模糊化的无线传感节点自适应定位算法  

Adaptive Localization Algorithm Based on Fuzzy Theory in Wireless Sensor Networks

作　　者：李响[1,2] 黄刘生[1,2] 汪炀[1,2] 徐宏力[1,2] 王培[1,2] 

机构地区：[1]中国科学技术大学计算机科学与技术学院,安徽合肥230027 [2]中国科学技术大学苏州研究院,江苏苏州215123

出　　处：《小型微型计算机系统》2010年第6期1058-1062,共5页Journal of Chinese Computer Systems

基　　金：国家"九七三"重点基础研究发展项目(2006CB303006)资助;中科院知识创新工程资助;中国博士后基金项目(20080430776)资助;博士学科点专项科研基金项目(20070358075)资助

摘　　要：节点定位是无线传感网络的研究基础,具有重要的研究价值和研究意义.现有定位算法主要分为测距相关的算法(range-based)与测距无关的算法(range-free)两类.前者通常使用未经处理的、包含环境噪声的测距信号,导致其定位稳定性较差;后者常使用节点间的连通性信息,导致其定位精度相对较低.针对该现状,本文提出一种基于模糊处理的节点自适应定位算法(fuzzy localization,FL).FL算法首先收集有关测距信息,然后对测距信息进行模糊化处理,最后采用自适应算法计算节点位置.FL算法对测距信息的模糊化处理,减少了环境噪声对定位的影响,提高了节点定位的稳定性;同时细化了通讯半径内的距离估计,提高了节点的定位精度;且自适应算法能对模糊化过程进行自适应控制,取得有效模糊化参数.实验结果表明,与DV-Hop算法和Spring算法相比,FL算法减少了约31%和6%的定位误差,且定位稳定性较好.Localization is a fundamental problem in the wireless sensor networks.Current localization algorithms can be mainly divided into two classes：range-based algorithms and range-free algorithms.As the former kind of algorithms mainly uses range information including unpredictable environment interference,the localization results often tend to be unstable.Correspondingly,since the latter kind of algorithms often takes the connectivity information into consideration,the localization results usually become imprecise.To fill the gap between range-based algorithms and range-free algorithms,in this paper we propose an adaptive localization algorithm based on fuzzy theory（fuzzy localization,FL）.By fuzzing the ranging information,FL Algorithm reduces the impaction of environment.With selecting fuzzy parameter adaptively,it gains better stability and precise than traditional method.The simulation results show that our scheme gains 31% and 6% more accurate and stable than DV-Hop and Spring in sparse networks,respectively.

关 键 词：定位算法 模糊化 自适应定位 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]