Artificial Neural Network-based Relay Selection in Underwater Wireless Sensor Network

As one of the most challenging and promising wireless communications, underwater communication has received much attention. According to the distance, there may be significant delay, limited bandwidth and attenuation, which reduce the system's overall performance. To solve these problems, Artificial Neural Network based Relay Selection (ANNRS) in Under Water Wireless Sensor Networks (UWSN) is introduced. This mechanism uses the Artificial Neural Network (ANN) algorithm to choose the relay nodes to pass the data from sender to receiver. The ANN algorithm picked out the relay using four input features: Energy, bandwidth, depth and packet received ratio (PRR). The UWSN may dynamically pick the optimum relay node depending on the current network circumstances by employing an ANN-based relay selection system, which improves network performance, lowers energy consumption, and increases data transmission reliability. The extensive simulation outcomes demonstrate that the ANNRS mechanism enhances the performance, such as PRR, and energy, and minimizes the delay and Assemble Propagation Distance in UWSN.