Edge Intelligence, IoT and Cyber-Physical Systems for Next-Generation Automation

Abstract

Intelligent automation increasingly depends on computation occurring close to sensors, machines, and physical processes rather than exclusively in centralized cloud infrastructures. Industrial Internet of Things deployments, embedded artificial intelligence, autonomous devices, digital twins, and cyber-physical systems generate continuous data streams whose operational value often depends on low latency, reliable connectivity, privacy preservation, energy efficiency, and immediate local response. This editorial argues that edge intelligence should therefore be understood as a fundamental architectural component of next-generation automation rather than merely a reduced form of cloud computing. The emerging landscape combines distributed inference, embedded machine learning, federated learning, real-time networking, digital twins, predictive maintenance, and edge-cloud coordination, but substantial challenges remain in constrained computation, heterogeneous hardware, interoperability, cybersecurity, synchronization, model lifecycle management, fault tolerance, and distributed-system validation. Future progress requires energy-efficient models, hardware-software co-design, resilient communication and control, secure edge architectures, standardized benchmarking, trustworthy federated learning, and validation under realistic hardware and network conditions. Dependable automation will ultimately require coordinated design across sensing, communication, computation, intelligence, and physical control.