Partical Competitive swarm optimizayion (PCSO) – An optimization enabled routing algorithm for heterogeneous wireless sensor networks

Abstract

The evolution of wireless communication, particularly Wireless Sensor Networks (WSNs) are being used extensively in various areas, like military, medical, etc. As an enhancement over WSN in node resources and topology, Heterogeneous WSN (HWSN) is developed. HWSN depends on the heterogeneity of nodal energy.To improve the network lifetime, a routing algorithm, named Particle Competitive Swarm Optimizer (PCSO), is proposed considering several factors, such as predicted residual energy, Link Lifetime (LLT), distance, and delay. PCSO is proposed by the hybridization of Particle Swarm Optimization (PSO) and Competitive Swarm Optimizer (CSO), which is a variant of PSO algorithm. Moreover, the residual energy of the nodes is predicted using Deep Recurrent Neural Network (DRNN) to extend the network’s lifespan during routing. The effectiveness of the proposed PCSO-based routing is proven by analyzing its performance. This has been validated by an evaluation of the effectiveness of the proposed methodology in comparison to existing work. using four metrics, namely residual energy, delay, link lifetime and throughput. The experimentation carried out reveals the proposed PCSO-based routing algorithm has better performance with the maximum residual energy of 0.000100285J, minimum delay of 0.21945s, maximum throughput of 58.03571, and maximum LLT of 0.0122sec.