Convergence of Artificial Intelligence in IoT Network for the Smart City—Waste Management System

The rapid growth of cities is putting immense pressure on resources and infrastructure. In response, the concept of smart cities has emerged, aiming to leverage technology to improve the efficiency and sustainability of urban environments. Artificial intelligence (AI) and the Internet of Things (IoT) are two key technologies playing a critical role in the development of smart cities. This paper explores the integration of AI and IoT for urban sustainability. It discusses the various applications of AI and IoT in different aspects of urban life, such as transportation, energy, waste management, and public safety. The paper also highlights the challenges and opportunities associated with integrating AI and IoT in smart cities.

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Taking solving urban problems and serving urban development as the starting point, smart city comprehensively uses information technology means such as big data, network communication, artificial intelligence and satellite remote sensing to solve population, resource and environmental problems in combination with scientific management methods. It is a new intelligent city model proposed to promote urban health, safety and sustainable development. Through the background of smart city, focusing on the core issues such as what is a smart city, what kind of smart city to build and how to build a smart city, and based on the analysis and investigation of the development status of the smart city in Huzhou, this paper analyzes and expounds the problems existing in the construction of the smart city in Huzhou, This paper puts forward the countermeasures and suggestions to promote the development of the new smart city in Huzhou City. It gives information on further development within the proper implementation of the smart city concept. Firstly, this concept needs a wide range of specialists in the field of management, informatics, geography, architecture, regional economy, who should work in close cooperation synergistically. Secondly, it is substantial to applicate new technologies, for instance, they can be cloud computing, big data, GIS, the Internet of Things and artificial intelligence, etc. The current state of construction of a smart city is emphasized in unsatisfactory condition, hence, the development of the smart city in Huzhou needs all of the above factors. In the light of all evidence, to further develop a smart city, a set of measures is recommended, including clarifying development goals and providing scientific assistance to construction, providing smart planning, strengthening leadership and introduction of new technologies, establishing communications to explain the concept of building a smart city. Such a city will have convenient public services, improved city management, a proper living environment, well-developed intellectual infrastructure and long-term network security.

Today, the state-of-the-art of technologies are playing a notable role in designing, constructing, and maintaining smart cities. Hence, the application of modern technologies in smart cities addresses various problems, for instance safety, security, transport, health care, waste management, environmental issues, etc. Although the applications of big data, artificial intelligence (AI), and the Internet of Things (IoT) in smart cities accelerate many prospects, a widespread study that covers the applications and their subsequent challenges in smart cities is lacking in the existing literature. Reviewing the existing literature exposes that a great deal of attention is paid to the applications of big data, AI and IoT in smart city development, yet a broad discussion on these technologies’ characteristics, usage, applications, and challenges in smart cities are still required. The main contribution of this chapter is that it offers a systematic evaluation and synthesis, of the critical applications and challenges of modern big data, AI, and IoT in smart city development. This chapter proposes an applied conceptually designed prototype that integrates the key domains, technologies, platforms, and architecture for the expansion of the smart city.

The increasing global waste generation has increased the challenges associated to pollution, waste management, and recycling, necessitating innovative strategies like the application of artificial intelligence (AI). This research highlights the diverse applications of AI in waste-to-energy processes, smart bins, waste-sorting robots, waste generation modeling, monitoring and tracking systems, plastic pyrolysis, distinguishing fossil and modern materials, logistics optimization, illegal dumping prevention, resource recovery, smart city development, process efficiency, cost reduction, and public health improvement. AI integration in waste logistics can reduce transportation distances by up to 36.8%, costs by 13.35%, and time by 28.22%. Furthermore, AI-enabled systems achieve waste identification and sorting accuracies between 72.8% and 99.95%. Combining AI with chemical analysis enhances waste pyrolysis, carbon emission estimation, and energy conversion. This paper also explores how AI can improve efficiency and reduce costs in waste management systems, making them more effective for smart city ecosystems.

The growing population and mass relocation of citizens from urban and semi-urban areas to Smart Cities have resulted in exponential growth in Smart Cities and thereby certain challenges. One of the major challenges Smart Cities are facing is to control, manage and process waste generation on daily basis. Waste collection and processing at a wider scale is not an easy job. Growing population and resource constraints in waste management activities are the primary reasons which have made waste management a tough job. To deal with this challenging process, Smart Cities uses Smart Waste Management System. This paper has provided an overview of a typical Smart Waste Management system and a review of selected research papers on Smart Waste Management. We tried to identify areas of improvement with the existing Smart Waste Management Solutions and proposed an innovative solution called "iSmartWMS" for carrying out waste management specifically for Smart Cities. The paper has discussed in detail the architecture and building blocks of the proposed Smart Waste Management System, along with the details of software tools, sensors, and technologies proposed in iSmartWMS. The Paper has finally discussed results with respect to prototype implementation of iSmartWMS and also future plans to further improve the iSmartWMS smart waste management system.

The growing problems in the cities, caused by the overpopulation, increased economic growth, the loss of natural resources, are creating the necessity of decision-making referring to the different economic, social and environmental issues. In the twenty-first century, the world faces such challenges, which has made people and city governances more concerned about ensuring the high quality of life and sustainable development of the urban systems. There are different definitions for smart cities, but the universal one is that a smart city is the city which satisfies the needs of people and meets different challenges. The ‘smart cities’ is a new concept and it measures not how smart the city is, but the city’s efforts to make itself smart—sustainable development, sound economic growth, and when the urban system adapts itself to the users’ needs. The smart city concept incorporates good urban planning, use of digital technologies, networks of technologies, networks of people who work together well, a change in the way of thinking, the transformation of the city governments for successful smart cities and applying the participatory approach. That’s why in order for to make a city smart, it is necessary to apply different innovative approaches and solutions and to use information and communication technologies. Smart cities utilize IoT (Internet of Things) sensors, actors and technologies to connect different components across the city from the air to the streets, to the underground. Cities have similar problems—traffic-stricken streets, poor public transport, air pollution, etc., but some of them are successfully managed to solve these problems. In this report, we will discuss good practices and experiences from different cities, which use technologies and communications to become smart, such as sensors for free parking spots, smart traffic lights, the use of energy-saving solutions, online platforms for creating solutions, bicycle lanes, smart waste management. The city will be considered as a living laboratory for experiments, some of them successful, other necessary and yet other still looking too abstract at the moment. The reason will be sought how some cities can use new technologies to make them look more efficient, competitive, functional and livable for citizens. This report is presented in 4 sections, including the foregoing introduction. Section “What is the concept for Smart Cities?” introduces the concepts for smart city innovations. Section “Good practices for Smart Cities” exposes the cases of development of smart cities in Barcelona and others. Section 3 discovers how some cities can elaborate and develop innovative approaches for a smart city, but others cannot. This section will reveal incentives and tools for the development of smart cities. Section 4 is for conclusion and aims to show how policy makers, NGO and citizens to identify cities which can be developed as smart cities and different solutions and practices how cities can become smarter through appropriate tools.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing urban development by enabling smarter, more efficient, and sustainable cities. AI-powered IoT systems integrate real-time data processing, predictive analytics, and automation to enhance various aspects of urban infrastructure, leading to improved resource utilization, reduced operational costs, and enhanced citizen services. This paper explores the transformative role of AI-enabled IoT in key smart city applications, including intelligent transportation, energy management, waste management, and public safety. In intelligent transportation, AI-driven IoT solutions optimize traffic flow, reduce congestion, and improve public transit efficiency through predictive modeling, autonomous vehicle integration, and adaptive traffic management systems. Energy management benefits from AI-enabled IoT networks that analyze consumption patterns, enhance grid stability, and facilitate the seamless integration of renewable energy sources, contributing to sustainable urban power distribution. In waste management, AI-powered IoT applications enable smart waste collection, optimize route planning, and enhance recycling processes through automated monitoring and real-time analytics. Furthermore, AI-driven public safety applications, including intelligent surveillance, emergency response systems, and crime prediction models, enhance urban security and disaster preparedness. This study presents various AI-driven IoT frameworks, highlighting their benefits, technological capabilities, and implementation challenges. Issues such as data privacy, cybersecurity, infrastructure scalability, and interoperability are analyzed in detail to assess the feasibility and long-term sustainability of these smart city solutions. Through an in-depth review of existing case studies and emerging trends, this research provides insights into how AI-enabled IoT can shape the future of urban development. Tables, figures, and bar charts illustrate key technological advancements, deployment strategies, and the overall impact of AI-driven IoT systems on modern urban infrastructure. By addressing existing challenges and leveraging the full potential of AI and IoT integration, cities can enhance operational efficiency, improve quality of life, and foster sustainable development in an increasingly digital world.

Due to the rapid increase in urban population, the today’s life of every citizen undergoes drastic changes. For the betterment of human life, Government of India had decided and announced the development of smart cities i.e the cities to be developed with all modern facilities in which people can use the internet for their daily activities. Smart city development would heavily depend on the Internet of Things (IOT) which combines three important aspects of Internet such as people-people interaction, people-objects interaction, and objects-objects interaction. Artificial Intelligence (AI) is another technology that provides the city equipped with efficient systems for security, safety, parking, etc. The applications of AI enable IoT in smart cities that are discussed in this chapter such as Smart Home, Smart Healthcare, Smart Water, Smart Grid and Energy, Smart Transport, and Real Estate investment. Section 12.1 gives an introduction about the chapter that includes an introduction to Smart City, IoT and integration of AI and IoT. Section 12.2 discusses the potential use cases of AI and IoT in smart cities. Smart Home use case that includes smart management of equipment and human activity recognition is discussed. It also discusses Smart Healthcare which includes the fitness-tracking system, glucose-level monitoring system, body-temperature monitoring system, stress detection system, and oxygen-saturation monitoring system. Smart Infrastructure is also discussed which includes Smart Water, Smart Grid and Energy, and structural health monitoring. Smart Transport is also discussed in this chapter which includes the Vehicle Infrastructure Pedestrian (VIP), Smart Parking System (SPS) and Automated Incident Detection system (AID). The final section concludes the chapter by discussing the challenges in the smart city environment and future enhancements.

PurposeWaste can be converted to a high-value asset if treated properly with smart solutions. The purpose of this research is to identify critical barriers hindering smart waste management (SWM) implementation in developing economies using comparative analysis and a mixed-method approach. The objective of this work is to provide exhaustive insight including the smart cities projects to discuss the deferring parameters toward IoT-enabled waste management systems.Design/methodology/approachTo accomplish the objective, the present study followed mixed-method approach consisting of two phases: In the first qualitative phase, barriers in the adoption of IoT (Internet of Things) for SWM were identified using extensive literature review and discussion with selected experts. In the second phase, the quantitative analysis using the Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) method was performed on the selected barriers. The fuzzy DEMATEL methodology helps in prioritizing the most significant causal barrier by separating them into the cause-effect group. The comparative analysis was used to understand two different perceptions. To provide more detailed insight on the problems faced while implementing SWM in developing economies.FindingsThe results disclose that “Lack of government strict regulatory policies,” “Lack of proper financial planning” and “Lack of benchmarking processes” are the most critical causal barriers toward IoT-enabled SWM implementation that are hindering the vision of efficient and effective waste management system. Also, “Difficulty in implementing innovative technologies” and “Absence of Dynamic Scheduling and Routing” fall under the potential causal category. The effect barriers include “Lack of awareness among the community,” “Lack of source segregation and recycling commitment” and “Lack of service provider” as concluded in results considering the comparative analysis. The results can aid the policy-makers and stakeholders to identify the significant barriers toward a sustainable circular economy and mitigate them when implementing IoT-enable waste practices. Also, it assists to proactively build programs, policies, campaigns and other measures to attain a zero-waste economy.Research limitations/implicationsThe research is focused on the context of India but it provides new details which can be helpful for other developing economies to relate. The research addresses the call for studies from public-sector and citizen’s perspectives to understand the acknowledgment of SWM systems and critical success factors using qualitative and exploratory method analysis.Practical implicationsThe practical implications of the study include strict regulatory policies and guidelines for SWM acceptance, proper financial administration and benchmarking waste-recycling practices (prominent causal barriers). The practical implication of the results includes assistance in smart city projects in handling barriers proactively. The “Lack of Benchmarking processes” provides a critical application to standardized recycling practices in developing economies to improve the quality of the recyclable material/product. The comparative analysis also provides in-depth reflection toward the causal barriers from both the perspective which can help the government and stakeholders to work in a unified manner and establish an efficient waste management system. The results also conclude the need for targeted training programs and workshops for field implementation of innovative technologies to overcome the causal barrier. Moreover, policy-makers should focus to improve source segregation and recycling practices and ensure dedicated communication campaigns like Swachh Bharat Abhiyan to change the behavioral functioning of the community regarding waste. Lastly, developing economies struggle with the adequacy of resources to establish SWM systems, hence the authors conclude that proper financial planning is required at the ground level for smart city projects to overcome the spillover effects.Social implicationsThe social implications of the study include a reduction in pollution and efficient handling of waste resulting in a healthier and cleaner environment using IoT technology. Also, the results assist decision-makers in developing economies like India to establish smart city projects initiatives effectively to improve the quality of life. It proposes to establish standardized recycling processes for the better quality of recyclables and help in attaining a sustainable circular economy.Originality/valueThe research is novel as it provides comprehensive and comparative information regarding the barriers deferring SWM including the field barriers. To our consideration, the present study serves the first to address the comparative analysis of barriers in IoT-enabled waste systems and establish the relationship from both the perspective in middle-lower income economies. The study also suggests that the effect barriers can be overcome automatically by mitigating the causal barriers in the long run.

Smart Waste Management (SWM) system is a waste management system that tracks the status of fill level of trash bins equipped with ultrasonic sensors and tracks GPS-equipped trash collection trucks. The problems identified are the overflowing trash bins especially in public areas with high population density and the complaints from the residents or public complaints regarding the punctuality of trash collection trucks. The objectives of the project are to design a Smart Waste Management (SWM) system based on Bootstrap platform, develop the system and test its functionality in fulfilling the requirements of the project. The methodological approach selected in this project is the waterfall methodology in which it comprises of four crucial phases: planning and analysis, system design, system implementation and system testing whereby each phase must be completed systematically prior to the commencement of subsequent phase. It is expected that the Smart Waste Management (SWM) system would be able to fulfill all of the project's objectives. This system is aimed to address the problems of overflowing trash bins and public complaints on trash collection trucks. The development of this system brings a huge significance in which operators would be able to know which trash bins require immediate collection and request for immediate dispatch by collection trucks. This method is seen to be more efficient compared to routine collection. Operators would also be able to track the relevant dispatch trucks through this convenient system.

Recently, the transformation of existing cities into smart cities has become an urgent necessity to solve urban problems by linking the dimensions of sustainability with information and communication technology to enhance the quality of life. The current research problem lies in the rapid increase in urban population growth, the increase in resource consumption over the past few decades, and the increase in migration rates from the countryside and neighboring villages to urban cities, which lead to an increase in the problem of solid waste management. Conventional waste management systems are not equipped to handle the excess waste generated by a growing population. To help bridge the gap, societies need to adopt smart waste management solutions that increase efficiency and reduce collection costs. Hence, transforming the solid waste management system into a Smart solid waste management system has become an urgent need for solid waste collection and treatment to achieve the Sustainable Development Goals (SDGs), especially in developing countries. Therefore, the study adopts examining the performance of East Mansoura city as a case study in the field of solid waste collection and treatment. To achieve the objective of this study, the key performance indicators for smart sustainable cities KPIs for SSC that were developed by the ITU and related to the collection and treatment of solid waste were adopted to measure and evaluate the current situation for the selected case study. Finally, an action plan was proposed to transform the waste management system in East Mansoura into a smart waste management system.

Smart city technology evolved with the developments in wireless sensor networks (WSN) and the internet of things (IoT). IoT-based waste management is an advanced waste management system offered in smart cities. The practice of monitoring, transporting, and processing of solid waste are included in the waste management. Litter bins play an indispensable role in the waste collection process at the primary level. The process of monitoring litter bins would become difficult for the ones placed at out of reach areas and remotely located sites. Smart litter bin (SLB) is generally embedded with different types of sensors where used for sensing the garbage levels and locating the bins location. Radio frequency identification (RFID), sensors, global positioning systems (GPS), general packet radio service (GPRS) are the components in smart waste management system and are discussed in this chapter. These components were used to monitor the collection, transportation, processing, and dumping. This chapter also focuses on the perception of IoT architecture to upgrade waste management in smart cities.