AI-Enabled Autonomous Cyber-Physical Systems Using Intelligent Sensors, Edge Computing, and Secure Wireless Communication
DOI:
https://doi.org/10.29284/xnqcnf41Keywords:
Cyber-Physical Systems; Edge Computing; Intelligent Sensors; Secure Wireless Communication; Artificial Intelligence; Autonomous Systems; Anomaly Detection.Abstract
Cyber-Physical Systems (CPS) integrate computational intelligence with physical processes to enable autonomous decision-making in domains such as manufacturing, transportation, healthcare, and smart grids. However, conventional CPS architectures that rely on centralized cloud processing suffer from high latency, bandwidth constraints, and vulnerability to cyber-attacks, which limit their applicability in time-critical and safety-critical operations. This paper proposes an AI-enabled autonomous CPS framework that unifies intelligent multi-modal sensors, edge-based artificial intelligence (AI) inference, and secure wireless communication into a coherent five-layer architecture. The proposed system embeds lightweight convolutional neural network (CNN) and long short-term memory (LSTM) models at the edge to perform real-time anomaly detection and local actuation, while a secure communication layer employs Advanced Encryption Standard (AES-256) with blockchain-assisted key exchange to preserve data confidentiality and integrity across the network. A prototype implementation was evaluated against conventional non-AI and cloud-only AI baselines using latency, detection accuracy, energy consumption, packet delivery ratio, encryption overhead, and mean-time-to-recovery as performance indicators. Experimental results demonstrate that the proposed architecture reduces end-to-end latency by approximately 82% relative to the conventional baseline, improves anomaly detection accuracy to 97.3%, and lowers energy consumption per inference by 57%, while maintaining a packet delivery ratio above 99%. These findings confirm that combining intelligent sensing, edge computing, and secure wireless protocols yields a scalable, resilient, and low-latency foundation for next-generation autonomous cyber-physical systems.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
