Research On Wireless Sensor Network Coverage Optimization Based On Improved Fruit Fly Optimization Algorithm

Wireless sensor network is a distributed sensor network,which is composed of a large number of fixed or mobile wireless sensor nodes in the form of self-organization and multi-hop transmission.The monitoring data collected by the sensor nodes can be transferred between multiple nodes hop by hop.Wireless sensor networks are widely used in many fields such as military,intelligent transportation,environmental monitoring,medical and health,because they have the advantages of flexible network setup and high quality of network service.In the traditional wireless sensor networks,network coverage and node deployment technology has been a lot of research results,but with the rapid development of network communication technology,people for wireless sensor network needs become larger.The traditional node deployment strategy will appear slow deployment,small coverage and poor quality of service.Wireless sensor network node deployment is divided into the network coverage of mobile sensor nodes and the network coverage of fixed position sensor nodes.Both of these nodes have the same problems.For example,in some areas,the nodes are too dense,resulting in the coverage of the network signal,while some area nodes are too sparse,resulting in the signal strength of the area is not enough to become a network blind area.Therefore,in order to improve the network coverage and network quality of service,usually by increasing the number of nodes to achieve,resulting in some node redundancy,lower resource utilization,network structure becomes complex,the system energy consumption and other issues.In this paper,an improved Fruit Fly Optimization Algorithm is proposed to optimize the coverage of wireless sensor networks in view of the deployment of these two kinds of nodes.At present,there are a variety of intelligent algorithms used in wireless sensor network coverage optimization problem,such as PSO,AFSA,GA and so on.But these algorithms in the wireless sensor network problem,or the complexity of the algorithm is too high,resulting in the calculation speed is too slow,or poor performance of the algorithm,resulting in too low accuracy,or algorithm parameters too much,resulting in complex network model.In order to solve these problems.In order to solve these problems,this paper combines the improved FOA with the two coverage models of the wireless sensor network.Through the comparison test,it is proved that the solution of the wireless sensor network is better than the previous solution,to cover further optimization.this paper proposes an improved FOA,and combines the WSN coverage model to optimize the network coverage.The algorithm has many advantages,such as less computation,short running time,low complexity and high precision.However,the algorithm also has some obvious defects,such as the stability is not high,easy to fall into the local optimum,the latter convergence accuracy,convergence rate slow down,etc.So this paper will improve the FOA to solve the above problems.Compared with other algorithms,the effectiveness and superiority of the improved FOA is verified,and it can greatly improve the performance of the proposed algorithm.Finally the two coverage model combined with the improved FOA with wireless sensor networks,through the contrast test,verify the coverage problem in wireless sensor networks,the solution is better than that of the solutions in the past,to further optimize the network coverage.The main work of this paper focuses on the following points:1.An improved FOA is proposed.:Change the Step of FOA.The search process is divided into several cycles,which can increase the diversity of the search process and greatly reduce the possibility of local convergence.Secondly,the algorithm uses the Sin(x)function in each cycle,so that the step can be changed in the unit cycle T.It can not only ensure that the algorithm has strong global search ability,but also can achieve fast convergence,and can make the algorithm can achieve high precision search in a small range.2.The use of a number of classical test functions of the variable step size algorithm to detect the performance of Change the Step of FOA,reflecting the algorithm on the optimization of the effectiveness and superiority.Through the demonstration and analysis of the experimental results,it is verified that the variable step size algorithm has better search performance and higher stability compared with other intelligent algorithms.3?The mobile sensor node network coverage,first establish the network model,and then combined with the variable step algorithm is proposed to optimize the process of Change the Step of FOA,a simulation experiment was carried out in the simulation environment,reflect the effectiveness and superiority of the optimization method.Through a series of experiments and data display,verified compared with other intelligent algorithms,Change the Step of FOA can more effectively combine mobile node network coverage model,further improve the network coverage,to realize the optimization of network coverage.4.According to the fixed position of the sensor node network coverage,first establish the network model,and then combined with the variable step algorithm is proposed to optimize the process of Change the Step of FOA,a simulation experiment was carried out in the simulation environment,reflect the effectiveness and superiority of the optimization method.Through a series of experiments and data display,verified compared with other intelligent algorithms,Change the Step of FOA can more effectively combine the fixed-location node network coverage model,further improve the network coverage and reduce the energy consumption of the network,to realize the optimization of network coverage.