Guest Editor(s)
Dr. Ateeq Ur Rehman
Email: 202411144@gachon.ac.kr
Affiliation: School of Computing, Gachon University, Seongnam-Si, Republic of Korea
Homepage:
Research Interests: internet of things (iot), smart cities, big data, renewable energy, blockchain
Dr. Sunawar Khan
Email: 2251200@ncbae.edu.pk
Affiliation: School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
Homepage:
Research Interests: smart cities, blockchain, cyber security, intrusion detection, fog and edge computing, IoT
Dr. Rizwan Tariq
Email: rizwan.tariq@ieee.org
Affiliation: Project Manager, National Power Construction Corporation (NPCC), Dammam, Saudi Arabia
Homepage:
Research Interests: fault detection and location, smart grids, renewable energy integration, electrical power system design, smart cities
Summary
The rapid proliferation of Internet of Things (IoT) devices is generating unprecedented volumes of real-time urban data, while deep learning has emerged as the leading paradigm for extracting actionable intelligence from this data. Integrating IoT sensing infrastructure with deep learning models is now central to realizing responsive, sustainable smart cities, spanning traffic management, energy optimization, public safety, and environmental sustainability. Deploying deep learning at the edge of IoT networks, however, introduces challenges related to scalability, latency, security, privacy, interoperability, and energy efficiency that must be addressed for these solutions to reach their full potential.
This Special Issue aims to bring together original research and comprehensive reviews advancing the design, deployment, and evaluation of IoT-integrated deep learning solutions for smart cities. It welcomes contributions addressing novel architectures, edge and federated learning approaches, real-time analytics, security and privacy mechanisms, and case studies demonstrating measurable impact on urban infrastructure and services.
Suggested themes for this Special Issue include, but are not limited to:
• Deep learning architectures for IoT-based smart city applications
• Edge and federated learning for real-time urban analytics
• Intelligent transportation systems and traffic prediction
• Smart energy management and renewable energy integration
• IoT security, privacy, and trust in smart city networks
• AI-driven public safety, surveillance, and disaster management
• Big data analytics and cloud/fog computing for smart cities
• Explainable and trustworthy AI for urban decision-making
• Sustainability, scalability, and interoperability challenges in smart city IoT deployments
Keywords
internet of things (IoT), deep learning, smart cities, edge computing, federated learning, big data analytics, cybersecurity and privacy, intelligent automation, sustainable urban systems
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