Enhancing TEEN Protocol using the Particle Swarm Optimization and BAT Algorithms in Underwater Wireless Sensor Network

Abstract

Recently, underwater wireless sensor networks (UWSNs) have been emphasized due to their immense value in monitoring the underwater environment and expanding applications for target recognition and underwater information gathering. Battery power is restricted underwater, and it is also difficult to replace, which limits the power supply. As a result, studies and research seek to extend the life of the network. The proposed Threshold Sensitive Energy Efficiency Sensor Network (TEEN) protocol, along with particle swarming optimization (PSO) and BAT algorithms disclosed in this paper, attempts to improve network lifetime and power consumption via optimal node distribution and cluster header selection. The K-mean technique is used in each algorithm that separates nodes into clusters and selects for each cluster a point to be the central point from which to choose the best node to be the block head (CH). This selection is based on the node with the most energy as well as the node closest to the center point. After this stage, the proposed algorithms continue with Particle Swarm Optimization (PSO), and BAT Apply Cluster Head Update (CH), until the best map is produced. The results revealed that the proposed protocol resulted in a significant reduction in power consumption and network lifetime compared to the original protocol. The results also show that TEEN enhanced with BAT is better than TEEN enhanced with PSO.