基于优化的布谷鸟算法的无线传感节点定位研究

Wireless Sensor Node Localization Based on Optimized Cuckoo Search

针对无线传感网络的DV-HOP定位算法存在误差大的缺点,提出了使用优化的布谷鸟算法的用于节点定位。首先,阐述了DV-HOP算法节点定位中存在的不足,其次,对布谷鸟算法在种群初始化使用伪反向学习策略,提高了种群多样性;在步长方面引入指引因子概念,仿真实验中,与布谷鸟算法在不同的基准测试函数中对比取得了较好的效果,在节点定位的节点能耗、测距误差和锚节点密度方面都取得了较好的效果。

Aiming at the disadvantage of large error in DV-HOP localization algorithm of wireless sensor network, an optimized cuckoo search algorithm is proposed for node localization.Firstly, the shortcomings of DV-HOP algorithm in node location are explained. Secondly, the pseudo-reverse learning strategy is used for the cuckoo search algorithm in population initialization to improve the population diversity;The concept of guide factor is introduced in step size. In the simulation experiment, compared with the cuckoo search algorithm in different benchmark test functions, it has achieved better results. Good results are obtained in node energy consumption, distance measurement error and anchor node density of node positioning.