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山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (3): 23-30.doi: 10.6040/j.issn.1672-3961.1.2015.099

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一种基于RSSI向量的传感器网络定位算法

刘陈1,蔡婷2   

  1. 1. 重庆工程学院电子信息学院, 重庆 400056;2. 重庆邮电大学移通学院, 重庆 401520
  • 收稿日期:2015-05-12 出版日期:2016-06-30 发布日期:2015-05-12
  • 作者简介:刘陈(1983— ),男,重庆人,讲师,主要研究方向为无线传感器与云计算. E-mail:liucschool2014@163.com
  • 基金资助:
    重庆市教育科学技术研究面上资助项目(KJ1402002)

A localization algorithm based on RSSI vector for wireless sensor networks

LIU Chen1, CAI Ting2   

  1. 1.College of Electronic Information, Chongqing Institute of Engineering, Chongqing 400056, China;
    2. College of Mobile Telecommunications, Chongqing University of Posts and Telecommunications, Chongqing 401520, China
  • Received:2015-05-12 Online:2016-06-30 Published:2015-05-12

摘要: 为了降低待定位节点在定位单元的边缘定位误差,设计了一种四边形区域定位算法。首先根据待定位节点在定位单元的不同位置,分别采用定位单元内部定位机制和定位单元外部定位机制,然后引入向量相近度来辅助寻找距离未知节点最近的参考样本点。试验结果表明,该算法可降低50%的定位误差,随着锚节点数的增加,定位精度逐渐增加且最终趋于稳定,解决了待定位节点的定位误差问题。

关键词: 传感器, 中位线, 向量相近度, 精度, RSSI(rceived signal strength indication)测距, 定位机制

Abstract: In order to reduce the edge location error of unknown nodes in the location unit, a quadrilateral region localization algorithm was proposed. Based on the different locations of unknown nodes in the location unit, the algorithm adopted either internal or external location mechanism of the location unit. Then, the vector similarity was introduced to facilitate the search of reference sample nodes closest to unknown nodes. Experimental results demonstrated that the proposed algorithm could reduce up to 50 percent of the location error. With the number of anchor nodes increasing, the location accuracy increased, and the accuracy values tended to be stable. The algorithm could also solve the problem that the location accuracy of unknown nodes.

Key words: localization mechanism, sensor, accuracy, RSSI(rceived signal strength indication)distance, median line, vector similarity

中图分类号: 

  • TP391
[1] IANF Akyildiz, SU Weilian,YOGESH Sankarasubramani, et al. A survey on sensor networks[J].IEEE Communications Magazine, 2002, 40(8):102-114.
[2] RABAEY J, AMMER M J, DA Silva Jr L, et al. Picoradio supports ad hoc ultra-low power wireless networking[C] // Proceedings of the 8th annual international conference on mobile computing and networking(MobCom). SanDiego, California, USA: ACM Press, 2002, 7:42-48.
[3] TIAN He, CHENG Duhuang, BRIAN M, et al. Range-free localization schemes in large scale sensor networks[C] //Proceedings of the 9th annual international conference on mobile computing and networking(MobCom). SanDiego, California, USA:ACM Press, 2003, 9:81-95.
[4] 任丰原,黄海宁,林闯.无线传感器网络[J]. 软件学报2003,14(7):1282-1290. REN Fengyuan, HUANG Haining, LIN Chuang. Wireless sensor networks[J]. Journal of Software, 2003, 14(7):1282-1290.
[5] 工福豹,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. GONG Fubao, SHI Long, REN Fengyuan. Self-localization systems and algorithms for wireless sensor networks[J]. Journal of Software, 2005, 16(5):857-868.
[6] DAI Guilan, ZHAO Chongchong, QIU Yan. A location scheme based on sphere for wireless sensor network in 3D[J]. Acta Electronica Sinica, 2008, 36(7):1297-1303.
[7] AWIDES A, PARK H, SRIVASTAVA M B. The bits and flops of the n-hop multilateration primitive for node localization problems[C] //Proceedings of the 1st ACM Int workshop on wireless sensor networks and application. Atlanta, USA:ACM Press, 2002:112-121.
[8] JIAN Liangxu, XUE Yantang,WANG Cheinlee. A new storage scheme for approximate location queries in object-tracking sensor networks[J].IEEE Transaction on Parallel and Distributed Systems, 2008, 19(2):262-275.
[9] KEVIN Yuen, BEN Liang, BACCHUM Li. A distributed framework for correlated data gathering in sensor networks[J].IEEE Transaction on Vehicular Technology, 2008, 57(1): 578-593.
[10] 赵昭,陈小惠.无线传感器网络中基于RSSI的改进定位算法[J].传感技术学报,2009,22(3):391-394. ZHAO Zhao, CHEN Xiaohui. Based on improvement of RSSI localization algorithm in wireless sensor networks[J]. Journal of Sensors and Actuators, 2009, 22(3):391-394.
[11] FOX D, HIGHTOWER J, LIAO L, et al. Bayesian filtering for location estimation[J].Pervasive Computing, 2003, 6(9):23-24.
[12] LI D, WONG K D, HU Y H, et al. Detection classification and tracking of targets[J].IEEE Signal Processing Mag, 2002, 5(5):17-29.
[13] NICULESCU D, NATH B. Ad hoc positioning system(APS)[J]. IEEE Globecom, 2001, 6(7):2926-2931.
[14] 刘志华,陈嘉兴,陈霄凯.无线传感器网络中序列定位新算法的研究[J].电子学报,2010,38(7):1552-1556. LIU Zhihua, CHEN Jiaxing, CHEN Xiaokai. A new algorithm research of sequence-based localization technology in wireless sensor networks[J]. Journal of Electronics, 2010, 38(7):1552-1556.
[15] YEDAVALLI K, KRISHNAMACHARI B. Sequence-based localization in wireless sensor networks[J].IEEE Transcations on Mobile Computing, 2008, 7(1):81-94.
[16] 朱剑,赵海,徐久强,等.无线传感器网络中的定位模型[J].软件学报,2011,22(7):1612-1625. ZHU Jian, ZHAO Hai, XU Jiuqiang, et al. Localization model in wireless sensor networks[J]. Journal of Software, 2011, 22(7):1612-1625.
[17] 文武松,王璐.基于启发式移动信标的无线传感器网络节点定位[J].软件学报, 2012,23(Supp1.(1)):1-8. WEN Wusong,WANG Lu. Localization in wireless sensor network with heuristic mobile beacons[J].Journal of Software, 2012, 23(Supp1.(1)):1-8.
[18] ELNAHRAWY E, LI X, MARTIN R. The limits of localization using signal strength: a comparative study[C] //Proc of the 1st annual IEEE communications society conf. on sensor and ad hoc communications and networks. Santa Clara, USA: IEEE, 2004:406-414.
[19] WHITEHOUSE K, KARLOF C, CULLER D. A practical evaluation of radio signal strength for ranging-based localization[J]. ACM Sigmobile Mobile Computing and Communications Review, 2007, 11(1):41-52.
[20] HIGHTOWER J, BORRIELLO G. Location systems for ubiquitous computing[J]. Journal of Computer, 2001, 34(8):57-66.
[21] PATWARI N, HERO A, COSTA J. Learning sensor location from signal strength and connectivity[C] //Secure localization and time synchronization for wireless sensor and ad hoc networks. Santa Clara, USA: IEEE, 2007:57-81.
[22] BAHL P, PADMANABHAN V. Radar: an in-building RF-based user location and tracking system[C] //INFOCOM 2000. Tel-Aviv, Israel: IEEE, 2000:775-784.
[23] ROOS T, MYLLYMAKI P, TIRRI H. A statistical modeling approach to location estimation[J]. IEEE Trans on Mobile Computing, 2002, 1(1):59-69.
[24] KRISHNAN P, KRISHNAKUMAR A, JU W, et al. A system for lease: location estimation assisted by stationary emitters for indoor RF wireless networks[C] // Proc of the 23rd annual joint conf. of the IEEE computer and communications societies.Hongkong, China: IEEE, 2004:1001-1011.
[25] RAY S, LAI W, PASCHALIDIS. Deployment optimization of sensor net-based stochastic location-detection systems[C] // Proc of the 24th annual joint conf. of the IEEE computer and communications societies. Miami, USA: IEEE, 2005:2279-2289.
[26] JI Y, BIAZ S, PANDEY S, et al. Ariadne: a dynamic indoor signal map construction and localization system[C] //MobiSys 2006. Uppsala, Sweden: IEEE, 2006:19-22.
[27] VARSHAVSKY A, DE Lara E, HIGHTOWER J, et al.Gsm indoor localization[J]. Pervasive and Mobile Computing, 2007, 3(6):698-720.
[28] YEDAVALLI K, KRISHNAMACHARI B. Sequence-based localization in wireless sensor networks[J]. IEEE Trans on Mobile Computing, 2008, 7(1):81-94.
[29] 宋保业,田国会,周风余.基于CC2431的智能空间定位系统[J].山东大学学报(工学版), 2011,41(1):40-44. SONG Baoye, TIAN Guohui, ZHOU Fengyu. CC2431 based intelligent space locating system[J]. Journal of Shandong University(Engineering Science), 2011, 41(1):40-44.
[30] 曾碧,毛勤.改进的室内三维模糊位置指纹定位算法[J]. 山东大学学报(工学版),2015,45(3):22-27. ZENG Bi, MAO Qin. Improved indoor 3-D fuzzy position fingerprint localization algorithm[J]. Journal of Shandong University(Engineering Science), 2015, 45(3):22-27.
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