proper basis for future research to contemplate further innovations and advancements in the field.

3.3 Critical analysis of research themes

3.3.1 data analysis approaches in iescs.

Data in smart cities is generated and analysed based on multiple relationships between various technological systems and their physical environments. IoT data plays a vital role in the success of any smart city [ 72 , 73 ]. Accessibility to real data offers the capacity to instantly assess the performance of any entity within a smart city by continuously collecting and analysing data from numerous sectors (e.g., health care or transportation), and actively attending to any anomalies manifested in manufacturing products [ 20 ]. Choi [ 47 ] stated that the quality of services in a smart city relies on the type of generated data gathered from different sources, levels and scales. Therefore, data collected from IoT devices should be effectively processed and transformed into actionable insights to regulate the massive flow of information in any smart city [ 74 ]. IoT data can be generated from various sources, including (i) equipment data to demonstrate the status of the IoT devices which facilitate activities of predictive maintenance, (ii) submeter data to measure utility usage (e.g., information about water, and electricity), and (iii) environmental data to evaluate and sense temperature, humidity, air quality and movements. IoT data is often generated in discrete values representing facts or numbers that convey information including useful, irrelevant or redundant information, hence these data need to be processed and analysed in order to be meaningful [ 75 ].

Data analytics is an integral part of deploying IoT technologies in smart cities, enabling the assessment of datasets to retrieve meaningful outcomes [ 76 ]. These outcomes are thence presented using statistics, patterns and forms that help to establish effective decision-making processes [ 45 , 77 ]. Moreover, data analytics provides solutions to overcome the issues attributed to unstructured data, including controlling variant types and formats of generated data [ 78 ]. Figure 4 presents the connection between IoT data to demonstrate the flow of collecting data from sensors installed in IoT devices and the process of transferring data through an IoT gateway for it to be analysed [ 79 ] and how different data analytics processes can be employed for processing data in the context of IESCs. Data of IoT in smart cities undergoes different complexities that are undertaken based on specific values as listed below:

Descriptive/time series analytics demonstrates time-based data and massive in-motion data sets to identify urgent situations, instant actions as well as associated trends and patterns [ 50 , 80 ].

Diagnostic analytics employs data mining and statistical analysis to detect latent relationships and patterns in data that are applied to uncover the causes of specific problems [ 49 ].

Predictive analytics aims to predict future events by employing various statistical and machine learning algorithms to develop models that can be utilised for predictions about future events such as weather forecasting [ 81 ].

Prescriptive analytics uses both descriptive and predictive analyses to recognise suitable actions based on a specific situation which is common with commercial IoT applications to reach better conclusions [ 76 ].

Data process in IoT and different types of data analytics based on complexity and value in IESCs

To analyse IoT data, it is vital to recognise the nature of data prior to processing. In principle, IoT data is categorised into structured and unstructured. Structured data follows a specific model to define how the data is organised or represented. Data collected via IoT sensors often comes in structured values, especially if these sensors are used for environmental assessments such as air temperature, humidity, or air quality. This type of data is simply formatted, queried, stored and processed. Unstructured data cannot fit into predefined data models such as text, speech, images, and videos, which requires conversion into a logical schema for decoding data. According to International Data Corporation, approximately 80% of business data is unstructured [ 5 ]. Therefore, data analytics techniques are gaining considerable interest in IoT, especially in smart cities due to their capacity for processing unstructured data [ 46 , 82 ].

Data generated via IoT devices can also be classified as Real Time and Non-Real Time or in motion versus at rest [ 83 , 84 ]. Most data in IoT are in motion as they move across different networks until they reach their ultimate target. However, inactive data is considered data at rest that can be stored in different digital forms such as mobile devices, spreadsheets, or databases. Mohammadi et al. [ 85 ] highlighted IoT data with five features: high volume, high velocity, high variability, value and veracity. High-volume data is distinguished by its enormous quantity, which is constantly produced via many IoT devices. High velocity refers to data type generated at high speed by many IoT devices. High variability data, which is inconsistent due to the dynamic nature of IoT environments, encompasses a variety of data formats such as unstructured, semi-structured, quasi-structured and structured data. Value data exemplifies the significance of collected data from IoT sensors after being analysed. Finally, veracity data promotes consistency, quality, and reliability in generated data.

Yang and Shami [ 45 ] and Yin et al. [ 51 ] demonstrated tasks in IoT data analytics that consist of classification, clustering, regression and anomaly detection. Furthermore, Yang and Shami [ 86 ] indicated that information from IoT is processed according to the type of data. Alternatively, algorithms in IoT data analytics are categorised into two types of learning: batch and online. L’heureux et al. [ 87 ] indicated that batch learning represents a method to analyse static IoT data in batches via Traditional Machine learning algorithms. Online learning is a technique that employs various methods to train models in IoT environments by continuously using incoming online IoT data streams [ 88 ].

Data analytics relies on advanced methods to perform analysis. Among all, machine learning (ML) has gained momentum for providing new knowledge and improving data quality via learning and processing repetitive data to attain efficiency [ 89 ]. ML employs two forms of techniques, namely supervised and unsupervised learning [ 90 ]. The machine learns rules and models of datasets drawn from clustering or frequency of particular data using pattern recognition and reinforcement techniques [ 90 , 91 ].

Another analytical technique often used for data processing in IoT-enabled platforms is deep learning. Li et al. [ 92 ] described deep learning as an ideal method for obtaining precise information from raw IoT data that existed in complex environments. Atitallah et al. [ 76 ] categorised deep learning into different modes of learning such as unsupervised, semi-supervised, supervised and reinforcement. Deng [ 93 ] classified deep learning into discriminative, generative and hybrid classes. According to Atitallah et al. [ 94 ], the most common deep learning models are Convolutional Neural Network (CNN), Deep Belief Network (DBN), Deep Reinforcement Learning (DRL), Generative Adversarial Networks (GAN), Recurrent Neural Network (RNN) and Stacked Auto Encoder (SAE). Figure 5 summarises different deep learning models, along with their applications. IoT data analytics utilises different advanced computational platforms to improve performance and accuracy. Three types of computational analysis cover computing using Cloud, Fog and Edge [ 94 ]. In fact, both Fog and Edge represent an extended development of cloud computing that offers the power for data analytics to be performed near the source of generated data.

Taxonomy of deep learning models and their main applications

3.3.1.1 Applications of IoT data analysis in IESCs

The process of retrieving information from data generated via IoT devices requires employing data mining tools. Mining data is a process of identifying patterns and correlations or discovering anomalies within large datasets to forecast outcomes. Various types of algorithms could be used for data assessment such as clustering algorithms. The selection of algorithms for clustering data is based on considering different variables such as size, data size, data type and the number of clusters. Daissaoui et al. [ 95 ] listed several algorithms to generate and manage data in smart buildings. The study covered four general types identified in previous studies including probabilistic graphical models, system identification methods, vector support machines and data mining and clustering. Several studies used probabilistic graphical models, e.g. Stoppel and Leite [ 96 ] incorporated probabilistic methods into simulation by analysing building energy models for describing occupant presence in buildings. Chen et al. [ 97 ] proposed methods using stochastic inhomogeneous Markov chains to examine occupancy in single-zone and multi-zones within a building. For vector support machines, Akbar et al. [ 98 ] investigated the occupancy state in a smart office by proposing a non-intrusive approach via Support Vector Machines to detect an occupancy state by using electricity consumption data. For data mining and clustering, D’Oca and Hong [ 99 ] presented a framework that utilised a three-step data mining through a decision tree model. The proposed model was used for forecasting occupant presence and occupancy patterns in office spaces.

Hong [ 100 ] suggested an approach to operate data analytics using Fog and Edge computing and central servers to enhance decision-making from IoT devices. Yang [ 101 ] and Rahman et al. [ 102 ] proposed a model to ingest IoT data into the nodes of Fog computing. The model covers the limitations relating to the fundamental aspects of data analytics associated with data, humans, systems and optimisation. Portelli and Anagnostopoulos [ 103 ] proposed a learning approach that enhances the prediction and precision of IoT data by using Adaptive Vector Quantization and Linear Regression to maximise communication efficiency. Lujic et al. [ 104 ] used a set of algorithms to examine IoT data that is affected by failures in sensors, systems and networks concerning smart buildings to recover incomplete datasets, reducing forecasting error and decreasing running time.

Several studies presented frameworks that combined data assessment from real-time and historical records to achieve prediction by using Decision Tree Regression, Multiple Linear Regression, Support Vector Regression and Random Forest Regression [ 105 ]. For instance, Xiaoyi et al. [ 48 ] presented a model for smart cities by analysing the management of renewable energy systems using a Multi-Objective Distributed Dispatching algorithm. The proposed method managed to decrease energy consumption while delivering high utility services in a smart city. Gomes et al. [ 50 ] also presented a framework and modules to facilitate data analytics in real-time and data stream enhancement. The analysis layer was developed to include a set of modules to extract relevant information as shown in Fig. 6 . These modules include (1) pre-processing to eliminate invalid values and reject values out of a specified range; (2) aggregation to aggregate a data set by using various functions (e.g., min, max, sum, or count); (3) statistics of a dataset in the form of median, average, standard deviation, variance, kurtosis and skewness; (4) pattern to detect behaviour patterns such as trend changing, and stability; (5) clustering to group datasets based on distance or similarities using density-based clustering, hierarchical clustering, k-means, or subspace clustering, and (6) prediction to forecast values in several steps using various approaches such as Autoregressive Integrated Moving Average, Artificial Neural Network (ANN), Kalman filter, and Forecasting Method to Model Time Series Data. Table 4 summarises the main applications of data analysis research in IESCs.

A model of data analysis in IoT processing layer

The summary showed that data processing and management in IoT devices are considered critical. The survey demonstrated various algorithmic techniques to facilitate data. It was found that studies are still exploring numerous methods based on the level of processing. The assessment revealed that many studies have been developed to create actionable platforms to enhance the data stream and data management, control data anomalies and improve data analytics. The assessment indicated that processing real data from IoT is challenging due to limitations in IoT architecture, data speed, size, accuracy, response and security. It was noted that many studies are still reviewing and testing the integration of different algorithmic techniques for new applications to obtain effective solutions. In addition, developing new IoT frameworks by integrating different algorithmic techniques has added a new level of complexity in designing smart cities. On the other hand, these findings indicate that IoT and big data analytics are still under development and require further research and investigation, especially in designing smart cities.

3.3.2 Network and communication management and technologies in IESCs

IoT network consists of various components such as sensors, software, gadgets and appliances that communicate and exchange information data with each other. Management of IoT networks allows for various functionalities such as authentication, configuration, provisioning, routing, monitoring and security to maintain a network performance in terms of low energy consumption and low latency [ 107 ]. Aboubakar et al. [ 108 ] stated that standard network management in IoT consists of specific logical elements that include agents, network devices, managed devices, and network managers which are supported by management databases and messaging protocols. Each element performs specific tasks to keep the network running. The ‘‘agent” represents the software which operates on managed devices or groups of IoT devices. The agent performs data aggregation into a combined stream to central IoT applications and is typically managed by IoT Gateway. The “network devices” include Firewalls for a security feature, Servers to manage the devices within that network, Client Applications to allow users access to complete tasks, Routers to connect to networks, Switches to allow the devices to communicate with each other and Access Points to connect the endpoint device with the network. The “managed devices” allow organisations to better monitor and control their connected IoT devices to register and deploy connected devices, device logging, organising devices into relevant groups, indexing and searching device fleets, remotely manage and update devices, custom scripting, security tunnelling for diagnosing and resolving issues and customisable dashboards for centralised device control. The management of devices plays an important role in increasing the speed of registration of IoT devices, improving device organisation, easier remote-device management and simplifying device location. The “network manager” is a device that manages a group of managed nodes. It facilitates network topology, synchronisation of devices, and management of traffic and congestion in IoT systems. The ‘‘management database” is located in the managed device and includes data about the managed device parameters. The ‘‘messaging protocols” function as a data exchange that connects information between the managed devices and the network manager. Generally, these networks need to be efficient to support specific functional operations (Fig. 7 ), such as Network Configuration Management, Security Management, Topology Management, QoS Management, Network Maintenance and Fault Management. These functionalities are typically provided as a network service in an IoT environment to ensure sufficient network performance.

Overview of entities, operations and solutions in IESCs network management

Aboubakar et al. [ 108 ] presented management solutions for IoT networks from various perspectives. These IoT network management solutions included software-defined networking (SDN)-based, machine learning-based, cloud-based, and semantic-based frameworks. The design of a new network must incorporate efficient management processes for managing a significant number of linked devices, immense amounts of data, and services with varying Quality of Service (QoS) requirements. Monitoring the network’s infrastructure makes it possible to detect any events or changes that might impact the network's resource security or usage. In this regard, several protocols have been developed to help with network management. These protocols control and monitor different network components such as gateways, devices, and terminal servers. Since the implementation of IoT low-power networks in both public and private spaces is growing at unprecedented rates, network management has emerged as a critical component of IoT low-power networks for maximising their performance and ensuring their continued availability.

3.3.2.1 Communication technologies in IESCs

In computer networks, there are several common types of network technologies such as Local Area Network (LAN), Wireless Local Area Network (WLAN), Virtual Private Network (VPN) and Wide Area Network (WAN). LAN connects devices in the same proximity, e.g., connecting devices in a small office or a building. WLAN functions in the same way as a LAN, but it uses wireless connections. VPN is a secure network which is used to communicate with encrypted data. WAN offers the possibility to connect devices across a large distance. In smart cities, heterogeneous objects are connected by IoT communication technologies to deliver intelligent services. In IoT, several wireless network types can facilitate IoT sensor deployment and IoT applications in industries such as Radio-frequency identification (RFID)/Bluetooth Low Energy (BLE)/Near Field Communication (NFC), Wi-Fi/(LoRa and Wi-Fi)LoFI, MESH Protocols, NarrowBand-Internet of Things (NB-IoT), Ultra-Wide Bandwidth (UWB), Low-Power Wide-Area Networks (LPWAN) (LoRa, Sigfox), ZigBee and Cellular (3G/4G/5G/6G) [ 109 ]. The next paragraph highlights some of these technologies.

Bluetooth is a short-wavelength radio-based communication standard for low-power data transfer between electronic devices over short distances [ 110 ]. Bluetooth 4.1, lately issued by the Bluetooth special interest group, offers Bluetooth Low Energy besides offering high-speed and IP connectivity to promote IoT applications [ 111 ]. Machine to Machine (M2M) is the next generation of the Internet revolution connecting many devices via the Internet. The M2M was initially implemented using RFID as the first technology (RFID tag and reader), and now it is aimed at automating the communications between machines and devices via provided networks without human intervention. Ultra-wideband communication (UWB) is a communication technology that was developed to strengthen communications in areas with a low-range coverage while consuming a low amount of energy and providing a high bandwidth. Recently, the number of applications using this technology to connect sensors has increased [ 112 ]. Wi-Fi is a wireless networking technology that allows devices within a 100-m radius to exchange data using radio waves [ 57 ]. In certain ad hoc configurations, Wi-Fi enables smart systems to communicate and exchange data without requiring a router. For low-power wireless networks with the goals of reliable and scalable communications, the IEEE802.15.4 standard details both the medium access control and the physical layer [ 113 ]. LTE-A (LTE Advanced) represents an enhanced variant of Long-Term Evolution (LTE) that offers benefits such as increased bandwidth (up to 100 MHz), spatial multiplexing on both the downlink and the uplink, wider coverage, greater throughput, and lower latency [ 114 ].

There are a variety of technologies developed to improve the effectiveness of network management. One of these technologies is Radio Access Network as a Service (RANaaS) which has been developed to facilitate adaptable management of network resources [ 115 ]. IPv6-based networks have management protocols in place, such as Long-Range Wide Area Network (LoWPAN), Network Management Protocol (LNMP) and Simple Network Management Protocol (SNMP) [ 116 ]. In addition, self-organising wireless networks can benefit from Time Synchronised Mesh Protocol (TSMP), a communication protocol that empowers the sensors/devices to be synchronised with one another. Furthermore, Software-Defined Networking (SDN) is a key component in the development of 5G systems, which intends to reduce complexity in network management and design while also allowing for the network to be managed and reconfigured in a way that is automated, flexible and dynamic [ 117 , 118 , 119 ]. Furthermore, the paradigms provide features for managing heterogeneous devices in a wide range of deployments and use cases [ 120 ]. Despite the potential of these paradigms for introducing effective methods in managing networks, several issues remain [ 121 ].

Several studies such as Cedillo-Elias et al. [ 56 ] proposed cloud platforms utilising SDN and OpenStack to safeguard citizens' data contained by the government and make extra efficient usage of existing IT infrastructures for smart city facilities. In another study, Purnama et al. [ 54 ] analysed the viability of deploying IoT connectivity for AMI (Advanced Meter Infrastructure) services in Surabaya, Indonesia. To experimentally evaluate and compare multi-hop and single-hop LoRa topologies in terms of energy efficiency and range extension. Aslam et al. [ 122 ] presented a case study that measured Packet Reception Ratio (PRR) for different source-to-destination distances, transmission powers and spreading factors (SFs). The findings demonstrated that the configuration of a LoRa network with multiple hops can save a significant amount of energy and improve coverage. Nashiruddin and Nugraha [ 53 ] investigated LoRaWAN's network planning for Smart Metering Infrastructure (SMI) by counting the number of gateways required to support the communication of SMI devices. Fraile et al. [ 55 ] also compared IEEE 802.15.4 and LoRa for indoor deployments in IoT-enabled school buildings. Using information gathered from 8 networks and 49 devices spread across 6 educational facilities, the study compared the efficiency and cost of various IoT solutions. The outcomes demonstrated that LoRa can achieve lower costs and higher data rates than IEEE 802.15.4 while maintaining similar or better link quality. Table 5 presents the recent protocols to enhance the management of IoT communication.

To sum up, this section discusses various network and communication management and technologies used in IESCs to connect heterogeneous objects and provide intelligent services while consuming low power. Intriguingly, surveying the literature on IoT network management shows no detailed or comprehensive overview available of existing resource-constrained network solutions. Future research could focus on developing more energy-efficient and scalable communication technologies to handle the increasing number of smart devices in smart cities. Further research should aim to investigate several solutions to improve data security, reliability, energy efficiency, network scalability, interoperability, and data privacy. These solutions include encryption, authentication, access control, low-power communication protocols, energy-efficient hardware, cloud-based architectures, edge computing technologies, blockchain, digital certificates, context-aware, adaptive IoT communication systems, and hierarchical clustering. Numerous studies have investigated the use of IoT connectivity for various applications, such as smart city services and advanced meter infrastructure, and have compared the efficiency and cost of different IoT solutions, including LoRa and IEEE 802.15.4. Therefore, it is recommended that IoT network management should incorporate efficient management processes for handling a large number of devices, vast amounts of data, and diverse services with varying QoS requirements. Furthermore, network managers should continuously evaluate and adopt new technologies and protocols to enhance network performance and security. Developing an effective solution for managing IoT networks can be challenging due to the inherent constraints of IoT networks [ 123 ], such as the diversity of IoT devices, the fluidity of network topologies, the scarcity of available resources, and the unpredictability of radio links. More research is needed to design effective solutions to manage IoT with low-power networks that can handle heterogeneity while ensuring security and privacy and allowing for scalable resource utilisation.

3.3.3 Security and privacy management in IESCs

Smart cities are equipped with advanced technological infrastructures to actively monitor and control physical objects and furnish citizens with real-time information about transport, smart parking, traffic, or public safety [ 124 ]. Nevertheless, there are various issues related to security and privacy at different levels of smart cities’ architecture. This is largely due to the nature of devices deployed in these cities which are often resource-constrained, thus making cities vulnerable to security attacks [ 64 ]. An example of such attacks is the major electricity breakdown that occurred in Ukraine due to malicious attacks on smart grids [ 124 ]. Therefore, this section discusses six major areas of security and privacy issues in IESCs.

Intrusion Detection System (IDS) . As the number of things connected to systems increases, the centralised or cloud-based IDS will suffer from excessive latency and network overhead. Subsequently, it makes it difficult to respond to attacks and detect rogue users. For example, a fog-oriented IDS was developed with the capacity to use an Online Sequential Extreme Learning Machine, which is decentralised in computing infrastructure and has no fixed location between the data source and the cloud [ 125 ].

Automobiles and Transportation . Attempts have been previously undertaken to develop a “Smart Accident Precognition System (SAPS)” aiming to minimise the risks of accidents and protect the users’ safety on the road. To further improve the system, SAPS was coupled with Google Assistant to make use of various embedded devices for monitoring several aspects of vehicles and passengers such as speed, distance, and safety measures. The real-time data are stored within the cloud and accessed by both the vehicle and the Google Assistant, allowing smarter decision-making and acting based on the previous data recorded [ 67 ]. However, the implementation of such a scheme may pose threats to unauthorised access to users’ personal details as well as gaining control over vehicles and transportation systems.

e-Healthcare System . This is an improvement to the traditional healthcare systems by connecting to IoT systems and the Internet. However, e-Healthcare systems are subject to the same issues as any IoT-enabled systems such as compromising privacy and personal data due to hacking by malicious users from across the Internet [ 126 ]. For example, in a smart city environment, the healthcare system is often a collaboration between the public and private sectors. Although the public sector can be the central decision-maker, distributing treatment and medicine to the private sector may be more effective and efficient. However, this means the personal information will be overseen by different parties and thus have a larger chance of being hacked or exposed [ 127 ].

Communication methods of IoT . Since the communication of IESCs is reliant on online networking systems, it is susceptible to different types of cyberattacks [ 124 ]. In communication, connectivity is a critical component in delivering an IoT solution. Many protocols can be employed within the same IoT solution to maintain the stability of IoT communications to be suited for varied contexts with different barriers and limits. Some of these difficulties relate to the physical elements, i.e., the distance between devices, the specific IoT task performed such as the need for real-time applications requiring higher and more stable connectivity capabilities, and the device’s computing resources such as weaker or power-saving devices may need communication protocols that require less power. Every of these communication protocols has their strengths and weaknesses and some of them are more prone to attacks [ 127 ].

Code and program level of IESCs . Data aggregation has different levels, such as the need to achieve trust and quality in shared information models to enable reuse, secure data interchange and transfer, and protection mechanisms for vulnerable devices [ 59 ]. As each of the IoT solutions is deployed with different objectives and means, on the coding level, they should be customised and carefully integrated to maximise their performance and data protection. As an example, most websites and Internet-connected apps incorporate at least one type of Web API to assist with a specific function in the grand scheme of things, such as Samsung SmartThings, which provides classes via an API to process HTTPS calls within the IoT solution asynchronously [ 127 ].

Ethics and morality of humans . Caution is required with those working with the system and those holding the collected data, as there are research papers which showed that immoralities and irresponsible acts of corporations and authorities are important causes of compromised security and privacy. A study conducted in a smart city in India showed that although not all the usable subjects think that it is significant, in general, the trust and intention of the authorities and holders of the gathered data are affecting their trust in IoT as a whole [ 128 ].

3.3.3.1 Managing and combating the issues

To combat the issues highlighted above, active attempts must be made to improve security and privacy by safeguarding communications of devices and networks. To this end, specific measures can be implemented.

To combat the ineffectiveness of a centralised IDS, hybrid semi-distributed and distributed intrusion detection systems can be promoted. In these systems, the associated databases demonstrate effective feature extractions and selections, combined with parallel machine-learning models and fog-edge coordinated analytics that can mitigate the risks of centralised IDS [ 62 ].

To combat possible security and privacy threats associated with transportation and automobile systems, including both unauthorized use of users’ information and attacks triggered by malware, spam, black holes, wormholes, and outages, it is necessary to improve Vehicular Sensor Networks in IoT-enabled transportation and automobile system in terms of robustness, reliability and security [ 65 ].

To improve security and privacy issues, an active defensive line is needed consisting of ML and blockchain technologies in which the former enables predicting and detecting vulnerabilities while the latter secures the networks by creating tamper-resistant records of shared transactions [ 61 ].

Securing different data layers is also a promising way to prevent security and privacy issues via using payload-based symmetric encryption for the data security layer, utilising computation of secured data for the data computational layer, and only extracting visions from the last data layer, i.e., the decision-making layer [ 60 ].

The physical layer is often ignored to be protected. The passive observer’s data is usually unreachable to the network's authentic source and destination nodes, deploying countermeasures such as an efficient “Sequential Convex Estimation Optimization” algorithm that can be very useful against them [ 129 ].

Table 6 presents a comprehensive summary of recent research in the field of security and privacy in smart environments. One of the notable observations is the diverse range of applications covered in the papers, including smart cities, e-healthcare systems, industrial IoT, smart homes, and more. This highlights the importance of context-specific solutions that can address the unique security and privacy challenges in different environments. The proposed solutions in the papers utilise various technologies, such as machine learning, blockchain, elliptic curve cryptography, and homomorphic encryption. While these technologies offer high levels of security and privacy, they also require significant computational resources, which can be a challenge in resource-constrained smart environments.

The limitations and trade-offs of the proposed solutions in the papers need to be carefully considered as well. For instance, Arunkumar et al. [ 130 ] presented a lightweight security key generating system that can detect and prevent security threats in smart cities using machine learning and elliptic curve cryptography. However, the hardware used to deploy the system is relatively immobile and consumes a lot of energy. The blockchain-based authentication method offers improved communication metrics and privacy-preserving features but comes at the cost of weaker identity management and slower automation speed [ 131 ]. Similarly, the routing model presented by Haseeb et al. [ 132 ] can efficiently establish direct trust between nodes but does not handle malicious attacks and flooding of messages well. The seamless authentication IoT framework for e-Healthcare systems presented by Deebak et al. [ 133 ] is more efficient and has a better packet delivery ratio and network lifetime, but consumes more resources compared to related works. Additionally, some papers address specific challenges in smart environments, such as predictive computation [ 134 ], security in fog computing [ 135 ], security in smart grid networks and access control in edge computing [ 136 ]. These papers highlight the importance of addressing specific security and privacy challenges in emerging technologies and infrastructures in smart environments.

In conclusion, Table 6 presents a summary of recent research in the field of security and privacy in smart environments. The diversity of applications, technologies, and challenges discussed in the papers highlights the need for context-specific solutions that can balance the trade-offs between security, privacy, efficiency, and scalability in resource-constrained smart environments.

3.3.4 Data collection approach in IESCs

The approach for data collection in the context of smart cities may vary depending on the smart data applications from macro to micro scales and the sectors from which the data is collected [ 138 , 139 , 140 ]. Different studies showed research in areas such as Energy Conservation, Urban Environment, Health & Wellbeing, Biodiversity, Surveillance/Security & Safety, Transportation & Mobility, Infrastructure & Communication, Tourism and Waste Management. Below, the review identifies a number of domains in which IoT can be integrated with advanced technologies for the purpose of data collection in smart cities.

Electrical Energy. Several studies collected and forecasted data from the microgrid using smart meters such as the Heuristic Intelligent Neural Decision Support System [ 141 , 142 , 143 ]. In a study, Abu-Rayash and Dincer [ 144 ] developed a new integrated solar energy system capable of meeting the energy demands of a small city of 5000 homes. The proposed system can also collect real-time solar energy and thermal energy data using Photovoltaic panels and thermal energy storage tanks, respectively.

Urban Environmental Pollution. Yu et al. [ 145 ] showed the effective collection of air pollution data (Particulate matter—PM2.5) from 242 cities in China based on an online IoT monitoring system. In another research, P.M2.5 sensors were deployed on street levels and via drones at Xidian University and Peking University using 4G (fourth-generation) internet network base platforms and the stations complied with narrowband IoT communications [ 146 ]. Furthermore, electrochemical (SNS-MQ135) and MQ9 gas sensors were employed via Bluetooth, ZigBee and Z-Wave networks to measure air quality at the polluted city Bucharest, Romania in order to detect carbon dioxide (CO2) level, ammonium (NH4), ethanol (C2H6O), toluene (C7H8), carbon monoxide (CO) and methane (CH4) [ 147 ]. Segura-Garcia et al. [ 148 ] also validated an IoT prototype for monitoring real-time Psycho-Acoustic Soundscape utilising 5G (fifth-generation) LTE-M1 sound monitoring devices. Further, Dembski et al. [ 149 ] developed an urban digital twin and computational simulations such as space syntax, SUMO—Simulation of Urban Mobility and simulated wind flow by installing a test sensor and mobile App – Reallabor Tracker for citizen’s feedback.

Biodiversity. In a study, Chen and Han [ 150 ] used a range of turbidity, oxidation–reduction, or pH potential (ORP), conductivity and dissolved oxygen (DO) sensors to measure water quality in Bristol, UK. In this project, a multi-parameter water quality sonde (Aqua Troll 600) was used to assess the water quality while an IP Network-based Camera was utilised to collate video images of the water surface. Studies also used low-cost data collection methods through renewable wireless sensor networks for measuring environmental parameters such as temperature, pressure, humidity, smoke, and noise sensors, smart IoT-enabled bins, pyroelectric infrared, UV/Lux sensors and rain sensors [ 151 ]. For instance, Gallacher et al. [ 152 ] deployed Echo Boxes that consisted of low‐cost sensor networks combined with artificial intelligence techniques to monitor bats’ activities in a large urban park. Podder et al. [ 153 ] also proposed an IoT-based Smart AgroTech system in the context of urban farming with the capacity to monitor humidity, temperature, and soil moisture, and decide when the irrigation system should operate.

Infrastructure, Information, Security and Safety. For example, real-time e-learning data collected via phones and gadgets can be applied in virtual classrooms [ 154 ]. Kinawy et al. [ 155 ] developed an online portal where the use of citizen profiles and knowledge items, such as tagging and comments on project websites were utilised. Similarly, business, parking, and tour information, i.e., users’ real-time scores and interaction, were shared with the people utilising e-government platforms and Mobile Apps at Petaling Jaya City Council and Putrajaya Corporation in Malaysia [ 156 ]. Recently, drones were also used for monitoring disasters, search and rescue tasks, surveillance and taking photographs with aerial views [ 157 ]. Shah et al. [ 106 ] developed a comprehensive disaster management model by which data can be collected from various sources such as Twitter datasets, weather sensors, surveillance sensors (e.g., CCTV cameras), pedestrian count, location, time, screen sensors for tracking vehicular traffic, and pollution and smoke sensors [ 106 ].

Transportation. Recently, Sato et al. [ 66 ] proposed a prototype that included a crowd road surface sensing system on a sensing vehicle on a winter road and a sensor server system using an Axis Mechanical sensor, GPS, temperature, humidity, quasi-electric sensor and infrared laser. This IoT-based server is connected to a communication server. Chakroun et al. [ 158 ] proposed a system to reduce delays during emergency traffic by focusing on the density of vehicles vs delays in alert dissemination. The project incorporated the Location-based Alert Messages Dissemination Scheme and the sensors that are provided in the vehicle cluster system look at the speed and flow of traffic using cameras. Ajay et al. [ 159 ] proposed smart management systems using IoT sensors such as CCTV, fuzzy logic, pedestrian sensors, and ultrasonic sensors. Li et al. [ 160 ] analysed data from 8,900 personal cars for three months in the city of Changsha, China from an IoT-based vehicle monitoring system. Toutouh and Alba [ 161 ] developed a data collection method using broadcasted beacon frequency (Hz) to neighbouring vehicles to maintain traffic safety, congestion control and efficiency.

Waste Management. Solid waste management with IoT was deployed using bin-level monitoring at home and public spaces using ultrasonic sensors and LoRaWAN networks [ 162 ]. Similarly, IoT-based sensors attached to bins included automatic open/close smart bin lids, filling level sensors, smart bin waste segregation, garbage collector alerts and ultra-sonic human detectors which helped arrange waste management systems and minimised delays in collecting bins when they are full [ 163 ]. Cerchecci et al. [ 164 ] proposed a prototype with an ultrasound distance sensor and a microcontroller for determining the level of bins and the count of changes in the bins.

Nonetheless, the process of data collection via IoT in the context of smart cities is being challenged by a number of factors (Table 7 ).

Based on the systematic review of surveyed articles, Table 7 illustrates data collection methods with a special focus on data collected from IoT devices, relevant human factors and other related systems. From the literature reviews in Table 7 , it was observed that there is an extensive range of data collection methods applied in IESCs. Despite the collaborations between IoT sensors and other types of data sources, there is still a lack of consistency when the human factor is involved due to the unpredictability of data and variables. The bulkiness of IoT devices may be reduced in the future with the advancement of technology. However, privacy, security and reliability of human data are the major apprehensions in the successful implementation of smart cities. Finally, Fig. 8 illustrates the main data collection approaches applicable in different sectors of smart cities.

IoT-based data collection approaches in various application sectors of smart cities

4 Challenges and recommendations

This review showed that IoT-based technologies have a critical role in realising smart cities. In the following sections, the key challenges and associated solutions are expanded by focusing on interconnectivity and integrating those challenges for more sustainable smart cities. From the critical analysis of the research themes, i.e. data analysis approaches, network and communication management and technologies, security and privacy management and data collection approach in IESCs, seven key challenges have been identified and these are elaborated below:

Energy consumption and environmental issues: IoT offers the possibility of collecting, analysing, and delivering massive amounts of data via advanced communication technologies. The big data received from IoT devices requires storage capacity, cloud computing, and wide bandwidth for data transmission [ 174 ]. However, the entire process of analysing and transmitting big data can be very energy-consuming. This is in addition to the amount of energy that sensing devices consume to continually remain operational. Therefore, there is a concern about the energy efficiency of IoT implementations in smart cities to meet specific SDGs such as Sustainable Cities and Communities (SDG11) and Responsible Consumption and Production (SDG12). This is compounded by issues associated with e-waste generation due to the booming trend in employing IoT in cities.

To address this challenge, the idea of green IoT has recently gained momentum. Green IoT is described as adopting energy-efficient measures to reduce energy consumption and GHG emissions caused by IoT systems in the built environment [ 175 , 176 ]. In this regard, studies suggested the use of green ICT technologies for green IoT, including the use of green RFID, green wireless sensor network, green cloud computing technology, green M2M, green data centre technology, green communication and networking, and green internet [ 174 , 175 , 176 ]. The use of drones to help with data transmission is another promising technology for improving energy efficiency in IoT systems. In principle, devices operating within IoT schemes consume high transmission power to transmit data over long distances. Drones can assist with this process by moving close to IoT devices, gathering data, analysing and processing the collected data, and sending it to those devices which are out of the coverage area [ 176 ].

Data analysis: It is evident that IoT analytics provides many benefits, however, these analytics share difficulties during the implementation, specifically in the form of technical challenges. Tibco [ 177 ] highlighted two types of challenges including features related to ascertaining time series with data structures and balancing speed and storage. This issue can affect diagnostic and predictive efforts. Alternatively, balancing speed with storage and scaling the process up, especially in the case of time-sensitive data is considered a challenge when historical data is necessary to make comparisons. Studies also underlined issues related to detecting anomalies with IoT data. These anomalies are considered serious complications in the upstream chain and the data ingestion process. As such, there is a need to manage a large amount of data by delivering timely and accurate feedback. Bellini et al. [ 178 ] suggested that a promising solution for anomaly detection is to examine IoT data through structure, movement, producer, stack faults, noise, outlier, conditional, typical trends, period, rate, and scalability. On the other hand, there are challenges associated with remote data processing that create issues in centralised computing systems due to high response time and connection loss [ 50 ]. For instance, Atitallah et al. [ 94 ] showed that false data injection can mislead the analytics processes. Such issues can subsequently lead to incorrect outcomes, guidance, and forecasts [ 179 ].

Privacy and security: The issues related to privacy and security are among the most daunting challenges for implementing IoT in smart cities. These issues are manifested in different layers of IoT architecture such as device level or communication level. At the device level of privacy, there is an issue of “inadequate authorisation and authentication”, “insecure software”, “firmware”, “web interface” and poor “transport layer encryption” [ 180 ]. To address this, security considerations should be improvised at different layers of IoT architecture to preclude security threats and attacks [ 181 ]. A number of protocols have been developed and implemented on different communication layers to ensure increasing security and privacy in IoT-based systems such as Secure Socket Layer (SSL) and Datagram Transport Layer Security (DTLS) [ 180 ]. However, the IoT communication layer is still open to threats imposed by malicious actions mainly due to employing wireless technologies within IoT systems. Therefore, there is an urgency to deploy methods for the detection of malicious activities and activating self-healing measures when threats are identified. Another issue associated with implementing IoT in the context of privacy is that users would feel secure once utilising services provided by IoT systems. Hence, recommendations point out the necessity of maintaining authorisation and authentication via secure networks which enable establishing safe communications between trusted parties [ 180 ].

Interoperability: Interoperability is widely regarded as a challenge in implementing IoT in smart cities. This term refers to the possibility for IoT devices and systems to readily communicate and exchange information with each other without using any particular middleware applications. The root of this issue stems from the heterogeneous nature of IoT systems in which various types of devices and technologies are often deployed for data collection purposes. The issue of interoperability may arise at four levels, including technical, semantic, syntactic, and organizational, as stated by van der Veer and Wiles [ 182 ]. Koo and Kim [ 183 ] presented five interoperability types, i.e., network, semantic, middleware, syntactic, and security, for which common security should be ensured for each other. Studies proposed solutions to facilitate interoperability in IoT systems such as “adapters/gateways-based solutions”, “virtual networks/ overlay-based solutions”, or “networking technologies” [ 184 ]. A study also proposed a hybrid solution, such as ‘Double Obfuscation Approach’, which is comprehensive and reliable in implementing IESCs [ 185 ]. Despite these efforts, interoperability still remains a challenge for the implementation of IoT in smart cities.

Ethical issues: Ethics in IoT applications in smart cities include issues related to social behaviour standards, encapsulating a wide range of challenges such as intellectual property rights, data accessibility, data sharing, and the use of data or information [ 186 ]. In a study, Allhoff and Henschke [ 187 ] discussed five fundamental issues associated with IoT applications in the context of smart cities, including informed consent, privacy, information security, physical safety, and trust. The study emphasised that these issues intersect in many ways, hence their impacts should be observed in connection with each other. While the ethical requirements vary between countries, Chang [ 188 ] proposed an ethical framework that can be used in six smart cities and explained how the framework can be used even in those countries with lower ethical requirements.

Scalability, adaptability, and reliability: IoT systems provide a large number of services and applications by connecting numerous devices. However, it becomes challenging to design a system that can constantly adapt to the changing needs of users. Scalability refers to the characteristics of a system for accepting the addition of new services, devices, and equipment to its configuration without suffering any interruption or degradation in performance [ 180 , 189 ]. The scalability characteristic can be vital in helping a system to be competitive, efficient, and capable of delivering sufficient quality of service. In this regard, one of the main challenges for the future development of IoT systems is to become scalable so that such systems can support the integration of a large number of devices with each other having different memory, processing, storage power, and bandwidth [ 180 ]. While Artificial Intelligence of Things (AIoT) Initiatives are being adopted to implement smart cities, they are usually ineffective due to a lack of preparedness, resources and capabilities [ 190 ]. This study proposed three emerging themes, i.e. proof-of-value, treating and managing data as a key asset and comprehensive commitments, that should be taken into consideration in cities to reduce the challenges of scalability issues.

IoT and future development of cities: There is a necessity to develop a vision at the government and policy level for incorporating IoT infrastructures when planning for the future development of cities. IoT is fundamental to the realisation of fully functioning smart cities, thus it is important that future urban development plans would encompass smart features (e.g., smart grids, connected homes, telematics, etc.) as a measure to facilitate IoT implementations. Javed et al. [ 73 ] emphasised examining smart cities as an integrated network of interconnected systems rather than isolated entities. Since IoT technologies are emerging and many concepts such as climate resilience, net-zero city, climate-intelligent cities, and digital circular economy are continuously influencing the future development of cities at policy levels, the lack of IoT integration within these concepts could have a social and economic impact, including unintended consequences following the adoption of efficiency-improving measures [ 191 , 192 ].

The literature review of the above seven challenges also indicated the interconnectivity between them which have direct and/or indirect influence on each other. The recommendations that emerged from the literature reviews are also potential solutions for more than one challenge and have a rebound effect. Figure 9 illustrates the interconnected nature of the challenges and solutions in implementing IESC. Figure 9 reveals that recommended solutions may help to mitigate more than one interconnected challenge, however, most challenges also depend on how IESCs would fit and integrate with future technologies.

Interconnectivity between challenges and potential solutions in implementing IESCs

By summarising the seven key challenges attained using hybrid systematic analysis, several aspects are identified in line with SDGs in terms of Industry Innovation and Infrastructure (SDG9), Sustainable Cities and Communities (SDG11) and Responsible Consumption and Production (SDG12) that can be listed into specific points. (1) The complexity of IESCs reveals limitations with performance across management, implementation and operation at different levels and scales, offering new opportunities to explore this field in-depth. (2) The projected deployment of 50 billion IoT devices around the world by 2030 [ 20 ] and the continuous growth of smart cities [ 21 ] would impose a devastating environmental impact in terms of e-waste. Hence, efficient implementations regarding circular economy are needed within the boundary of IESCs developments. (3) Data transmission and data storage from IoT devices require considerable electrical power that intensifies energy loads in smart cities. Several solutions were proposed to overcome this issue, but further research is needed to limit this demand to achieve net zero by 2050. (4) Handling and analysing IoT data in smart cities encounter challenges in terms of speed, connection, process, anomalies and forecast that pose a need to improve the reliability of computing in selected IESCs infrastructure to ensure delivering smooth and efficient actions. (5) Data privacy and security in wireless environments are still developing. Protections from any external threats or breaches require further research and exploration to maintain secure authorization and authentication in IESCs. (6) Unclarity or immaturity of standards and guides towards intellectual property rights, data accessibility, data sharing, and the use of data or information are the key challenges in terms of ethical use. Further investigations are needed to enhance transparency in developing frameworks. (7) Exchanging data in different devices and environments without using proper intermediate layers could lead to issues in terms of interoperability as data moves through different levels. Thus, further studies should examine these aspects in detail. (8) Integrating IoT solutions in smart cities features issues in terms of scalability and adaptability to meet users’ needs that are always changing based on the context. Thus, advanced studies to explore the reliability of modifications are required. (9) Several IESCs concepts have been proposed to achieve functionality, however, environmental aspects in terms of future climate resilience were found to be neglected. As a result, accelerating research in this direction is a future demand. (10) Exploring and developing IESCs requires comprehensible vision and policies as stated in several guides to achieve optimum implementation, such as Smart Readiness Indicator and IoT Readiness Level Index, however, further explorations should be extended in providing proper regulations for different industries and experts in this field, especially in the built environment [ 5 ].

5 Conclusions

This study has adopted a hybrid literature review technique to identify and critically analyse hot research topics in the field of IESCs. To this end, 843 documents were retrieved from the WoS database and analysed with reflection on the defined objectives. The results of keywords’ co-occurrence analysis in combination with text-mining analysis identified four main areas of IESCs research, including (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection.

From bibliometric analysis and text-mining analysis, the publication trend has shown a consistent upward trajectory in recent years, with 53% of all materials (i.e., 445) published between 2020 and 2022. Considering this steady rise in publications across these periods, alongside the escalating interest in ICT-based technologies, the interest in IESCs is expected to continue growing in the foreseeable future. The findings also revealed that a total of 127 journals collectively published 843 articles spanning the years 2010 to 2022. The top ten prominent journals that have significantly contributed to the advancement of the field, accounting for nearly 51% of the published materials.

The study examined actionable platforms to enhance the data stream, data management, control data anomaly and improve data analytics. The content analysis of these research areas showed that most data collected via IoT devices is unstructured. Thus, data analytics techniques are required for deployment to process unstructured data for IESCs. Data in IESCs undergoes different complexities that are undertaken based on specific values. The assessment revealed several limitations with IoT data speed, size, accuracy, response and security. It was found that studies are still investigating numerous methods based on the level of processing. The review found that many studies are still exploring and experimenting with different algorithmic techniques for new applications to obtain effective solutions. Finally, achieving integration between IoT and big data analytics shows a promising future but requires further research and investigation, especially in designing smart cities.

The study found that IoT network management shows no available detailed or comprehensive overview of existing resource-constrained network solutions. The study found that IoT network management needs to incorporate efficient management processes for handling a large number of devices, vast amounts of data, and diverse services with varying requirements. In addition, there are some limitations in terms of developing an effective solution for managing IoT networks that can be challenging due to the inherent constraints of IoT networks. Finally, further studies are required to provide efficient solutions to manage IESCs with low-power networks to handle heterogeneity while ensuring security and privacy and allowing for scalable resource utilisation.

The assessment of security and privacy management in IESCs revealed issues at different levels of smart cities’ architecture due to the nature of devices being deployed in cities, which are often resource-constrained, thus making cities vulnerable to security attacks. The study identified new technologies that offer high levels of security and privacy, however, they require significant computational resources, which can be a challenge in IESCs with resource-constrained. The study found that areas of predictive computation, security in fog computing, security in smart grid networks and access control in edge computing have specific challenges in addressing particular security and privacy in emerging technologies and infrastructures. Finally, the survey found the need for context-specific solutions to balance the trade-offs between security, privacy, efficiency, and scalability in resource-constrained smart environments in IESCs.

Data collection in IESCs demonstrated challenges that may vary depending on the smart data applications, data scale and the sector subject to data collection. The study identified 9 areas for data collection, including Energy Conservation, Urban Environment, Health & Wellbeing, Biodiversity, Surveillance/Security & Safety, Transportation & Mobility, Infrastructure & Communication, Tourism and Waste Management. Also, it was observed that there is an extensive range of data collection methods applied in IESCs. Despite the collaborations between IoT sensors and other types of data sources, there is still a lack of consistency when the human factor is involved due to the unpredictability of data and variables.

The review singled out seven main challenges associated with the implementation of IoT in smart cities for future research. These include energy consumption and environmental issues, data analysis, privacy and security, interoperability, ethical issues, scalability, adaptability and reliability and incorporation of IoT systems into future development plans of cities. The review also revealed some recommendations for those interconnected challenges in implementing IESCs, where most of those issues rely on future/emerging technology and effective integrations within policies to support environmental agendas such as circular economy, climate resilience and net-zero futures.

Data availability

No data was used for the research described in the article.

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Omrany, H., Al-Obaidi, K.M., Hossain, M. et al. IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction. Discov Cities 1 , 2 (2024). https://doi.org/10.1007/s44327-024-00002-w

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Since the term first coined in 1999 by Kevin Ashton, the Internet of Things (IoT) has gained significant momentum as a technology to connect physical objects to the Internet and to facilitate machine-to-human and machine-to-machine communications. Over the past two decades, IoT has been an active area of research and development endeavors by many technical and commercial communities. Yet, IoT ...
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Topic Information. Dear Colleagues, The Internet of Things (IoT) is one of the most prominent tech trends to have emerged in recent years. It refers to the fact that while the word "internet" initially referred to the wide-scale networking of computers, today, devices of every size and shape - from cars to kitchen appliances to industrial machinery - are connected and sharing information ...
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Over the past two decades, IoT has been an active area of research and development endeavors by many technical and commercial communities. Yet, IoT technology is still not mature and many issues need to be addressed. In this paper, we identify 10 key research topics and discuss the research problems and opportunities within these topics.",
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activities, IoT techniques still remain immature and many technical hurdles need to be overcome. The aim of this paper is to identify several important IoT research topics and areas, ranging from energy harvesting, data analytics, search, recommendation, security, privacy and trust in IoT, as well as the topics that arise in the adoption of
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100+ IoT Research Topics for Final Year Projects

IoT Protocols and Architectures

Embedded Systems for IoT

Sensors, Actuators and Connectivity

IoT Wireless Sensor Networks

IoT Data Management

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IoT System Design and Testing

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IoT Applications

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Q1. How do I select a good final year IoT research topic?

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