Research On Coverage Control Strategy For Wireless Sensor Networks Within Specific Regions

With the rapid development of Wireless Sensor Networks (WSNs),it will gradually become the inseparable part of people's life. The WSNs build a bridge between the physical and logical world and greatly expanded the human cognition of the world.Wireless coverage control strategy is a Critical underlying issue in WSNs research,which purpose is to eliminate blind spots, reduce the redundant nodes in the network and increase the network coverage by controlling the distribution of Wireless Sensor Node(WSN) in the guaranteed quality of service conditions.In recent years,the Particle Swarm Optimization (PSO) algorithm as the representative for the swarm intelligence algorithm has been more and more used to the coverage control strategy. Researchers through a large number of experiments show that the algorithm can realize WSNs layout optimization effectively, but the particles are often caught in the "premature" phenomenon and can't get the optimal solution,when the particle swarm algorithm in the optimization process.Under the influence of optimization search space vector dimension, the computational time is also one of the problems to solve.This paper will research on the control strategy of PSO, through the optimization of algorithms to solve the above problems, improving the effect of coverage.Fristly,we will improves the strategy of linear inertia decreasing type proposed by Shi to strategy of Index weight decreasing type.This method by accelerating particles into the local search, which has higher search efficiency. Simulation shows that this strategy doesn't exacerbate the algorithm "premature" phenomenon, improve the algorithm convergence speed and network coverage and reduces the calculation time.Secondly, in order to optimize the deployment of WSN, a virtual force disturbance index weights diminishing particle swarm optimization algorithm(VFDPSO) which combines virtual force(VF) with particle swarm optimization(PSO) is proposed. On the one hand,When the particle into the local optimal,particle will get rid of the local optimal particle constraint under the influence of external force.This method reduce the time to obtain the optimal solution, improve the convergence speed and solve the problem of premature.On the other hand, under the action of external force,the particles can be extended to other areas and reduced the number of overlapping areas. Simulation analysis show that the above assumptions are confirmed, and further illustrates the algorithm has good effect to optimize coverage by compared with other intelligent swarm algorithm. At lastly, this paper simulates a complex network environment to test the proposed algorithm the capability of adapt to the environment.the simulation results show that the algorithm can be very good to avoid obstacles in the environment, and realize the WSNs layout optimization in the environment.