Improved Jellyfish Search Algorithm based Multipath Routing with Atom Search Algorithm for Best CH Selection

Abstract

Several regions that are inaccessible to people have benefited from the deployment of Mobile Ad hoc NETwork (MANET) apps. There is a new topic of study related to low-power usage in MANET. To maximize energy efficiency and extend the MANET's lifespan, it is necessary to use clustering and routing strategies. Despite this, one of the primary obstacles in sensor networks is routing, which is crucial for the timely transmission of sensed data to the base station. Clustering, which increases scalability, and Multipath Routing, which uses many paths to transmit data, has been employed recently to make MANETs more dependable and scalable. Clustering and routing are examples of problems that are classified as NP-Hard. Meta-heuristic optimization is well suited to this particular kind of challenge. When it comes to clustering, this research makes use of atom search optimization (ASO), and when it comes to achieving effective shortest route communication, the researchers turn to ad hoc on-demand multipath distance vector routing with enhanced jellyfish search optimization (AOMDV IJSO). IJSO method development anticipates the best course of action. Therefore, the ad hoc on-demand multipath distance vector (AOMDV) generates a set of potential pathways, and IJSO selects one that is close to ideal. Using the NS-2 simulator, the proposed protocol's effectiveness is measured across various use cases. Simulated findings show that the proposed protocol excels over the state-of-the-art in terms of Quality-of-Service network longevity.