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一种考虑可能区域和智能搜索相结合的定位算法 被引量:2

A new method for localization based on network coverage and intelligent search
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摘要 提出了一种考虑可能区域和智能搜索相结合的无线传感器网络节点定位算法。该算法首先利用各个锚节点到未知节点的距离确定未知节点的可能区域,然后利用微粒群算法(particle swarm optimization,PSO)搜索出落在可能区域内的符合条件的结果,最后取符合条件的结果的均值作为未知节点的估计位置。实验结果表明,该算法定位精度较高,并且具有很强的鲁棒性,相比于一般的定位算法(如最小二乘法),在测距误差为35%的情况下,其定位精度可以提高49%左右。 A new method for localization based on network coverage and intelligent search (CIL) was presented, First, the distance from each anchor nodes to the unknown node was used to determine the possible region. Second, the positions which meet specific criteria were searched out by a particle swarm optimization algorithm, and the searching results within the possible region were recorded. Finally, the unknown node's localization could be obtained by calculating the average recording results. Experimental results showed that CIL has high positioning accuracy and strong robustness. Compared with normal schemes such as the least square method (LS), the CIL's positioning accuracy could improve 49% when the ranging error was 35%.
出处 《山东大学学报(理学版)》 CAS CSCD 北大核心 2010年第11期27-31,共5页 Journal of Shandong University(Natural Science)
基金 国家自然科学基金资助项目(60802030) 山东省中青年科学家科研奖励基金资助项目(2007BSC01002) 山东省科技攻关计划项目(2007GG2QT01007)
关键词 无线传感器网络 节点定位 微粒群算法 可能区域 wireless sensor networks (WSNs) node localization particle swarm optimization (PSO) possible region
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参考文献12

  • 1丁海斌,曾鹏,梁韡.智能无线传感器网络系统[M].北京:科学出版社,2006.
  • 2MECHITOV K, KIM W, AGHA G. High-frequency distributed sensing for structure monitoring [ C ]// Proceedings of the 1st International Workshop on Networked Sensing Systems (INSS). [ S. 1. ] : [ s. n. ], 2004 : 101- 104.
  • 3SZEWCZYK R, MAINWARING A, POLASTRE J. An analysis of a large scale habitat monitoring application [ C]// Proceedings of the 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys'04). New York: ACM Press, 2004:214-226.
  • 4MECHITOV K, SUNDRESH S, KWON Y. Cooperative tracking with binary-detection sensor networks [ C ]//Proceedings of the 1st ACM International Conference on Embedded Networked Sensor Systems (Sensys). New York: ACM Press, 2003:332-333.
  • 5MAROTI M, SIMON G, LEDECZI A. Shooter localization in urban terrain[J]. Computer, 2004, 37(9) :60-61.
  • 6CHACZKO Z, KLEMPOUS R, NIKODEM J. Method of sensors iocalization in wireless sensor networks [ C ]// Proceedings of the 14th Annual IEEE International Conferenc and Workshops on the Engineering of Computer- Based Systems-Cover. Washington: IEEE Computer Society, 2007 : 145-152.
  • 7KEGEN Y, HEDLEY M, SHARP I, et al. Node positioning in Ad hoc wireless sensor networks [ C ]// Proceed- ings of IEEE International Conference on Industrial Informatics, 2006, 8:641-646.
  • 8SAAD C, BENSLIMANE A, KONIG J. MuR: a distributed preliminary method for location techniques in sensor networks [ C ]//Proceedings of IEEE International Conference on Wireless and Mobile Computing, Networking and Communication (IEEE WiMob 2006 ). Washington: IEEE Computer Society, 2006 : 61-68.
  • 9FU Yaoxian, LIU Haitao, QIN Jiang. The localization of wireless sensor network nodes based on dsss E C ]// Proceedings of IEEE Intemational Conference on Electro/Information Technology. Washington: IEEE Computer Society, 2006: 465-469.
  • 10周书旺,王英龙,郭强,魏诺.基于微粒群算法的无线传感器网络节点定位方法[J].山东大学学报(理学版),2009,44(9):52-55. 被引量:5

二级参考文献12

  • 1段渭军,王建刚,王福豹.无线传感器网络节点定位系统与算法的研究和发展[J].信息与控制,2006,35(2):239-245. 被引量:42
  • 2丁海斌,曾鹏,梁韡.智能无线传感器网络系统[M].北京:科学出版社,2006.
  • 3LANGENDOEN K, REIJERS N. Distributed localization in wireless sensor networks: a quantitative comparison[ J]. Computer Networks, 2003, 43(8) :499-518.
  • 4NICULESCU D, NATH B. Error characteristics of ad hoc positioning systems[C]// Proceedings of MobiHoc'04. Roppongi, Japan: [s.n. ], 2004:20-30.
  • 5AKYILDIZ I F, SU W, SANKARASUBRAMANIAM Y, et al. Wireless sensor networks: a survey [ J ]. Computer Networks, 2002, 38(4) :393-422.
  • 6GAU YUHE, CHU HUNGCHI, JAN RONGHONG. A Weighted multilateration positioning method for wireless sensor networks[ C ]// Proceedings of Workshop on Wireless, Ad Hoc, and Sensor Networks. Taiwan, China: [ s. n. ], 2005 : 3-8.
  • 7KENNEDY J EBERHART R. Particle swarm optimization [ C]// Proceedings IEEE Int Conf on Neural Networks,Perth: [s. n. ], 1995:1942-1948.
  • 8EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]// Proceedings 6th Int Symposium on Micro Machine and Human Science, Nagoya: [ s. n. ], 1995 : 39-43.
  • 9SHI YUHUI, EBERHART R. A modified particle swarm optimizer[C]// Proceedings IEEE Int Conf on Evolutionary Computation,Anchorage: [s. n. ], 1998: 69-73.
  • 10EBERHART R, SHI YUHUI. Particle swarm optimization: Developments, applications and resources [ C ]//Proceedings IEEE Int Conf on Evolutionary Computation, Seoul: [ s. n. ], 2001 : 81-86.

共引文献7

同被引文献22

  • 1王福豹,史龙,任丰原.无线传感器网络中的自身定位系统和算法[J].软件学报,2005,16(5):857-868. 被引量:672
  • 2KULKARNI R V, VENAYAGAMOORTHY G K. Parti- cle swarm optimization inwireless-sensor networks: a brief survey [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 2011, 41 (2) :262-267.
  • 3GOPAKUMAR A, JACOB L. Localization in wireless sensor networks using particle swarm optimization [ C ]// Proceedings of IET International Conference on Wireless, Mobile Multimedia Networks. London :IET, 2008 : 227- 230.
  • 4KULKARNI R V, VENAYAGAMOORTHY G K. Bio- inspired algorithms for autonomous deployment and local- ization of sensor nodes[J]. IEEE Transactions on Sys-tems, Man, and Cybernetics-Part C: Applications and Reviews, 2010, 40(6) :663-675.
  • 5LOW K S, NGUYEN H A, GUO H. A particle swarm optimization approach for the localization of a wireless sensor network[ C ]// Proceedings of IEEE International Symposium on Industrial Electronics. Piscataway: IEEE, 2008 : 1820-1825.
  • 6J Yick, B Mukherjee, D Ghosal. Wireless sensor network survey [ J]. Computer networks, 2008,52(12) :2292-2330.
  • 7Eunchan Kim, Sangho Lee, Chungsan Kim. Long-range Beacons on Sea Surface based 3D-Localization for Underwater Sensor Net- works[ J]. Communications Letters, 2010,14(7) :647-649.
  • 8Vesterstrom, S Jakob, Thomsen. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems [ C ]. Congress on Evolutionary Computation, 2004 : 1980-1987.
  • 9Latiff, N M Abdul, Tsimenidis, C Charalarnpos, Sharif, S Bayan. Performance comparison of optimization algorithms for clustering in wireless sensor networks[ C ]. IEEE Int Conf, Mobile Adhoc and Sensor Systems, 2007 : 1-4.
  • 10J Kennedy, R Eberhart. Particle Swarm Optimization [ C ]. Pro- ceedings IEEE Int Cord" on Neural Networks, 1995:1942-1948.

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