Research On WSN Coverage Optimization Based On Improved Swarm Intelligence Algorithm

In recent years,with the rapid development of wireless communication technology,wireless sensor network has been widely used in different fields,including forest monitoring,intelligent transportation and image processing.Coverage is an important index to evaluate the performance of wireless sensor networks.On the premise of ensuring the quality of network connection,it is always a hotspot in wireless sensor networks to use the least number of sensor nodes to achieve the maximum coverage.In this thesis,we design a node deployment strategy based on improved Gray Wolf Algorithm(IGWO)to improve the coverage and reduce the number of nodes.In order to solve the problems of local optimization and long running time,an improved sparrow algorithm(ISSA)is proposed for WSN node deployment,which can effectively reduce the coverage hole and accelerate the convergence of the algorithm.The specific content of the study is as follows:(1)Aiming at the problems of uneven distribution and high redundancy in the development of wireless sensor network nodes,an improved gray wolf algorithm(IGWO)is proposed and applied to WSN node deployment.Optimize the model with the coverage of nodes as the optimization factor constructor.In the initialization stage,the good point set method is introduced to optimize the initial position of the gray wolf population to make the population distribution more uniform;In order to better match the GWO search process,the nonlinear convergence factor is used to replace the original linear convergence factor;The dynamic weight strategy is used to simulate the different levels of wolves to the omega wolf leadership,further optimize the algorithm mechanism,and improve the algorithm optimization ability;The t-distribution perturbation is added to the formula in the optimal wolf position to improve the convergence speed of the algorithm.In the experimental section,simulation experiments and result analysis of the improved algorithm are carried out,and the simulation results verify the effectiveness of the improved gray wolf algorithm.(2)Aiming at the problems that the intelligent algorithm used in WSN coverage optimization is prone to local optimization and long iteration time,an Improved Sparrow Search Algorithm(ISSA)is proposed.The good point set method is introduced into the sparrow algorithm to optimize the initial position of the sparrow search population to avoid uneven distribution of the population;Introduce a nonlinear adjustment factor to adjust the ratio of discoverers and followers in the population;When the position of the follower population is updated,the mutation mechanism of Levy’s flight is added to improve the convergence speed and avoid the algorithm from falling into the local optimal value,in turn to improve the correctness of the algorithm optimization.In the experimental simulation section,the convergence performance and coverage performance of the sparrow search algorithm SSA and the improved sparrow search algorithm ISSA are compared and analyzed.The simulation results show that the ISSA can effectively reduce the coverage holes while accelerating the convergence rate and reducing the moving distance of the sensor node.It can provide a better solution for node deployment.