In today's world, Underwater Wireless Sensor Networks (UWSN) have become more and more necessary for multiple applications such as measuring water levels, tracking offshore issues, and tracking the impacts of underwater conditions. Based on surveillance records, such as the harshness of waves, salty state and ocean, the observation is based on the observation. The lack of balance in energy consumption results in some sensor nodes getting damaged, resulting in holes. In the proposed work, we compare Beetle Antenna Search Algorithm with Genetic Algorithms for optimizing USWN selection. Deep learning and optimized energy consumption with 20.83% are compared with genetic algorithms with 17.34%. In addition, protocols that classified network nodes by bandwidth and lifetime gave results of 30.76%, which is lower than existing algorithms....
Authors: Neeta Sharma, Anamika Singh.