Journal of AI, Data Science and Cyber Systems

The implementation of Artificial Intelligence in Cyber-Physical, IoT, and Edge Systems refers to the integration of intelligent digital systems with physical components and other smart devices, such as sensors and constrained devices. This includes systems such as smart grids, self-driving cars, industrial control systems, healthcare technologies, smart city solutions, and extensive IoT systems that require real-time interaction with the physical world. AI helps these systems run perception, prediction, optimization, and control functions, by analyzing data in real-time from distributed sensors and making quick decisions at the data source. The engagements of cyber-physical systems, the Internet of Things (IoT), and edge computing are characterized by stringent requirements of latency, reliability, resource (energy and computational) efficiency, and security. This imposes the need for AI models to be lightweight, robust, and adaptive. Edge AI obviates the need for a centralized cloud by facilitating local processing of data (inference and decision-making), resulting in improved responsiveness, greater privacy, and increased resilience to network disruptions. Simultaneously, systems must be defended from cyber threats with the potential to inflict physical damage. This necessitates the integration of security, threat (anomaly) detection, and fault-tolerant design. Therefore, AI for cyber-physical systems, IoT, and edge systems combines intelligent automation with real-world safety, scalability, and trust. This increasingly promotes enhanced and secured interactions between the physical and digital worlds.