Research On The Application Of Swarm Intelligence Algorithm In WSN Spectrum Allocation And Coverage Optimization

With the development of information technology and communication network,as the core technology of the perception layer of the Internet of Things system,wireless sensor network technology has gradually attracted the attention of scholars from all over the world,and the related research of Wireless Sensor Networks(WSN)has also been paid more and more attention.However,the communication and limited spectrum resource in sensor networks are increasing demanded.It‘s very important to research on efficient and intelligent spectrum allocation algorithm for reducing channel interference effectively,improving spectrum utilization and reducing the nodes' energy consumption.In addition,coverage is also an important performance index to measure WSN.How to use quantitative sensors to cover a monitoring area extensively and make nodes evenly deployed in WSN are worth to study.This paper focuses on the spectrum allocation and coverage optimization of WSN.The research work of this paper is as follows:(1)On the basis of data transmission characteristics of WSN,the energy consumption model is established.Combined with cognitive radio technology,an improved grey Wolf optimization algorithm is applied to the spectrum allocation scheme.The algorithm uses nonlinear convergence factor to coordinate the global search and local exploration capabilities of the algorithm.Secondly,a weight factor constituted by the proportion of fitness value is used to adjust the position update process dynamically.Then,a new dynamic transfer function is used to map positions into binary space.Finally,in order to maximize the residual energy of the network,the spectrum allocation scheme is simulated experimentally.(2)Aiming at maximizing network coverage and improving coverage uniformity,a sensor network coverage optimization model is established,which is combined with an improved whale optimization algorithm to solve the optimization problem.Firstly,the improved algorithm adopts an improved position update method based on step size.Secondly,it uses a control switch to control the hybrid mutation for expanding the ability of exploring search space.Finally,the centroid opposition-based learning is performed to help the algorithm jump out of the local optimum.The study shows that,compared with the comparison algorithm,the improved gray wolf optimization algorithm proposed in this paper can maximize the residual energy of the sensor network during spectrum allocation,and the improved whale optimization algorithm in this paper can better improve the coverage and deployment uniformity of WSN.