A node placement strategy using Moth flame Optimization algorithm to improve network coverage of WSNs

Abstract

The significant challenges of wireless sensor networks include the connectivity and coverage, which is impacted by the node placement. Accordingly, the cost-effective deployment could be achieved with the optimal sensor node placement in the monitored area. The maximum coverage should be provided by the sensor nodes’ positions with maximized network lifetimes. Researchers aim to achieve an optimal deployment that increases the coverage rate and network lifetime with minimization in energy consumption. Moth-Flame optimization algorithm is a bio-inspired optimization method that is used to solve k-coverage node deployment on target based WSN. However the conventional MFO algorithm suffers from premature convergence and stagnation problems.  In this work, a new approach of MFO with mutation capability - MUMFO has been introduced to balance the exploration and exploitation capability of the traditional MFO and to increase coverage rate and connectivity. The moths are divided into three categories namely ‘good’, ‘average’ and ‘bad’ moths, based on the evaluated fitness values and mutation is performed among these categorized moths. The proposed strategy of MUMFO node deployment strategy has been compared with the existing node deployment strategies PSOIL & EDEM node placement methods. The MUMFO algorithm’s effectiveness has been demonstrated in the simulation results that achieved better data delivery rate, minimal energy consumption rate, and maximum coverage.