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| 一种基于网络密度分簇的移动信标辅助定位方法 | |
| 引用本文： | 赵方,马严,罗海勇,林权,林琳.一种基于网络密度分簇的移动信标辅助定位方法[J].电子与信息学报,2009,31(12):2988-2992. |
| 作者姓名： | 赵方 马严 罗海勇 林权 林琳 |
| 作者单位： | 1. 北京邮电大学软件学院网络技术研究院,北京,100876 2. 中国科学院计算技术研究所,北京,100190 3. 北京航空航天大学软件学院,北京,100083 |
| 基金项目： | 国家高技术研究发展计划，国家自然科学基金(60873244;60973110)资助课题 |
| 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％。 |
| 关 键 词： | 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 |
| 收稿时间： | 2008-11-24 |
| 修稿时间： | 2009-9-10 |
A Mobile Beacon-assisted Node Localization Algorithm Using Network-Density-based Clustering for Wireless Sensor Networks |
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| Zhao Fang,Ma Yan,Luo Hai-yong,Lin Quan,Lin Lin.A Mobile Beacon-assisted Node Localization Algorithm Using Network-Density-based Clustering for Wireless Sensor Networks[J].Journal of Electronics & Information Technology,2009,31(12):2988-2992. | |
| Authors: | Zhao Fang Ma Yan Luo Hai-yong Lin Quan Lin Lin |
| Affiliation: | School of Software Engineering, Researching Acadamy of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; School of Software Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China |
| 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. |
| Keywords: | Wireless sensor networks Mobile beacon-assisted localization Density-based clustering Incremental localization |
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