Research On Path Coverage Of Intelligent Algorithms In Wireless Sensor Networks

Optimization problems exist widely in engineering and other fields,and have high research value.In order to solve the optimization problem,a series of optimization algorithms are proposed.One kind of optimization algorithm is called intelligent optimization algorithm.This kind of algorithm is inspired by the social behavior of biological groups in the nature,imitates the group division of work and cooperation to solve the problem,and achieves better results after being applied to the actual problems.Wireless sensor network coverage is a kind of optimization problem.Firstly,we need to define quantifiable objectives,which can be roughly divided into point,fence and area coverage according to different objectives.Assuming that there are several paths,how to allocate the location and number of detectors reasonably to ensure higher path coverage and lower detector cost is the core issue of this paper.Aiming at the problems to be studied,this paper studies the path coverage of wireless sensor network and the particle swarm optimization(PSO)algorithm in intelligent algorithm.Firstly,the research progress of wireless sensor network(WSN)and intelligent algorithm,especially particle swarm optimization(PSO)is introduced.Aiming at the problems of PSO,two improved algorithms are designed.Finally,two improved algorithms are applied to the path coverage of WSN.In the first scheme,particle position is used to calculate the inertia weight,quantum particle swarm optimization(QPSO)and chaos random number generator are introduced to generate the random number.The three aspects improve the particle swarm optimization algorithm,which is called quantum particle swarm optimization(AWCQPSO)based on adaptive inertia weight and chaos random number generator.In the second scheme,the selection mutation crossover operation in the differential evolution algorithm is used to improve the probability that the poor particles in the population can find a better solution and the better particles can jump out of the local optimum,and different mutation methods are used for the two operations.This method is called the difference particle swarm algorithm(AMDEPSO)with multiple mutation strategies.When applied to the path coverage problem of wireless sensor networks,this paper also proposes two ways to express the rationality of path expression.One is to select the maximum dimension of the path,divide it evenly in this dimension,get the coordinates of the path in this dimension,then calculate the coordinates of other dimensions of the path according to the path coordinates in this dimension,and finally get the coordinates of the discrete points of the path.The other is to randomly select a dimension and divide it evenly to get a series of discrete points,combine these discrete points to get a series of curves,calculate the tortuosity and average tortuosity of each section of these curves,then traverse these curves and judge the relationship between the tortuosity and average tortuosity of each section of curves.Aiming at the two optimization objectives of higher coverage and lower detector cost,the current scheme is further improved,and a particle swarm optimization algorithm based on detector optimization is proposed.At the end of each iteration,the algorithm randomly selects a dimension of the optimal particle to delete and compares whether the fitness of the particle after deleting the dimension is not affected.If so,the deletion operation is retained.The experimental results show that the above three strategies have good performance in solving the path coverage problem of wireless sensor networks.The AWCQPSO algorithm has achieved good results in all cases.The AMDEPSO algorithm with multiple mutation strategies has better performance in solving the coverage problem under the complex path.The proposed detector optimization method can well balance the coverage and detector cost,and can meet the actual requirements and cost control for the coverage of wireless sensor network,AWCQPSO algorithm and AMDEPSO.Compared with the traditional optimization algorithm,the PSO algorithm improves 16% and 10% respectively.The method proposed in this paper will expand the ideas for urban traffic monitoring,underwater vehicle navigation path monitoring and other issues,and can solve practical problems,which is conducive to the development of high-tech information technology.