Reducing the Network Latency to Maintain Network Stability in UASN by Using Bio-Inspired Algorithms

Underwater Acoustic Sensor Networks, also known as UASNs, are an important component of many different types of systems, including those used for environmental monitoring, maritime exploration, and underwater surveillance. However, UASNs confront a number of issues, one of which is a high network latency, which can have a substantial influence on both the stability and performance of the network. Utilizing the Dolphin Swarm Optimization (DSO) algorithm and contrasting it with the Cuckoo Search Algorithm (CSA) is what this study suggests as a revolutionary method to lessen the amount of time it takes for a network connection to be established as well as to keep the connection stable. This study performs simulations with a realistic model of an underwater environment to determine which method is more efficient at cutting down on the amount of time it takes for network requests to be processed. This study evaluates the performance of DSO and CSA regarding the decrease of latency, stability, and energy utilization. The findings of the simulation show that the DSO algorithm outperforms the CSA algorithm when it comes to lowering network latency while keeping network stability intact. The DSO method optimizes the network's topology, increasing communication effectiveness and decreasing the time it takes for packets to be transmitted. In addition, DSO displays superior resistance to network disturbances and node failures compared to CSA.