Improved hybrid swarm intelligence technique to enhance wireless sensor network data aggregation and routing

Abstract

Wireless Sensor Networks (WSN) is energy constrained as these newlinenetworks are formed by several sensor nodes that are energized by battery. newlineFor the application to run well, the Quality of Service (QoS) must be newlinemaintained. Energy efficiency, network lifetime, delay, and throughput are a newlinefew of the WSNand#8223;s key QoS parameters. An effective way for improving newlineenergy usage and WSN lifespan is dividing the network into varied groups or newlineclusters. The sensor nodeand#8223;s energy specification is quite complex as it will newlineneed optimized variables for efficient clustering as well as routing, which in newlineturn, is a Non-deterministic Polynomial-time (NP) hard problem. As a result, newlinethe development of diverse Swarm Intelligence (SI) techniques is necessary to newlineextend the network lifetime using limited power, and also to focus on various newlineprinciples of transmission. newlineThe main objective of this research is to adapt various newlinemetaheuristic optimization algorithms to optimize the clustering of the sensor newlinenodes in WSN and to select an optimal set of routes to achieve energy newlineefficiency and maximize network lifetime. Various metaheuristics like newlineChaotic Sandpiper Optimization (CSPO) algorithm, Groundwater flow newlineoptimization technique, Glowworm Swarm Optimization (GSO) and newlineQuantum Salp Swarm optimization Algorithm (QSSA) are explored to newlineachieve energy efficiency and maximizing network lifetime. newlineThe CPSOC-based clustering technique finds an optimal Cluster newlineHead (CH) set. The GFLR Based Routing Technique (GFLR) will be newlineemployed to pick the optimal route for inter-cluster transmission. newline