无线传感网络移动节点位置并行微粒群优化策略

Parallel Particle Swarm Optimization Based Mobile Sensor Node Deployment in Wireless Sensor Networks

网络节点位置优化是无线传感网络研究的核心问题之一.无线传感网络通常由固定节点和少量移动节点构成,传统的虚拟力导向算法无法解决固定节点对移动节点优化的约束.该文针对这一问题,提出了基于并行微粒群算法的优化策略.微粒群算法具有适于解决连续空间多维函数优化问题、能快速收敛至全局最优解的特点.并行框架提高了算法的运行效率,降低了算法的运算复杂度,使算法能够满足无线传感网络的需求.通过并行微粒群算法搜索不同状态下无线传感节点的最优位置,使无线传感网络能够利用移动节点实现网络结构的动态重组,最大化网络覆盖范围,提高网络测量可靠性.实验证明,并行微粒群优化策略能快速有效地实现无线传感网络移动节点位置优化.

Sensor node deployment is one of the key topics addressed in the researches of wireless sensor networks （WSNs）. WSNs always consist of stationary and mobile sensor nodes. Virtual force （VF） algorithm can not conquer the impact of stationary sensor nodes because force exerted by stationary sensor nodes will fetter the movements of mobile sensor nodes, which will strongly deteriorate its performance. This paper proposes a self-organizing technique for enhancing the coverage of WSNs, which is so-called parallel particle swarm optimization （PPSO）. PSO is an outstanding algorithm for solving multi-dimension function optimization in continuous space and has a series of advantages, such as, high-speed regional convergence and efficient global searching ability. In the proposed algorithm, PSO is adopted in a parallel mechanism to optimize the deployment of mobile sensor nodes. Because of the parallel mechanism, PPSO can be successfully used in WSNs and effectively achieve global searching for optimal strategy of mobile sensor nodes deployment. Simulation results demonstrate that the proposed PPSO has better performance on regional convergence and global searching than VF algorithm and can implement sensor deployment more efficiently and rapidly.