An Efficient Cluster Based Routing in Wireless Sensor Networks Using Multiobjective-Perturbed Learning and Mutation Strategy Based Artificial Rabbits Optimisation

Abstract

Wireless sensor networks (WSNs) is a wireless system including the set of distributed sensor nodes used for physical or environmental observation. A network energy expenditure is considered as a significant concern because of battery restricted sensors of the WSN. Clustering and multi hop routing are considered as effective approaches to enhance the network lifecycle and communication. Achieving the anticipated objective of reducing the energy expenditure, thereby increasing the network lifecycle, is considered as an optimisation issue. In recent times, a nature inspired meta-heuristic approaches are extensively utilised for solving the different optimisation issues. In this context, this research aims to accomplish the objective by proposing the multiobjective-perturbed learning and mutation strategy based artificial rabbits optimisation namely M-PMARO for an optimum cluster head (CH) selection and route discovery. The proposed M-PMARO incorporates an experience based perturbed learning (EPL) and mutation strategy to identify the capable regions over the search space for enhancing the exploration and avoiding the local optima issue. To formulate the multiobjective, the residual energy, average intracluster distance, average base station (BS) distance, CH balancing factor (CHBF) and node centrality are incorporated for optimum CH discovery while the residual energy and average BS distance are considered for multi hop routing. The M-PMARO is analysed based on alive nodes, dead nodes, energy expenditure, throughput and data received in BS and network lifecycle. The viability of M-PMARO is validated by comparing it with existing approaches such as fitness based glowworm swarm with fruitfly algorithm (FGF), energy balanced particle swarm optimisation (EBPSO), improved bat optimisation algorithm (IBOA), graph neural network (GNN) and fuzzy logic and particle swarm optimisation (PSO) based clustering routing protocol namely PFCRE. The alive node count of M-PMARO is 100 for 1200 rounds, which is higher than the EBPSO.