Energy Saving Coverage Control Study Of Wireless Sensor Networks Based On Particle Swarm Optimization Algorithm

Coverage control is a basic problem in Wireless Sensor Network(WSN). The main content of coverage control is:first is to ensure the network do have certain quality of service. Then, you can optimize it by some technology or protocol. So that it can meet its maximize coverage area. People may get reliable monitoring data and target tracking service. Effective strategies of the coverage control and algorithms can be used to optimize the allocation of resources of WSN, increase the efficiency of the energy usage of network nodes, and improve the perceived quality of service and the overall survival time. How to combine different environmental demands and design a practical strategy for coverage is a significant research field.Researchers have put forward the strategies for reducing energy consumption and prolong the network life cycle from all aspects of the wireless sensor network. These strategies can be divided into four basic categories:sleep scheduling scheme, adjusting the sensing radius of the nodes, choosing the best route, highly effective data fusion system. Based on particle swarm optimization algorithm, this paper studies the network lifetime maximization problem by adjusting the sensing radius and sleeping scheduling two aspects. The main research contents and results are as follows:1. First, studied the influence of the sensing radius of the wireless sensor network coverage. Build network coverage model by MATLAB platform. Then use the particle swarm optimization (PSO) algorithm and society particle swarm optimization (SPSO) algorithm to optimize the network model respectively. Simulation experiment confirms that:first, social particle swarm algorithm’s optimization result is better than particle swarm optimization algorithm in convergence and coverage rate, second, adjust the radius can effectively improve the network coverage quality. For sensing radius have close relationship with coverage performance and energy consumption, this paper proposed a coverage strategy with adjustable sensing radius based on social particle swarm optimization algorithm. The basic idea is: defines the network energy consumption coefficient, according to the node energy consumption coefficient, periodically selecting the working nodes and its sensing radius, makes the target area to be covered with a long time, average energy consumption is small. Simulation experiment results show that the proposed strategy improves the network coverage effectively, reduces the energy consumption, so as to prolong the survival time of network. 2. As random distribution may result in high density and large energy consumption, this paper proposes balanced an energy consumption covering strategy based on multi-objective particle swarm. When selecting the optimal coverage node set, consider the network energy consumption and the coverage rate at the same time. In each cycle, node calculates its sleep probability according to its own energy consumption and their neighbor’s information. Network coverage, the number of working nodes and the energy consumption are as the optimization goal, and then the multi-objective particle swarm optimization algorithm is used to get the optimal coverage solution. Then compared with the classical coverage control algorithm of PEAS and SPAN. Simulation results show that the coverage control strategy can achieve high coverage rate and effectively reduce the energy consumption at the same time. So ensure the network energy balance, keep the network stable operation and prolong survival time.