Optimization for connectivity and coverage issue in target‐based wireless sensor networks using an effective multiobjective hybrid tunicate and salp swarm optimizer

Abstract

SummaryThe two main important issues in designing target‐based wireless sensor networks (WSNs) are coverage and connectivity maximization. In order to tackle the coverage and connectivity problems, we have proposed a hybrid optimization‐based model. Thereby, the target‐based WSNs can include the sensor nodes which are placed based on determining minimum number of selected potential positions. To do that, an optimization approach based on a hybrid tunicate swarm optimizer (TSO) and salp swarm optimizer (SSO) is proposed for coverage and connectivity problems in WSNs. Feature exploitation ability of SSO is improved by TSO operators, because these operators can act as local search operators. Basically, these hybrid algorithm operators are used to derive the fitness function followed with the solution representation step. Initially, the population is generated, and the hybrid tunicate swarm optimizer and salp swarm optimizer (HTSS) algorithm has updated the solutions in the next subsequent step. Ultimately, the nondominated solutions are determined in the final step. The two different scenarios of WSN are used for simulation of this scheme. Simulations have shown its outstanding performance in solving the coverage and connectivity problems in wireless networks.