Multi-objective evolutionary algorithms for coverage and connectivity aware relay node placement in cluster-based wireless sensor networks

Abstract Wireless sensor networks (WSNs) have been extensively explored due to their incredible capabilities and ever-growing field of applications. In cluster-based WSNs, cluster heads (CHs) deplete their energy quickly due to the extra workload of data aggregation and data forwarding as relay node from the member sensor nodes (SNs). While placing the RNs in the application areas to form the clusters, coverage of all the SNs and connectivity amongst the RNs are essential for proper function of the networks. Moreover, reducing the inter-cluster distances between the RNs and SNs is important to save the transmission energy of the SNs. Moreover, the problem of placement of RNs in cluster-based WSNs is known as NP-Hard. To solve the RN deployment problem with multiple conflicting objectives, we propose various evolutionary algorithms (EAs) like differential evolution (DE), particle swarm optimization (PSO), multi-objective DE (MODE) and multi-objective PSO (MOPSO). First, mathematical formulation of the problem is given. The solution vectors are efficiently encoded. All the objective functions are efficiently derived to evaluate the solution vectors. An extensive simulation is performed over the proposed algorithms. The results are analysed to determine the robust algorithm to be recommended for studied problem. The simulated results claimed that the MODE is comparably better to others for the studied problem. A statistical analysis, analysis of variance (ANOVA) is also performed followed by the least significant difference (LSD) post-hoc analysis.