Emperor Penguin Optimized Q Learning Method for Energy Efficient Opportunistic Routing in Underwater WSN

Underwater Wireless Sensor Network (UWSN) is a popular technique for monitoring marine environments. It consists of several acoustic channels, sink nodes, sensor nodes and base stations. Source nodes are deployed at different depth levels of oceans for monitoring purposes. Acoustic channels communicate between a source node and a sink node. After that, the information from the sink node is delivered to the base station using an RF signal. Limited Bandwidth, Energy Consumption and propagation delay are major challenges faced by UWSN. Battery consumption of sensor nodes leads to a great impact on the performance of the underwater wireless network. Void-hole occurrence and duplication of packets from sensor nodes to sink nodes increases energy dissipation and reduces the lifespan of the UWSN. Hence energy-efficient opportunistic routing with emperor penguin optimized Q learning method (EPO-Q) for UWSN was introduced to avoid the void-hole problem and reduce energy dissipation. The proposed work was implemented in the MATLAB platform and improved performance in terms of end-to-end delay, network overhead, energy consumption, accumulated propagation distance, battery power, packet loss ratio, absorption loss, throughput, energy efficiency and packet delivery ratio.