AGRIC: Artificial-Intelligence-Based Green Routing for Industrial Cyber-Physical System Pertaining to Extreme Environment

Industrial cyber-physical systems (ICPSs) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, artificial intelligence (AI) has had a significant impact on wireless sensor networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based green routing for ICPS. While following the cluster-based routing, the election of cluster head (CH) is executed using our proposed AI-inspired extended spotted hyena Levy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.