The Internet of Things (IoT) has emerged as a transformative paradigm that interconnects billions of smart devices, sensors, and systems to enable seamless data exchange and intelligent automation. With widespread adoption across critical sectors including smart cities, industrial automation, healthcare, transportation, and smart homes, IoT delivers substantial economic and social value by enhancing operational efficiency, service personalization, and resource optimization. However, the exponential growth of IoT ecosystems—characterized by device heterogeneity, distributed architectures, and massive data transmission—poses unprecedented challenges in ensuring information security (e.g., data privacy, access control, and threat mitigation) and developing robust machine learning models for real-time analytics, anomaly detection, and adaptive decision-making. To address these issues, researchers are exploring innovative methodologies such as federated learning, lightweight cryptography, and AI-driven threat intelligence. The interdisciplinary nature of IoT research necessitates collaboration among experts in computer science, cybersecurity, data science, and electrical engineering, whose collective insights drive technological breakthroughs and risk mitigation strategies. In summary, IoT is a rapidly evolving field where machine learning and information security are foundational pillars, holding immense potential to unlock safe, reliable, and innovative applications that reshape industries and daily life.

This collection aims to bring together researchers and practitioners from academia and industry to present their latest findings, discuss recent advances, and exchange ideas on machine learning and information security for IoT. The collection will focus on the development of novel models, control strategies, learning techniques, and security methods for IoT, with an emphasis on their application to real-world problems. Analytical, numerical, and experimental works which contribute to the development of machine learning and information security for IoT, are welcome.

This Collection supports and amplifies research related to SDG11.

Keywords: Machine Learning; Information Security; Internet of Things; Control Systems; Signal Processing; Mathematical Optimization; Complex Networks; Big Data and Data Mining; Modelling and Simulation; Neural Networks

The goal of this Topical Collection is to investigate the novel ways that the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), cyber-physical systems, CPS, smart cities, 5G, and Internet of Vehicles (IoV) can be used to address problems and promote sustainable development in the fields of urban sustainability, health, and agriculture. The aim of this showcase is to provide innovative research, techniques, and case studies that illustrate how technology integration can significantly improve human well-being, food security, and environmental sustainability.

This Topical Collection is significant because it has the potential to further research, spur innovation, and motivate action in support of sustainable development objectives. This Topical Collection can accelerate the integration of diverse developing technologies and their applications for smart cities by uniting interdisciplinary viewpoints and presenting best practices. Sub-themes or topics mapping to the scope of the Topical Collection proposal include but are not limited to the following:

• 5G and IoT Integration and Interoperability and Standards in IoT

• Agri-Tech Solutions for Sustainable Agriculture & Precision Agriculture

• AI and Machine Learning in Cyber-Physical Systems

• AI Enabled Smart Health Solutions

• Blockchain for IoT Security and Trust

• Edge Computing in IoT Systems

• Health and Wellness in Urban Environments

• Intelligent Transportation Systems using Internet of Vehicles (IoVs) and Vehicular Networks

• IoT and AI based Smart Home Innovations for Health and Comfort

• IoT and AI Enabled Smart City Infrastructure and Urban Development

• Medical Technology Advancements Using IoT and AI

• Pervasive and Ubiquitous Computing

• Security and Privacy in IoT Networks

• Sensors and Devices for IoT and CPS

• Smart Environmental Monitoring and Conservation using IoT sensors, satellite imagery, and data analytics

• Sports Science and Performance Enhancement using Data Analytics and AI

• Virtual Reality, Augmented Reality and Mixed Reality in Industry

Keywords:

Internet of Things (IoT); Artificial Intelligence (AI); Emerging Technologies; Smart Cities; Cyber Physical Systems