AI-Driven Energy-Aware Clustering and Communication Protocols for 6G-Enabled Cyber-Physical Systems: A Review

From Classical Clustering to AI-driven Unmanned Clustering for Energy-efficient CPS Communications. The rapid development of Cyber-Physical Systems (CPS) and Internet of Things (IoT) networks into the era of 6G connectivity is creating a pressing requirement to develop intelligent, energy-efficient communication mechanisms. While the classical Wireless Sensor Network (WSN) protocols were developed to cater to static sensing environments with a limited number of nodes, the developing CPS environments demand domain-appropriate, autonomous, and scalable communication frameworks. This review paper studies the evolution of clustering and routing protocols from classical WSN models to the modern AI-assisted communication models. The investigated models are evaluated based on their energy-efficiency, scalability, reliability, and network stability. The evolution of clustering mechanisms from classical, baseline, optimization, and AI-assisted approaches is analysed and benchmarked using comparative tables and performance trend analysis. Existing gaps in the protocols are highlighted in terms of the non-existence of fully integrated intelligence and energy-adaptive communication mechanisms that are required for the imminent 6G CPS systems. The findings reveal the necessity for eco-adaptive, learning-driven communication frameworks that can sustainably cope with scalability requirements and optimize critical issues such as trade-off in network lifetime, reliability, and intelligence in decision-making processes.