Research On WSN Coverage Optimization Based On Improved Sparrow Search Algorith

In the new era,with the advancement of technology,the research on Wireless Sensor Networks(WSN)has become more and more important.Wireless sensors are the gateway for everything to access the Io T world.Sensor devices deliver the collected data to host terminals or users for processing through intelligent sensing,processing control,wireless communication,etc.The most important technology in wireless sensor network coverage optimization is to solve the network service quality and extend the network survival cycle,where energy consumption balance is one of the influencing factors of network quality,improve the node energy consumption,avoid the early death of network nodes,and form the network coverage hole.Besides,coverage is also one of the key factors of network quality of service,which has received a lot of research and attention from scholars.It is well known that in the field of wireless sensors,wireless sensor nodes are deployed in harsh environments and constrained by the limited battery energy of nodes,which often results in phenomena such as coverage holes or node redundancy,which in turn affects the quality of service of the network and shortens the service life of the network.Therefore,it is of great significance to study how to reasonably deploy nodes with more uniform distribution and low redundancy in the target monitoring area to achieve the maximum coverage effect for WSN coverage optimization.The main research works in this paper are as follows.(1)To find an effective and feasible algorithm for the WSN coverage optimization problem,an algorithm that can combine WSN coverage optimization with intelligent optimization algorithms.In this paper,a WSN optimization algorithm based on Hybrid Strategy Sparrow Search Algorithm(HSSSA)is proposed.The algorithm firstly considers the characteristics of chaotic system and reverse learning strategy,and uses Tent chaotic mapping to initialize the sparrow population to increase the population diversity;then uses reverse learning strategy to generate reverse solutions to expand the search range and improve the global search ability of the algorithm;adds inertia factor to select Levy strategy updates for individual early warning sparrows to improve the local search ability of the algorithm;perturbs the optimal sparrow position with random The local search ability is further improved by randomly perturbing the optimal sparrow position.The stability and feasibility of the improved algorithm HSSSA are tested by bench marking functions.(2)For WSN coverage optimization in two-dimensional plane,a mathematical model is established,and the coverage rate is used as the optimization index to establish the WSN coverage optimization objective function.For the effectiveness of WSN coverage optimization of HSSSA algorithm,two sets of simulation experiments were conducted by selecting monitoring areas of different sizes,one is to test the effect of HSSSA algorithm optimized deployment with random deployment and SSA algorithm optimized deployment before and after,the experimental results show that the coverage of the whole network optimized by HSSSA is about 96.28%,which is improved than the coverage of random node deployment by 12.04%,and 9.97% improvement over the SSA algorithm node deployment coverage.Second,to test the effect of HSSSA algorithm and single policy improved SSA algorithm deployment optimization,the experimental results show that the WSN coverage after HSSSA optimization is improved by 12.11%,8.16% and 2.99%compared to SSA,SSAL and SSARW algorithms,respectively.The two sets of simulation results show that the HSSSA algorithm makes the nodes in the monitoring area more uniformly distributed and the coverage rate is significantly improved.(3)For WSN coverage optimization in 3D space,a spatial three-dimensional coverage mathematical model is established,and the coverage rate is taken as the optimization index,and the volume of the ball formed by the sensing radius of all nodes accounts for the volume of the whole target space as the coverage space.Through a set of simulation experiments,the WSN coverage optimization effects of HSSSA,SSA,SSAL and SSARW are compared,and the simulation experiment results show that the HSSSA coverage optimization makes the nodes disperse a larger spatial range and the nodes cover the largest volume,and the spatial coverage of HSSSA is improved by 2.37%,2.3% and1.41%.