一种基于网络密度分簇的移动信标辅助定位方法

赵方; 马严; 罗海勇; 林权; 林琳

doi:10.3724/SP.J.1146.2008.01532

一种基于网络密度分簇的移动信标辅助定位方法

doi: 10.3724/SP.J.1146.2008.01532

基金项目:

国家高技术研究发展计划(2006AA10Z253，2007AA12Z321，2009AA011902)和国家自然科学基金(60873244，60772111，60973110)资助课题

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出版历程
- 收稿日期: 2008-11-24
- 修回日期: 2009-09-10
- 刊出日期: 2009-12-19

A Mobile Beacon-assisted Node Localization Algorithm Using Network-Density-based Clustering for Wireless Sensor Networks

摘要

摘要: 现有移动信标辅助定位算法未充分利用网络节点分布信息，存在移动路径过长及信标利用率较低等问题。该文把网络节点分簇、增量定位与移动信标辅助相结合，提出了一种基于网络密度分簇的移动信标辅助定位算法(MBL(ndc))。该算法选择核心密度较大的节点作簇头，采用基于密度可达性的分簇机制把整个网络划分为多个簇内密度相等的簇，并联合使用基于遗传算法的簇头全局路径规划和基于正六边形的簇内局部路径规划方法，得到信标的优化移动路径。当簇头及附近节点完成定位后，升级为信标，采用增量定位方式参与网络其它节点的定位。仿真结果表明，该算法定位精度与基于HILBERT路径的移动信标辅助定位算法相当，而路径长度不到后者的50％。
- 无线传感器网络; 移动信标辅助定位; 基于密度分簇; 增量定位
Abstract: All the current mobile beacon-assisted localization algorithms do not make full use of the practical node distribution information and let the mobile landmark travel the entire network, which causes large path length and low beacon utilization ratio. A novel mobile beacon-assisted node localization algorithm using network-density-based clustering (MBL(ndc)) for wireless sensor networks is presented, which combines node clustering, incremental localization and mobile beacon assisting together. It first selects the cluster heads that has highest core density, and then employs density-reachable method to cluster the network into several branches with the same density, and lastly obtains the optimum trajectory of mobile beacon by combining cluster head path planning using genetic algorithm with in-cluster path planning using hexagon trajectory. After the cluster heads and nearby nodes have completed localization, they become beacons, then cooperate with each other to localize the left unknown nodes in an incremental way. Simulation results demonstrate that the proposed MBL(ndc) algorithm offers comparable localization accuracy as the mobile beacon-assisted localization algorithm with HILBERT trajectory, but with less than 50% path length of the later.

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