Development of clustering with Bayesian algorithm for optimal route formation in software-defined radio underwater WSN

Abstract

Underwater wireless sensor networks (UWSNs) have recently offered chances to investigate oceans and thus enhance the underwater world. WSNs are imperative for discovering the ocean region. Software-defined networking (SDN) improves flexibility and uses the clustering method to improve lifespan. This article introduces the Development of a clustering process with a Bayesian algorithm (CPBA) for optimal route formation in software-defined radio UWSN. The clustering concept improves energy efficiency; however, cluster head (CH) selection is challenging. The present clustering mechanisms could be more successful in suitably assigning the node's energy. This mechanism utilizes a slap swarm optimization algorithm to pick out the optimal CH by node energy and distance among inter-cluster as well as intra-cluster. In addition, the Bayesian algorithm selects the best forwarder from sender to base station. Thus, enhances efficiency. The simulation results demonstrate that the UWSN improves both the 23% packet forward ratio and 0.014 joule energy. Furthermore, it minimizes the 30% network delay.