Introduction
In today’s urban landscape, cities are leveraging technology to become smarter. In the era of rapid urbanization, managing utilities efficiently is pivotal for the development of smart cities. Integrating Internet of Things (IoT) and Artificial Intelligence (AI) offers transformative solutions to streamline utility management, optimize resources, and improve urban living. Smart city applications integrating artificial intelligence (AI) and the Internet of Things (IoT) enhance various urban aspects. One primary focus is enhancing citizen engagement and transparency.
Rapid urbanization strains resources, impacting energy, water, transportation, the environment, and healthcare budgets. However, cities globally can combat these challenges through IoT and AI solutions. These technologies not only alleviate but solve crucial urban problems.
With over half the world’s population in cities expected to rise to two-thirds by 2050, smart solutions are essential to address the complexities of urban living.
The integration of Internet of Things (IoT) sensors and artificial intelligence (AI) is reshaping how we live, work and interact within urban environments. As cities around the globe embrace technology to address modern challenges, the fusion of these two transformative fields is unlocking opportunities for efficiency, safety and sustainability. By harnessing the power of connected devices and intelligent algorithms, smart cities are becoming the blueprint for a future where technology seamlessly integrates into everyday life.
The IoT market is expected to surpass $3 trillion in 2025 and is projected to grow exponentially in the coming years.
This growth is driven not only by technological advancements but also by the increasing need for resilient infrastructure, effective resource management and proactive community safety.
According to a 2018 Gartner report, it’s expected that AI will become a critical feature of 30% of smart city applications by 2020, up from just 5% a few years prior.
Intentional AI implementation is rapidly becoming recognized as the not-so-secret ingredient helping major energy providers accomplish their lowest carbon footprint to date with sustainability and attractive profit margins.
Integrating AI with the IoT in smart cities is a growing trend changing how cities are managed and developed. This integration involves using AI algorithms to analyze the vast data IoT sensors generate in smart cities. This integration also facilitates the advancement of development processes, offering novel opportunities and features, all while significantly reducing human interaction. IoT sensors are devices embedded in different parts of a city’s infrastructure, such as buildings, bridges, roads, and public spaces. These sensors collect and transmit data on various parameters, such as temperature, humidity, traffic flow, energy consumption, and air quality. In general, the use of IoT devices generates vast quantities of data. These data subsequently enhance city management and elevate residents’ living standards.
Recently, many critical applications have a data-centric model, such as electricity or water resource management, verification, and payment of intelligent card services. Using smart mobility can decrease the emission of CO2 and improve traffic efficiency. The applications of smart city and their usefulness completely relies upon the compilation of information, data connectivity, and their pervasiveness. Smart cities benefit individuals and their natural environment, as shown below.
In this article, we explore the convergence of AI and IoT in smart city scenarios and the synergy of IoT and AI in revolutionizing utility management across energy, water, waste, and transportation systems.
What is a Smart City?
Smart cities strategically use technology to enhance daily operations and residents’ lives, covering areas such as smart waste management, transportation, and healthcare. In essence, a smart city is an interconnected, technology-enabled environment designed to improve living standards.
Key components of smart cities include the following:
- IoT devices, including traffic and environmental sensors, form the foundation of smart city systems. They collect vital data for informed environmental, traffic, and utility management decision-making.
- A smart city IoT data analytics component processes information from devices. It utilizes edge computing for low latency and cost efficiency or cloud computing for scalability and flexibility in data analysis.
- Communication networks enable data exchange among devices, analytics systems, and infrastructure in smart city IoT systems. Options like Wi-Fi, cellular networks (3G, 4G, 5G), and LPWAN offer varied advantages, addressing specific needs from short-range to real-time, low-power applications like environmental monitoring.
- Public services and infrastructure encompassing roads, buildings, and public areas. These aspects benefit from data analytics and IoT, improving transportation, education, and healthcare through AI advancements.
The concept of a smart city varies based on development levels, but fundamentally, it employs Information and Communication Technologies (ICT) to enhance lives, boost efficiency, share information, and improve services.
A smart city aims to create an efficient society using city infrastructures through AI, optimizing functions, pushing economic growth, and improving the quality of life.
Smart city technologies rely on AI, cloud-based services, and IoT devices like sensors. This data enhances infrastructure, public services, and utilities, fostering a smarter urban environment.
Smart Meters and Water Usage
Smart meters allow IoT sensors to track water usage. This, in turn, allows city administrators to predict energy consumption patterns with much greater accuracy and ultimately save money.
With the help of AI, people can monitor water usage in real-time and assess things like potential water damage with greater accuracy. In addition, algorithms analyze water samples so that cities can find contaminants and eventually make their water safer. To accomplish this, utility companies are using a variety of technological developments to better understand and predict water usage.
Thanks to AI algorithms that combine with accompanying technologies, IoT sensors across whole infrastructure systems provide additional information about water usage. One technology that utility companies use to gather information is Geographical Information Systems (GIS). This provides the companies with precise geographical information about where processes take place rather than simply what the system reads overall.
In applying their algorithms to IoT sensors equipped with GIS sensors, AI-driven systems can detect leaks and identify other types of problems, such as contaminants and other water-quality issues, faster and more accurately. As a result, companies can adjust system requirements with far greater precision, streamline their processes, and save money. When necessary, they can shut off water supplies where they are not needed, thereby reducing energy consumption.
Water systems are an example of cities utilizing AI-powered network optimization throughout urban areas. These types of networks will soon become more common, and more cities will adopt them for various functions.
AI models empower water management systems to analyze water consumption patterns, detect leaks, and optimize distribution networks. Similarly, telecommunications companies leverage AI models to enhance communication network efficiency and performance. Through AI-powered network optimization, they dynamically allocate resources and improve signal quality, ensuring seamless connectivity.
Environmental Monitoring
With climate change and urban pollution emerging as significant global challenges, the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) offers advanced environmental monitoring solutions. AIoT systems combine IoT sensors’ real-time data collection capabilities with AI’s analytical power to monitor and address environmental issues effectively.
Smart cities leverage these technologies to monitor air quality, water pollution, and noise levels, providing actionable insights for policymakers and residents. For instance, smart waste management systems use AI to optimize collection routes, reducing fuel consumption and emissions. Similarly, AI-powered streetlights adjust their brightness based on real-time environmental conditions, conserving energy that would otherwise be wasted.Emerging technologies include:
- Multi-parameter sensors that track greenhouse gases and particulate matter.
- AI models that forecast pollution trends.
- Systems that optimize urban greenery to improve air quality.
Waste Management
Another area where cities are combining IoT with AI is waste management. Cities are installing waste receptacles equipped with IoT sensors to optimize waste collection. Sensors can detect when bins overflow, and cities can target problematic areas by making receptacles more efficient. These “smart” trash receptacles in urban areas will reach 2.4 million by 2025.
Automated waste-sorting systems are redefining the efficiency and accuracy of material recovery facilities (MRFs), particularly with the integration of AI and machine learning. In these facilities, waste is sorted, processed, and prepared for recycling, but traditional systems have often required extensive manual labor and involved high operational costs due to inefficiencies and limitations in sorting technology.
AI-powered robotics, computer vision, and IoT-enabled sensors have transformed waste processing, reducing reliance on manual labor and enhancing sorting accuracy. Modern Material Recovery Facilities (MRFs) use automated systems that leverage AI to identify, classify, and separate materials like metals, plastics, and glass with remarkable precision. Machine learning (ML) enables robotic arms to adapt to new materials, ensuring efficient sorting of emerging waste types.
AI-driven computer vision enhances speed and reliability by analyzing the shape, color, and texture of items in real-time, enabling precise categorization and reducing contaminants in recycling streams. IoT sensors continuously monitor waste composition, providing real-time data to dynamically adjust sorting priorities and optimize processing. Predictive maintenance supported by IoT minimizes equipment downtime, ensuring uninterrupted operations.
These technologies also help forecast waste trends, enabling facilities to adapt to shifts in waste composition and improve recovery rates. Data sharing with governments and recycling agencies supports targeted policies and public awareness campaigns. Automation improves safety by reducing human exposure to hazardous waste while increasing productivity.
By producing high-quality recycled materials, AI-powered systems enhance economic viability and promote a circular economy, conserving resources and reducing emissions. Future advancements in ML, robotics, and sensor technology promise even greater precision and sustainability in waste management.
Traffic Control Mechanisms
In enabling more efficient, streamlined traffic control mechanisms, transportation management systems have been applying sensors on roads to identify and manage the incidence of accidents, traffic congestion, and other types of obstacles, as well as related factors affecting traffic. These sensors help to communicate information related to speed, direction, and the time it takes to travel specific distances. And in addition to using sensors on roads, they can also be used on any moving object. By collecting all the information provided by moving and still objects in a given area, officials can make faster, more informed decisions regarding traffic control.
Thanks to the combination of “computer vision” that sensors allow city administrators to have – enabling people to see fine details of traffic scenes – along with AI algorithms that can detect minor situational changes instantaneously (including everything from walking patterns to light changes), officials can now take many subtle measures with regard to traffic control. These include reducing risks to pedestrians as well as traffic, as well as larger-scale functions such as enabling traffic lights to function more effectively.
Intelligent Security Cameras
Computer vision to enhance security cameras is a pretty straightforward application of AI. Having a video where a crime is recorded is great, but unless someone knows something occurred, there is simply no reason to review the footage until that footage is eventually lost.
A surveillance system supported by a robust AI looking for patterns of criminal behavior is the equivalent to a team of detectives that never sleep analyzing all video in real time.
AI-enhanced security cameras can be used in schools and businesses to cut the response time whenever action needs to be taken. For example, if the person that needs to be detected is a “white male wearing a blue shirt,” the AI can differentiate between people entering an area who corresponds to the description and send an alert in real time.
His photos and video can also be uploaded directly to local first responders, who can find segments of videos which may contain him based on keywords (white, male, and blue shirt, in the above example) instead of having to search through hours of footage.
Air Quality Control
Air quality is also a significant concern in urban areas. By installing sensors in different parts of a city, city officials can obtain air quality readings in many other distinct places as they wish. People can obtain real-time information about air quality using AI-driven technology and take appropriate actions accordingly.
By detecting severe problems, such as gas leaks, officials can quickly solve emerging problems before too much damage is done.
Additional benefits of using AI in smart sensors include making better long-term predictions about air quality. In learning precisely, the patterns in different neighborhoods, at different times of day, etc., administrators can adjust policies and relay necessary information to other municipal agencies accordingly. If air quality levels become dangerously low, administrators can warn the public promptly and thereby help people avoid particularly bad quality air.
Smart Healthcare in Smart Cities
Smart healthcare leverages IoT, AI, machine learning, wearables, and telemedicine to improve access to services, enhance patient outcomes, and boost healthcare efficiency. Telemedicine and wearable devices enable continuous health monitoring, offering personalized, real-time care regardless of location. AI-powered chatbots and virtual assistants assist with triaging, medical advice, and appointment scheduling, reducing the workload on healthcare professionals.
The Internet of Medical Things (IoMT) and point-of-care (POC) devices enhance cardiac care, cancer diagnosis, and diabetes management. AI integration improves the accuracy, functionality, and decision-making of IoMT devices while supporting disease diagnosis, treatment, and prediction.
Smart health monitoring systems, enabled by Industry 5.0 and 5G, provide cost-effective, reliable remote health services. AI and big data analytics improve resource planning, m-health systems, and personalized healthcare. AI accelerates drug discovery by identifying candidates, predicting efficacy, and optimizing development. Natural Language Processing (NLP) transforms healthcare delivery by improving hospital management, clinical practices, and drug development.
Improving Traffic Control
Another place where AI can help with the reduction of GHG emissions is transportation systems.
Other than making autonomous cars possible in the near future, transportation can be made more sustainable even today. (Read more: Are These Autonomous Vehicles Ready for Our World?)
Researchers at the Department of Energy’s Lawrence Berkeley National Laboratory are working on a computational tool based on deep reinforcement learning models called CIRCLES to smooth traffic in all congested cities.
CIRCLES — which stands for “Congestion Impact Reduction via CAV-in-the-loop Lagrangian Energy Smoothing” — simulates large amounts of vehicles driving in custom traffic scenarios.
This connected and autonomous vehicle (CAV)-enabled system can reduce energy consumption and improve traffic flow by reducing stop-and-go phantom traffic jams on freeways.
Used to make cities much more livable, advanced traffic control can also help reduce air pollution. Deep learning algorithms are used to combine satellite images with traffic information obtained from smartphones and environmental IoT sensors to improve air quality predictions.
AI-based automatic license plate reader (ALPR) software such as the one deployed by Rekor can also be used to provide recognize vehicles or for real-time detection of crimes and violations.
Smart Irrigation Systems
AI and IoT are revolutionizing agriculture through smart irrigation systems, optimizing water usage and improving crop yield. IoT-enabled sensors monitor soil moisture, weather conditions, and temperature in real time, providing precise data about the farm environment. This data is analyzed by AI algorithms to determine the exact water needs of crops, minimizing wastage and over-irrigation.
By integrating weather forecasts and soil analytics, AI predicts optimal irrigation schedules, ensuring crops receive the right amount of water at the right time. IoT devices like automated sprinklers or drip systems can then execute these schedules seamlessly, reducing manual intervention.
Smart irrigation systems also offer remote control through mobile apps, allowing farmers to monitor and adjust irrigation settings from anywhere. AI can detect anomalies, such as leaks or equipment malfunctions, and provide alerts for timely maintenance.
These systems promote sustainable agriculture by conserving water resources, reducing energy consumption, and lowering costs. By leveraging AI and IoT, farmers can enhance productivity, adapt to climate changes, and ensure long-term agricultural sustainability.
Enhanced Emergency Response Systems
In 2025, AI-powered IoT systems are expected to play a pivotal role in emergency response. From detecting natural disasters to responding to public safety incidents, these systems will ensure faster and more efficient interventions.
Key innovations include:
• IoT-connected fire detectors and flood sensors.
• AI-driven analytics for real-time traffic optimization during emergencies.
• Drones and robotic systems for search and rescue operations.
Smarter Homes for Safer Communities
IoT sensors in homes are becoming more intelligent, with AI enhancing their ability to create safer and more convenient living environments. Smart locks, motion detectors and energy-efficient systems will integrate seamlessly with broader community safety networks. This technology will be especially helpful for aging individuals as IoT sensors can make it possible for them to live independently at home while alerting family and loved ones if anything becomes amiss.
Notable developments include:
• AI-assisted home security systems capable of identifying unusual behavior.
• Energy management solutions using IoT to optimize power usage.
• Connected appliances that learn and adapt to user preferences.
Future Trends in Smart Cities
Cities play a crucial role in creating sustainability as they represent the largest consumers of resources and the primary emitters of carbon. As the world’s population increasingly urbanizes, utilities have a significant responsibility in meeting decarbonization targets. This involves collaborating with urban planners and decision-makers, integrating with various sectors, engaging with city residents, and leveraging technology to its fullest potential. Smart cities rely on IoT devices for managing urban aspects like traffic, energy, waste, and public safety. These devices generate massive data transmitted to central systems for analysis, necessitating robust IoT networks. 5G-based IoT networks, with speeds up to 20 Gbps and low latency (as low as 1 ms), provide reliable, real-time communication, supporting applications like autonomous vehicles, drone transport, and remote healthcare.
5G networks enable seamless communication between IoT devices, supporting real-time, demand-based events through intelligent algorithms. Features like network slicing, multi-access edge computing (MEC), and enhanced security ensure consistent connectivity and data protection in congested urban areas. AI integration with 5G enhances urban management by analyzing IoT data for precise decision-making, reducing costs, errors, and inefficiencies.
Smart cities will leverage 5G for applications like smart traffic, homes, agriculture, grids, and resource management. AI and 5G combined can optimize energy use by predicting renewable energy availability and instantly adjusting supply. In transportation, AI optimizes traffic flow and routes, reducing congestion, emissions, and energy consumption.
By integrating AI, 5G, and IoT, future smart cities aim to enhance efficiency, scalability, and sustainability, transforming urban life and paving the way for a data-driven, eco-friendly future.
Figure. Illustration of the combination of IoT, 5G and AI, which would be the integral components
of a futuristic smart city.
Conclusion
In the ever-evolving urban landscape, the synergy of Artificial Intelligence (AI) and the Internet of Things (IoT) is reshaping cities worldwide. Through cultivating active citizen participation and ensuring transparency, smart cities effectively confront the challenges of rapid urbanization.
Real solutions emerge, from robust encryption combating privacy concerns to advanced traffic management mitigating network congestion. Efficient data handling through edge computing and standardized protocols exemplifies progress.
However, global variances persist, with disparities in infrastructure, ethical dilemmas, and regulatory conflicts. Nevertheless, forward-thinking cities such as Singapore and Amsterdam provide guidance amid these challenges.
With the surge in urban populations, adopting these innovations isn’t merely a choice – it’s the blueprint for sustainable, interconnected urban futures.