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Volume 31 Issue 12
Dec.  2010
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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. doi: 10.3724/SP.J.1146.2008.01532
Citation: 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. doi: 10.3724/SP.J.1146.2008.01532

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

doi: 10.3724/SP.J.1146.2008.01532
  • Received Date: 2008-11-24
  • Rev Recd Date: 2009-09-10
  • Publish Date: 2009-12-19
  • 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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  • Sichitiu M L and Ramadurai V. Localization of wirelesssensor networks with a mobile beacon. Proc. of the IEEE Int'lConf. on Mobile Ad-hoc and Sensor Systems. Fort Lauderdale,Florida, USA, October 24-27, 2004: 174-183.[2]Ssu K F, Ou C H, and Jiau H C. Localization with mobileanchor points in wireless sensor networks[J].IEEE Transactionson Vehicular Technology.2005, 54(3):1187-1197[3]Xia Zhen-jie and Chen Chang-jia. A localization scheme withmobile beacon for wireless sensor networks. Proc. of 6thInternational Conference on ITS Telecommunications.Chengdu, China, June 21-23, 2006: 1017-1020.[4]Kim Kyunghwi and Lee Wonjun. MBAL: A mobilebeacon-assisted localization scheme for wireless sensornetworks. Proc. of 16th International Conference onComputer Communications and Networks. Honolulu, Hawaii,USA, August, 13-16, 2007: 57-62.Lee Sangho, Kim Eunchan, and Kim Chungsan, et al..Localization with a mobile beacon based on geometricconstraints in wireless sensor networks. Proc. of 3rdInternational Conference on Intelligent Sensors, SensorNetworks and Information. Melbourne, Australia, December3-6, 2007: 61-65.Huang R and Zaruba G V. Static path planning for mobilebeacons to localize sensor networks. Proc. of 5th IEEEInternational Conference on Pervasive Computing andCommunications Workshops. White Plains, New York, March19-23, 2007: 323-330.[5]Koutsonikolas D, Das S M, and Hu Y C. Path planning ofmobile landmarks for localization in wireless sensor networks[J].Computer Communications.2007, 30(13):2577-2592[6]Bahi J M, Makhoul A, and Mostefaoui A. Localization andcoverage for high density sensor networks. ComputerCommunications, 2008, 31(4): 770-781.[7]Ester M, Kriegel H P, and Sander J, et al.. A density-basedalgorithm for discovering clusters in large spatial databaseswith noise. Proc. of 2nd Int. Conf. on Knowledge Discoveryand Data Mining. Portland, Oregon, USA, 1996: 226-231.[8]Shang Y.[J].Ruml W, and Zhang Y, et al.. Localization frommere connectivity. Proc. of the 4th ACM Intl Symp. onMobile Ad hoc Networking Computing. Annapolis,Maryland, USA, June 1-.2003,:-[9]Reino V and Andreas S. TASC: topology adaptive spatialclustering for sensor networks. Proc. of the IEEE Int'l Conf.on Mobile Ad-hoc and Sensor Systems. Washington, DC,November 7-10, 2005, 10 pp.-614.-ISBN: 0-7803-9465-8.[10]Grefenstelle J J, Gopal R, and Rosmaita B, et al.. Geneticalgorithms for the traveling salesman. Proc. of InternationalConference of genetic algorithm and their applications.Carnegie-Mellon University, Pittsburgh, Pa, USA, July 24-26,1985: 359-371.
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