Abstract:

Energy-efficient routing optimization in underwater acoustic sensor networks (UASNs) is a challenging problem, but it is critical for the performance and lifetime of the networks used in ocean monitoring and data gathering.  To resolve the energy-efficient routing problem in UASNs, a hybrid optimization technique (HOP) has been proposed, which integrates Cat Swarm Optimization (CSO) and COVID-19 algorithms. Most existing approaches to UASNs have problems with inefficient route selection, excessive energy consumption, and prolonged convergence times. These issues limit the effectiveness of some standard algorithms which need to adjust to the ever-changing undersea environment and the evolving network conditions HOP focuses on energy efficiency and reducing routing, communication, and control overhead in UASNs. CSO enhances the routing search processes using energy-efficient routes, while the dynamic nature and rapid convergence demand of the network are countered by the resilience offered with the COVID-19 method. This study shows that HOP outperforms previous algorithms in terms of energy efficiency, routing, and time taken to converge, making it a viable candidate for underwater sensor networks_IPV4-real time applications.