Guest Editorial: Smart cities 2.0: How Artificial Intelligence and Internet of Things are transforming urban living

The evolution of smart cities marks a profound shift in urban life globally, where new technologies enhance efficiency, sustainability, and the quality of life for residents. At the forefront of this transformation are Artificial Intelligence (AI) and the Internet of Things (IoT), driving cities into a new era of innovation. AI and IoT connect devices and infrastructure, enabling cities to process vast amounts of data efficiently. These technologies have already revolutionised various aspects of daily life. IoT, for example, powers intelligent systems in logistics, healthcare, and automotive technology. In line with the trend of advancing urban technologies, this Special Issue aims to present the latest advancements and explore the opportunities and challenges of integrating these technologies into city infrastructure. It provides policymakers, urban planners, and stakeholders with critical insights into how these innovations shape the future of our cities. By sharing best practices, we highlight the potential of AI and IoT to foster smarter, sustainable, and more liveable cities. This issue underscores the importance of integrating these technologies into city planning and development, empowering stakeholders to drive positive change and build resilient urban communities. The issue contains a curated selection of six papers, each offering groundbreaking insights into how AI and IoT are revolutionising urban living. From air quality prediction to cybersecurity and digital twin cities, these studies showcase diverse applications that are shaping the future of smart cities worldwide. In their paper titled ‘Optimising Air Quality Prediction in Smart Cities with Hybrid PSO-LSTM-RNN Model’, Dalal et al. introduce a novel approach combining Particle Swarm Optimisation, Long Short-Term Memory, and Recurrent Neural Network. The model demonstrates superior performance in predicting air quality indicators using data from Salt Lake City, USA. The study demonstrates significant improvements in RMSE (Root Mean Square Error), Mean Absolute Error, Mean Absolute Percentage Error, and R2-Score compared to existing methods. Santos et al. present a case study in ‘Smart Resilience through IoT-Enabled Natural Disaster Management: A COVID-19 Response in São Paulo state’, highlighting the role of IoT in managing the COVID-19 pandemic. This case study demonstrates how IoT technologies were utilised during the COVID-19 pandemic to monitor mobility, social distancing, and economic recovery in São Paulo state, Brazil. Their approach leverages data-driven insights to support crisis management and enhance urban resilience. Ahmed et al. propose a machine learning-based security approach in ‘Securing Smart Cities Through Machine Learning: A Honeypot-Driven Approach to Attack Detection in IoT Ecosystems’. Their research focuses on using honeypot data and machine learning algorithms to enhance IoT security by detecting and mitigating cyber-attacks. The study contributes practical insights and methodologies for strengthening cybersecurity in smart city infrastructures. Bellodi et al. explore predictive analytics in ‘Predicting the impact of public events and mobility in Smart Cities’, using AI techniques to forecast crowd behaviour and density during public events, offering valuable insights for optimising urban mobility and event management strategies. The research aims to improve decision-making processes in smart city planning and management. Rehman et al. introduce a state-of-the-art fire surveillance model in ‘Smart City Fire Surveillance: A Deep State-Space Model with Intelligent Agents’. Their novel approach integrates convolutional neural networks and multilayer perceptrons for proactive fire detection and surveillance in urban environments. The study demonstrates the effectiveness of intelligent agents guided by a state-space navigational model, enhancing public safety measures in smart cities. Kommey et al. propose an AI-based energy monitoring system in ‘An AI-based Non-intrusive Load Monitoring of Energy Consumption in an Electrical Energy System Using a Modified KNN Algorithm’. Their study proposes a non-intrusive approach using AI and machine learning to monitor energy consumption in smart electrical systems. The research provides insights for optimising energy usage and enhancing sustainability in smart cities, supporting efficient decision-making and planning of energy needs. All of the papers selected for this Special Issue showcase the diverse and transformative potential of AI and IoT technologies in shaping the future of smart cities. From optimising air quality prediction using advanced hybrid models to enhancing cybersecurity through machine learning-driven approaches, each study contributes unique insights and practical solutions. Additionally, research on digital twin cities, ICT acceptance models, and art-based interventions underscores the interdisciplinary nature of smart city development, emphasising community engagement and sustainable urban planning. These findings collectively highlight the pivotal role of technological innovation in fostering resilience, efficiency, and inclusivity within urban environments. As smart cities continue to evolve, the lessons and advancements presented in this issue provide valuable guidance for policymakers, urban planners, and researchers striving to build more intelligent and liveable cities worldwide. Data sharing is not applicable to this article as no new data were created or analysed in this study. Zhengyi Chai is currently a professor at the school of computer science and technology, Tiangong University, Tianjin, China. He received his Ph.D. degree in computer science in China in 2012. He was a visiting scholar with the department of computer science, University of Nottingham, UK. He is selected as an Innovation Leading Talent of Tianjin University Discipline. In recent years, he has managed two projects from the National Natural Science Foundation of China, one project from the Tianjin Natural Science Foundation of China, and more than ten other projects. He also serves as a reviewer of the National Natural Science Foundation of China (NSFC). He has published his own academic monograph and over 50 papers in international journals and conferences. He has contributed to the development of 20 copyrighted software systems and invented over 6 patents. He served as the editorial board member of the ‘Journal of Computer Engineering’. He is also a member of the China Computer Federation (CCF) and a member of ACM and the Chinese Institute of Electronics. He serves as the general chair, the PC chair, and the workshop chair or a TPC member of a number of conferences. He also serves as guest editor/reviewer of some SCI/EI indexed journals. His research interests include the Internet of Things and artificial intelligence (especially in machine learning and intelligent computing). Syed Attique Shah received the Ph.D. degree from the Institute of Informatics, Istanbul Technical University, Istanbul, Turkey. During his Ph.D. degree, he studied as a Visiting Scholar with the National Chiao Tung University, Taiwan, the University of Tokyo, Japan, and the Tallinn University of Technology, Estonia, where he completed the major content of his thesis. He has worked as an Associate Professor and the Chairperson at the Department of Computer Science, BUITEMS, Quetta, Pakistan. He was also engaged as a Lecturer at the Data Systems Group, Institute of Computer Science, University of Tartu, Estonia. Currently, he is working as a Lecturer in Smart Computer Systems, at the School of Computing and Digital Technology, Birmingham City University, United Kingdom. He has published more than 20 research papers in reputable Q1 journals with a cumulative impact factor of more than 100. He has also presented several research papers at prestigious conferences. He is nominated as an IEEE Senior Member and a professional member at British Computer Society. As a researcher, his academic pursuits are cantered on the exploration of innovative technologies, including but not limited to big data analytics, machine learning, information management, software-defined networking, and the Internet of Things. Dirk Draheim received the Ph.D. degree from Freie Universität Berlin and the Habilitation degree from the University of Mannheim, Germany. Currently, he is a Full Professor of information society technology with the Tallinn University of Technology, Estonia, where he is the Head of the Information Systems Group. The Information Systems Group conducts research in large and ultra-large-scale IT systems. He is also an Initiator and the Leader of numerous digital transformation initiatives. He is an author of the Springer books Business Process Technology, Semantics of the Probabilistic Typed Lambda Calculus and Generalised Jeffrey Conditionalisation, and a coauthor of the Springer book Form-Oriented Analysis. Sufian Hameed received the Ph.D. degree in networks and information security from the University of Göttingen, Germany. He works as a full Professor at the Department of Computer Science, National University of Computer and Emerging Sciences, Pakistan. He also leads the IT Security Laboratories, NUCES. The research laboratory studies and teaches security problems and solutions for different types of information and communication paradigms. His research interests include network security, web security, mobile security, and secure architectures and protocols for Cloud and the IoTs. M. Mazhar Rathore received the master’s degree in computer science and communication security from the National University of Sciences and Technology, Pakistan, in 2012 and the Ph.D. degree in computer science and engineering from Kyungpook National University, South Korea, in 2018. He is currently a Postdoctoral Researcher at the University of New Brunswick, Canada. His research interests include big data analytics, the Internet of Things, smart systems, network traffic analysis and monitoring, remote sensing, smart cities, urban planning, intrusion detection, and information security and privacy. He is a Professional Member of the IEEE and the ACM. For his paper ‘IoT-Based Smart City Development Using Big Data Analytical Approach’, he received the Best Project/Paper Award at the Qualcomm Innovation Award 2016 at Kyungpook National University, South Korea. He was also the 2015 IEEE Communications Society Student Competition Best Project Award Nominee for his project ‘IoT-Based Smart City’. He is serving as a reviewer for various reputed IEEE, ACM, Springer, and Elsevier journals.

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