Deployment strategy of wireless sensor network based on reformative sparrow search algorithm

Coverage problem is the most important issue in the design of Wireless Sensor Network (WSN), optimizing regional coverage rate as much as possible is a direct means to improve network sensing performance. In view of this, a node deployment optimization scheme based on Reformative Sparrow Search Algorithm (RSSA) is proposed. Firstly, in the search phase, RSSA improves the ergodicity of the algorithm by introducing the sine cosine guidance mechanism to replace the location update mode of the original algorithm. Secondly, the stagnation disturbance mechanism is added to the algorithm by using the characteristics of Lévy random step size, so that RSSA has stronger ability to resist local extremum. At the same time, a more practical probability perception model is used to detect the coverage state of nodes, and a better node set is compared and replaced in the iterative update process, so as to improve the regional coverage. In order to verify the optimization effect of the reformative algorithm, six groups of general benchmark functions are used to test the performance of RSSA, and compared with three different algorithms. The results show that RSSA has good optimization performance. Finally, RSSA is applied to two groups of WSN node deployment optimization examples, and the coverage optimization algorithms in different literatures are compared. Using the proposed algorithm RSSA to optimize node deployment can achieve a maximum coverage of 99.99%, and make the nodes in the region present a uniform distribution. While ensuring high coverage requirements, fewer nodes are used, which reduces node redundancy and reduces the deployment cost of the overall network system.