基于改进布谷鸟搜索算法对水质监测无线传感器部署的优化

为解决传统传感器网络随机部署分布不均的问题,提出采用布谷鸟搜索算法(CS)进行节点部署优化。为改善CS算法的全局优化性能以提升传感器节点部署优化能力,受动量梯度下降法、均方根算法和Adam优化算法的启发,提出Momentum-CS、RMSprop-CS与Adam-CS三种改进算法,对CS算法中的步长控制量和淘汰概率进行优化调整。以网络覆盖率为优化目标,将3种算法用于长宽为100 m水域的水质监测无线传感器节点部署进行优化。仿真结果表明,Adam-CS算法能够在较少迭代次数获取更高的网络覆盖率,达到90.35%,对于指导水环境监测中无线传感器节点部署具有现实意义。

Optimization of wireless sensor network for water quality monitoring based on improved cuckoo search algorithm

In order to solve the problem of uneven distribution of random deployment of traditional sensor networks, cuckoo search algorithm (CS) was proposed to optimize node deployment. In order to improve the global optimization performance of CS algorithm and enhance the deployment optimization ability of sensor nodes, inspired by gradient descent with momentum, root mean square algorithm and Adam optimization algorithm, three improved algorithms, namely Momentum-CS, RMSprop-CS and Adam-CS, were proposed to optimize and adjust the step size control and elimination probability in CS algorithm. Taking network coverage as the optimization target, three algorithms were used to optimize the deployment of water quality monitoring wireless sensor nodes in water area with length and width of 100 meters. Simulation results showed that the Adam-CS algorithm could achieve a higher network coverage of 90.35% in fewer iterations, which was of practical significance for guiding the deployment of wireless sensor nodes in water environment monitoring.