The Research Of Coverage Optimization In Wireless Sensor Network Based On Swarm Intelligent Algorithm

Coverage optimization is one of the basic problems in wireless sensor networks(WSN);it mainly reflects the quality of service in WSN,in real situation,the initial random sensor deployment may lead to the problem of coverage hole and redundancy.Therefore,how to optimize the sensor deployment to achieve the maximum coverage rate and small redundancy is widely discussed.The fast development of swarm intelligence algorithms in recent years has promoted its broad use in coverage optimization in WSN.Swarm intelligence algorithms are inspired from biological phenomenon in the nature,besides,swarm intelligence algorithms and dynamic sensor deployment both belong to the optimization problem.Therefore,those algorithms can solve the coverage problem in WSN very well.This paper we study the coverage optimization in WSN based on swarm intelligent algorithm,and proposed two new strategies based on swarm intelligence algorithm for sovling the coverage optimization problem.(1)We presented a novel sensor deployment scheme based on Fruit Fly Optimization Algorithm(FOA)to improve the coverage rate.Each fruit fly represents a solution for sensor deployment independently,and they are given the random direction and distance for finding food using osphresis.Then we find out the fruit fly with the highest smell concentration judgment value from the fruit fly group and keep its position,and then the fruit fly group will fly towards that position by using their sensitive vision.We have done simulations both in the ideal and obstacle areas,FOA-based sensor deployment is compared with the classic standard Particle Swarm Optimization Algorithm(PSO)and the novel Glowworm Swarm Optimization Algorithm(GSO),simulation results show the effectiveness of the proposed approach.(2)In this paper we proposed a hybrid algorithm which is combined with PSO and GSO,denoted as PGSO.The hybrid algorithm not only has good global search ability of PSO,but also has strong local search ability of GSO,it can converge to global optimal solution quickly.We have done experimental simulations when there are different number of nodes.Simulation results demonstrate that the PGSO can improve the area coverage rate and speed up the convergence speed effectively.