Artificial intelligence enabled energy aware clustering technique for sustainable wireless communication systems. (March 2023) - Tirth, Vineet; Alghtani, Abdulaziz H.; Algahtani, Ali
Abstract: Green energy is one of the effective solutions for future sustainability and finds use in several application areas. Sustainable communication aims to decrease energy utilization of the system and increase resource utilization to a maximum extent. On sustainable wireless sensor network (WSN), the restricted battery capacities of the sensors necessitate energy efficient protocols to accomplish sustainable communication. Earlier works reported that clustering is one of the effective techniques employed to accomplish energy efficiency. Since clustering is treated as an NP hard optimization problem, meta-heuristic optimization algorithms are executed. In this aspect, this study designs an artificial intelligence enabled energy aware clustering scheme for sustainable wireless communication system, named AIEAWC-SWCS technique. The major intention of the AIEAWC-SWCS technique is for attaining sustainable energy management in WSN. To accomplish this, the AIEAWC-SWCS model derives a novel lens oppositional based learning (LOBL) with deer hunting optimization algorithm (DHOA) for optimum selection of cluster heads (CHs) and construct clusters. The proposed AIEAWC-SWCS model computes a suitability purpose with four variables namely energy competence, cluster density, inter-cluster coldness, and intra-cluster distance. The nodes with maximum fitness values have the ability to become CHs and perform sustainable communication. The AIEAWC-SWCS approach demonstrates a minimum MPLR of 2.67 percent with 200–100 base stations. It is guaranteed to be able to carry out WSN sustainable energy management. The experimental validation of the AIEAWC-SWCS model is tested in various features and the comparative analysis shows the potential of the AIEAWC-SWCS model in accomplishing sustainable communication.