An Enhanced Variable Delay Supported Path Loss Model for Underwater Acoustic Sensor Network

In underwater acoustic sensor networks (UASNs), the most complicated issues are higher latency, propagation delay, and transmission loss. This work presents an enhanced path loss model for the UASN to minimize these issues. Initially, random locations of sensor nodes are generated in the UASN environment. Then, the unknown depth of each sensor node is calculated. The unknown depth is computed by the distance between the source and each node. Subsequently, the status of each node is computed. Afterwards, an accurate channel is modeled by considering various parameters like spreading loss, absorption loss, sound propagation, and transmission path loss. This accurate channel model reduces the delay of communication. Then, the transmission loss is computed for the acoustic links to find the link connectivity. In case of loss in connectivity, new node positions are selected using the weighted moving average-based adaptive horse herd optimization with optimal beacon based on the mobility of nodes. Finally, the network reliability is evaluated for the developed path loss model. The performance of the proposed approach is compared with different existing approaches in terms of various performance metrics such as transmission loss, attenuation coefficient, probability density function (PDF), throughput, and average delay. The performance results proved that the proposed approach outperforms the other existing approaches.