WSN Coverage Optimization based on Improved Sparrow Search Algorithm

In order to solve the problems of node redundancy, low node utilization and low coverage in the coverage of WSN, this article adopts an improved sparrow search algorithm method named ISSA to deal with these problems. Firstly, the good point set population initialization strategy is introduced to replace the random population initialization in the original SSA, so as to make the sparrow population more evenly distributed and increases population diversity. Secondly, improved adaptive learning factor is incorporated into SSA to further update individual position, and then introduce refraction reverse learning strategy to perturb individuals to avoid falling into local optimal, difficult to jump out of. Finally, ISSA is used for dealing with the coverage optimization of WSN. According to the experimental results, ISSA can indeed effectively improve the coverage of WSN, reduce the network node redundancy and the distribution of nodes is therefore more uniform.