基于水波优化算法的无线传感器网络覆盖研究

Wireless sensor network coverage based on water wave optimization algorithm

为了提高无线传感器网络(WSN)覆盖的有效性,提高传感节点对目标区域的覆盖率,采用水波优化(WWO)算法对传感节点部署坐标进行优化。首先,根据区域像素点及目标节点数量初始化传感节点坐标集,根据传感节点坐标和目标节点坐标、区域像素大小计算区域覆盖率;然后,将区域覆盖率作为适应度函数,建立WWO算法模型,将初始传感节点集作为WWO输入集,通过水波的传播、折射和碎波操作不断更新水波位置,同时求解各水波的适应度值;最后,选择适应度值最高的水波个体作为传感节点分布的最优解,当区域覆盖率或水波更新迭代次数达到阈值时,输出最优水波个体。分别采用蚁群、人工鱼群、粒子群优化算法和该文算法进行覆盖性能仿真。在覆盖率方面,该文算法最优,达到稳定时可以获得约95%的区域覆盖率,粒子群次之,蚁群算法最差。在收敛性能方面,蚁群算法最快,该文算法次之。

In order to improve the coverage efficiency of wireless sensor network(WSN)and the coverage rate of sensor nodes in the target area,water wave optimization(WWO)algorithm is used to optimize the deployment coordinates of sensor nodes.Firstly,the sensor nodes are initialized according to the number of pixels and target nodes,and the area coverage is calculated according to the coordinates of sensor nodes and target nodes and the pixel size of the area;then,taking the area coverage as the fitness function,a model of WWO algorithm is established,taking the initial sensor node set as the input set of WWO algorithm,updating the position of the water wave continuously based on wave propagation,refraction and breaking operation,solving the fitness value of each water wave at the same time;finally,the water wave individual with the highest fitness value is selected as the optimal solution of sensor node distribution.When the regional coverage or the number of water wave update iterations reaches the threshold,the optimal water wave individual is output.Ant colony,artificial fish swarm,particle swarm optimization and the algorithm proposed here are used to simulate the coverage performance.In terms of coverage,the algorithm proposed here is the best.When it reaches stability,it can obtain about 95%regional coverage,followed by particle swarm optimization,and ant colony algorithm is the worst.In terms of convergence performance,ant colony algorithm is the fastest,followed by the algorithm proposed here.