Research On WSN Coverage Optimization Algorithm For 2D And 3D Complex Deployment Environment

With the development of 5G and the Internet of Things,wireless sensor networks(WSN)have been widely used in medical health,environmental monitoring and industrial fields,and coverage optimization is one of the most basic problems of WSN.In addition,network energy consumption and network secondary deployment costs are also key points that cannot be ignored in network deployment.This paper discusses the optimization problem of WSN coverage in the complex deployment environment of two-dimensional(2D)plane and three-dimensional(3D)surface,and designs corresponding deployment algorithms respectively.For the problem of deploying the network in a 2D plane,according to whether the network deployment considers the node energy consumption,it can be divided into two deployment scenarios: urban and forest.Based on the flower pollination algorithm(FPA),two improved FPA are proposed for WSN deployment in two scenarios.In the 3D surface deployment,based on the grey wolf optimizer(GWO),an enhanced grey wolf optimizer(EGWO)is proposed to optimize the network coverage.In this regard,the main innovations and results of this paper are as follows:(1)In the optimization of WSN deployment in a 2D plane,the network deployment of this paper is to deploy multiple batches of heterogeneous nodes in the monitoring area with obstacles.Urban network deployment only needs to maximize network coverage,while in a forest deployment environment,in addition to network coverage,it is also necessary to consider minimizing network energy consumption and secondary deployment costs.To optimize these two deployment models,an improved flower pollination algorithm(IFPA)and a non-dominated sorting multi-objective flower pollination algorithm(NSMOFPA)based on FPA are proposed respectively.First of all,in IFPA,to improve the slow convergence speed and insufficient precision of the original algorithm,a nonlinear convergence factor and a greedy crossover strategy were designed respectively.Secondly,in NSMOFPA,in order to solve the global pollination problem of the algorithm,external storage strategies and leader strategies were introduced respectively.Finally,IFPA and NSMOFPA are applied to urban network deployment and forest network deployment respectively.At the same time,it is compared with several existing well-known network coverage optimization algorithms to verify the effectiveness and superiority of the proposed algorithms.In addition,the network connectivity judgment and sensor node mobility scheme planning are added to the experiment.Simulation results show that IFPA can get higher network coverage and save network deployment costs.Besides,it is verified that the optimization effect of NSMOFPA is outstanding,and it can provide a better solution for WSN deployment.(2)To solve the problem of WSN coverage optimization on 3D surfaces,this paper proposes a coverage optimization algorithm and supplements some techniques used by WSN in 3D surface deployment.First of all,this paper perfects the method of judging the perceived blind zone.At the same time,it proposes a method to calculate the area of network coverage by combining grid and integral.Secondly,on the basis of GWO,the EGWO which divides the grey wolf population into two parts is proposed to optimize the network coverage model.Finally,EGWO is compared with several well-known WSN deployment algorithms in simple and complex surface environments to verify the advantages and uniqueness of the algorithm.Simulation results show that compared with other deployment algorithms,EGWO can not only improve the network coverage of WSN,but also increase the possibility of network connectivity,that is,it can provide a better deployment scheme.