Research On WSN Coverage Optimization Based On Improved Quantum Behaved Particle Swarm

Wireless sensor network integrates sensor technology,MEMS technology,embedded computer technology and communication technology,which is composed of a large number of inexpensive sensor nodes.Because of its high monitoring accuracy,high fault tolerance,large coverage area,remote monitoring and other advantages,WSN is widely used in military,agriculture and environmental testing,medical health,space exploration and other fields.Because the sensing range of each sensor is limited,in order to ensure that the entire area is within the scope of monitoring,we need to determine the appropriate coverage strategy to maximize network coverage.In addition,WSN networks are often deployed in harsh and even very dangerous environments,node's power replacement,battery charging and other work are often unable to carry out.Therefore,in the study of WSN coverage problem,in addition to maximizing the network coverage as the optimization goal,but also need to explore the relationship between the node's range of perception and energy consumption,in order to achieve energy saving WSN optimal deployment.This thesis focuses on the WSN energy saving and optimization deployment,based on the particle swarm optimization and the quantum behaved particle swarm optimization model,the WSN coverage optimization model is established,and an improved quantum behaved particle swarm WSN algorithm is proposed.It can effectively reduce the energy consumption of nodes by dynamically adjusting the sensing radius,and achieve more than 90% of the network coverage.The structure of the thesis is as follows: The first chapter introduces the concept of WSN network,the research background and significance,and the research status.The second chapter introduces the related issues of WSN deployment and the typical network coverage algorithm.Chapter 3 studies the application of the basic particle swarm optimization algorithm in WSN deployment,puts forward a kind of improved quantum particle swarm network deployment algorithm,analyzes the influence of perceived radius for WSN coverage performance,by dynamically adjusting the sensing radius,optimizes network deployment from the perspective of coverage and energy consumption.The fourth chapter carries on the simulation and analysis of the performance of the proposed improved quantum particle swarm algorithm WSN deployment.The conclusion part summarizes the full text,points out the deficiency of this thesis,and prospects for its development direction.The third chapter and the fourth chapter are the key point of this thesis.In Chapter 3,taking into account the particle swarm optimization algorithm cannot guarantee the global convergence,quantum behaved particle swarm optimization algorithm has strong global optimization capability,but regional repeat coverage rate of the quantum behaved particle swarm optimization is higher,so the quantum behaved particle swarm optimization algorithm is improved: in the position evolution equation of the quantum behaved particle swarm optimization algorithm,two influence factors are introduced,which are "quasi-gravitation" and "quasi-coulomb force".so it can reasonably adjust the distance between sensor node,achieve rapid optimization of the same time to reduce the repetition rate of the region.In addition,because of the difference of each sensor node sensing radius,energy consumption is also different,by dynamically adjusting the sensing radius can make consumption of each sensor node energy tend to minimize,achieve optimization and energy saving of WSN coverage.The fourth chapter is the performance simulation of the algorithm.The results show that improved quantum behaved particle swarm optimization algorithm based on WSN are better than the particle swarm algorithm and quantum behaved particle swarm optimization algorithm in terms of coverage rate and convergence speed.At the same time,the algorithm has certain advantages in reducing the energy consumption of the network.