Enhancing Cyber Physical Systems Performance Using Edge Computing and Adaptive IoT Network Architectures for Real Time Applications

Abstract

Cyber Physical Systems (CPS) demand high performance and low latency for real-time applications, which cloud-centric architectures often fail to achieve due to communication delays. This research investigates the enhancement of CPS performance through integration of edge computing and adaptive IoT network architectures. The proposed method utilizes distributed edge nodes for localized data processing combined with a self-adaptive IoT routing architecture employing artificial neural networks, enabling dynamic real-time reconfiguration based on network conditions. Experimental evaluation on real-time CPS scenarios demonstrates significant improvements, with system reliability increasing by 13.5% and processing performance by 28.6%, achieving accuracy exceeding 95%. These improvements notably reduce latency and bandwidth consumption while maintaining high data security and system trustworthiness. The study concludes that leveraging edge computing and adaptive IoT networks substantially optimizes CPS operations, enabling efficient and reliable real-time response critical for applications such as autonomous systems and smart industrial environments. Future work focuses on enhancing scalability and security for broader CPS deployment.