Performance enhancement of underwater wireless sensor network using deep learning mobile edge model and optimization by BAS algorithm

Abstract

In natural resources there are many components that can handle network connectivity, interaction between many sensors or many vehicles to understand the necessity of resources. Wireless Sensor Network (WSN) has sender and receiver communication through radio signals, frequency and user equipment based on various devices connected. Underwater WSN is the most important research idea that can analyze the communication performance and difference in data rate that are referring to the fields such as marine, ship, water levels, etc. The need and necessity of Underwater Wireless Sensor Networks (UWSN) is increasing day by day for multiple applications like water level measures, offshore issues and tracking the impacts of underwater conditions. The observation is based on surveillance records that have been noted from ocean, marine, harshness of waves, salty state etc. Due to a lack of balanced energy consumption, some sensor nodes get damaged during this process, resulting in hole problems. To analyze the various research problems proposed work focuses on three objectives. The certain attraction towards this idea is to recognize the environment needs, impacts, nature deviations and many more. The aquatic environment has plenty of resource providers where the medium of communication faces complications such as delay in multilevel path, noise in harsh water, interference in nodes, bandwidth shortages etc. Basically, the research aims to improve the existing UWSN work based on routing protocols for collecting data, analyzing network variation, increasing energy consumption, and increasing battery life of the sensor nodes. newline