Research On Coverage Optimization Algorithms For Wireless Sensor Network

With the continuous development of technology,Wireless Sensor Network(WSN),as a popular research content,has been widely used in the fields of vehicle tracking,forest monitoring,earthquake observation and building monitoring,and water resource monitoring.Wireless sensor network coverage is one of the key technologies of wireless sensor networks.When deploying large-scale sensor networks in actual environments,random deployment is often adopted,resulting in low coverage.Therefore,it is of great research significance that how to adjust the position of the wireless sensor node through the optimization algorithm to make the wireless sensor network achieve better performance index of coverage optimization.This paper aims at the situation where all sensor nodes can be movable and some sensor nodes can be movable in a dynamic wireless sensor network,respectively,in-depth research on wireless sensor network coverage optimization algorithm.The purpose is to maximize the sensory coverage of a certain number of sensor nodes and achieve better coverage optimization performance indicators.The main research results and innovations are as follows:For the research of dynamic wireless sensor network coverage optimization algorithms,the main purpose is to obtain the optimal node position by applying group intelligence algorithms,but due to the relatively insufficient search ability of existing group intelligence algorithms,resulting in uneven node distribution and low coverage.In this paper,the Lévy-embedded Gray Wolf Optimization(LGWO)embedded in Levi 's flight with greater search capability is applied to the wireless sensor network coverage optimization.In order to make the node distribution more uniform,based on the LGWO algorithm,combined with the improved virtual force algorithm,Virtual Force-Lévy-embedded Gray Wolf Optimization,(VFLGWO)is proposed.In addition,most studies lack the consideration of node movement,which leads to a longer average node movement distance.A greedy node matching algorithm is designed and proposed.First,apply VFLGWO to obtain the optimal node position,and then apply the greedy node matching algorithm to obtain a new node matching order.Through simulation experiments,it is verified that the VFLGWO algorithm has an optimization effect with higher coverage and more uniform node distribution in different simulation environments.At the same time,the greedy node matching algorithm can make the average moving distance of the nodes shorter.The results of simulation experiments can prove that the VFLGWO wireless sensor network coverage optimization algorithm proposed in this paper can better improve the dynamic wireless sensor network coverage optimization performance.For the hybrid wireless sensor network coverage optimization algorithm,the coverage hole is repaired mainly through the movable node.In order to adjust the nodes in the process of theswarm intelligence algorithm,the random movement of the nodes does not have a clear goal,which leads to the problem of slow convergence speed.This paper innovatively applies Fuzzy C-means(FCM)clustering algorithm to hybrid wireless sensor network coverage optimization.Fuzzy c-means Coverage Optimization(FCMCO)is proposed.Target points that are not covered by static nodes are clustered.The movable nodes are explicitly moved to the cluster center to complete the coverage hole repair.In addition,although the greedy node matching algorithm proposed in this paper can make the average moving distance of nodes shorter,the greedy algorithm has the disadvantage that it is not easy to obtain the optimal solution.This paper proposes a greedy exchange node matching algorithm.In summary,this paper proposes the Fuzzy c-means Greedy Exchange Coverage Optimization,(FCMGECO).First,the optimal node position is obtained through FCMCO,and then Greedy Exchange(GE)node matching is applied.The algorithm completes node matching and realizes coverage optimization.Simulation experiments were conducted through MATLAB.The experimental results prove that FCMCO has higher coverage and the GE node matching algorithm has a shorter average moving distance of nodes.The results of simulation experiments can prove that the FCMGECO algorithm proposed in this paper can better improve the coverage optimization performance of hybrid wireless sensor networks.