摘要
为提高无线传感器网络节点粒子群优化(PSO)定位算法的收敛速度与搜索性能,将惯性权重的非线性调整策略及目标值排序的思想引入其中,从而实现对算法的改进,并将改进后的算法应用于传感器网络节点的定位。最后,通过仿真实验分别比较了在不同的锚节点密度、网络连通度以及测距误差下,该算法与标准粒子群优化算法及最小二乘法的定位结果。结果表明,改进后的算法不仅有效地抑制了测距累计误差,而且提高了收敛速度,该方法用于传感器网络节点的优化定位是可行的。
For improving the convergence rate and search ability of particle swarm optimization（PSO） localization algorithm for wireless sensor networks（WSNs）,the non-linear inertia weight and sorting fitness strategies were applied to improve this localization algorithm for nodes localization.Finally,through simulation,the localication result of this algorithm was compared with the standard particle swarm optimization algorithm,least-squares method in different anchor node desity,connectivity and measurement error.The results show that this improved algorithm can effectively suppress the ranging-error and improve the convergence rate,and using this method to optimize the localization of sensor nodes is feasible.
出处
《计算机科学》
CSCD
北大核心
2012年第12期51-54,共4页
Computer Science
关键词
无线传感器网络
改进粒子群
节点定位
优化
Wireless sensor networks
Improved particle swarm
Nodes localization
Optimization
