无线传感器网络定位算法研究
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摘要
本文研究内容为无线传感器网络的分布式无测距依赖定位算法。
无线传感器网络由大量具有感知、计算和无线通信能力的廉价传感器节点以自组织方式构成。网络布线多采用空投抛撒,节点位置未知。但节点观测信息一般要求附带位置参数,因此节点自定位算法是无线传感器网络首先要解决的关键技术之一。分布式无测距依赖算法不需要网络计算中心和节点硬件测距能力,成本低、鲁棒性好、定位精度基本满足需要,更有实用价值。目前代表性算法只有四种,其中DV-Hop算法综合性能最好,它用两次泛洪法通信量达到33%定位精度。
本文介绍了研究背景和主流算法；分析了分布式无测距依赖定位算法原理,指出定位误差根本原因是量化误差；提出了四种新算法,理论分析并通过OPNET仿真证实,四种新算法以近一次泛洪法的通信量可以获得高于DV-Hop算法16-20个百分点的定位精度,最高可达13%。
本文的主要研究成果如下：
1.分析分布式无测距依赖定位算法原理,构建理论模型。推导计算公式：平均连通度与节点数、无线射程平方成正比,和测试区域面积成反比。推导估算公式：通信量和锚节点数、节点数成正比。分析定位精度：跳数是用无线射程对节点间距量化后的量化值,量化误差是以跳数为基础的定位算法产生定位误差的根本原因。
2. LSHop算法,综合指标良好,可以全面替代DV-Hop算法。折合跳数降低量化误差,与折合跳距结合,可以提高测距精度。迭代三(多)边法,充分利用冗余信息,提高定位精度。记忆泛洪法,避免转发不参与定位计算的无用消息,减少通信量。仿真证实,可以用DV-Hop算法52.0%通信量,达到15%定位精度。
3. Cluster算法,抗不可靠锚节点和路径影响。三边比例法,知道三点之间距离比例值(跳数)和其中两点(锚节点)位置,可计算出第三点(未知节点)一对候选共轭位置。简单密度聚类法,用聚类半径内候选位置数作为密度来确定相似类,用类质心定位,排除不可靠网络参数,提高定位精度。选举泛洪法,利用最短路径生成树获取聚类定位所需信息,减少通信量。仿真证实,聚类半径等于无线射程时算法定位精度最好,建议作为经验值使用。该条件下,可以用DV-Hop算法52.3%通信量,达到17%定位精度。
4. HopScale算法,抗节点失效和位移,可以应用于移动锚节点场景。比例定位法,知道节点到锚节点距离比例和锚节点位置,即可实现定位。定义邻域(节点无线覆盖区域)内所有节点到同一锚节点的跳数为邻域值,折半查找法可以用实测获得的邻域值计算出梯度,梯度可以消除量化误差,是更好的距离比例值。邻域泛洪法,基于一次普通泛洪法,不增加通信量即可实测获得邻域值。仿真证实,可以用DV-Hop算法50.0%通信量,达到16%定位精度。
5. Fuzzy算法,定位精度高,可以用较小改造代价提高已有算法性能。以已有算法粗测位置作为模糊聚类核心,去共轭干扰并确定候选位置概率分布统计参数。用候选位置正态分布概率密度作为模糊隶属度加权定位,降低不可靠锚节点和路径影响。梯度泛洪法,对已有算法增加附加通信阶段,较小改造即可获得邻域值,拟合曲线可以简化邻域值到梯度的计算。仿真证实,可以用DV-Hop算法54.4%通信量,达到13%定位精度
This paper studied the distributed range-free localization algorithms for WSN (Wireless Sensor Networks).
WSN is an ad hoc network composed of many cheap nodes with capability of detections, calculations and wireless communications. Nodes' locations are usually random and unknown. Unfortunately, observation informations without location parameters are mostly useless. Therefore, node self-localization algorithm, especially distributed range-free one leading to low cost and well robustness, is one of WSN's key techniques. There are four major algorithms. DV-Hop has the best performance with precision 33% and traffic twice floods.
This paper discussed the localization principle and quantization error, proposed four new algorithms. Simulations by OPNET showed that the precisions are 16-20 percentages higher than DV-Hop's and the traffics are near once flood.
The main research results are as follows:
1. From analysis of distributed range-free localization algorithms' principle, the paper constructed theoretical models, and derived average connectivity formula and traffic estimate formula.Theoretical research showed that quantization error, which comes from using the radio range to quantize the distance, is the fundamental reason for localization error.
2. LSHop has the good integrated performance and may instead of DV-Hop. Equivalent hop counts and AHS (Average Hop Size) can reduce the impact of quantization error. Iterative Positioning strategy promotes precision by redundant informations. Memorial Flood avoids forwarding useless packets. Simulations showed that precision can be increased to approach to 15% and traffic can be decreased to 52.0% of DV-Hop's.
3. Cluster can self-exclude unreliable anchors or paths. Trilateral Proportion strategy calculates a pair of conjugate candidate positions from two points'positions and trilateral proportions. Simple Density Clustering strategy defines the number of candidates located within the clustering radius as density and uses centroid of the densest cluster as the node's location. Vote Flood avoids twice floods by the shortest path tree. Precision can be increased to close to 17% with 52.3% traffic of DV-Hop's, from the simulations.
4. HopScale has only one communication stage and adapts to the mobile anchors scenarios. Proportion Localization strategy can locate nodes without AHS calculation needed. Neighborhood Flood can keep the same process of a normal flood and obtain the Neighborhood Value. Gradient is a better distance proportion and may be calculated from Neighborhood Value by BinarySearch. Simulation showed that precision can be increased to about 16% with once flood traffic.
5. Fuzzy has the highest precision. Using the result of other existing algorithms as the clustering core and the statistics of distribution as the fuzzy membership grades, nodes may localize bypassing clustering. Gradient Flood simplifies the gradient calculation by Fitting Curve and avoids big changes to existing algorithms. Simulation showed that precision can be increased to near 13%, higher than that of all similar algorithms including the others put forward in this paper.
无线传感器网络由大量具有感知、计算和无线通信能力的廉价传感器节点以自组织方式构成。网络布线多采用空投抛撒,节点位置未知。但节点观测信息一般要求附带位置参数,因此节点自定位算法是无线传感器网络首先要解决的关键技术之一。分布式无测距依赖算法不需要网络计算中心和节点硬件测距能力,成本低、鲁棒性好、定位精度基本满足需要,更有实用价值。目前代表性算法只有四种,其中DV-Hop算法综合性能最好,它用两次泛洪法通信量达到33%定位精度。
本文介绍了研究背景和主流算法；分析了分布式无测距依赖定位算法原理,指出定位误差根本原因是量化误差；提出了四种新算法,理论分析并通过OPNET仿真证实,四种新算法以近一次泛洪法的通信量可以获得高于DV-Hop算法16-20个百分点的定位精度,最高可达13%。
本文的主要研究成果如下：
1.分析分布式无测距依赖定位算法原理,构建理论模型。推导计算公式：平均连通度与节点数、无线射程平方成正比,和测试区域面积成反比。推导估算公式：通信量和锚节点数、节点数成正比。分析定位精度：跳数是用无线射程对节点间距量化后的量化值,量化误差是以跳数为基础的定位算法产生定位误差的根本原因。
2. LSHop算法,综合指标良好,可以全面替代DV-Hop算法。折合跳数降低量化误差,与折合跳距结合,可以提高测距精度。迭代三(多)边法,充分利用冗余信息,提高定位精度。记忆泛洪法,避免转发不参与定位计算的无用消息,减少通信量。仿真证实,可以用DV-Hop算法52.0%通信量,达到15%定位精度。
3. Cluster算法,抗不可靠锚节点和路径影响。三边比例法,知道三点之间距离比例值(跳数)和其中两点(锚节点)位置,可计算出第三点(未知节点)一对候选共轭位置。简单密度聚类法,用聚类半径内候选位置数作为密度来确定相似类,用类质心定位,排除不可靠网络参数,提高定位精度。选举泛洪法,利用最短路径生成树获取聚类定位所需信息,减少通信量。仿真证实,聚类半径等于无线射程时算法定位精度最好,建议作为经验值使用。该条件下,可以用DV-Hop算法52.3%通信量,达到17%定位精度。
4. HopScale算法,抗节点失效和位移,可以应用于移动锚节点场景。比例定位法,知道节点到锚节点距离比例和锚节点位置,即可实现定位。定义邻域(节点无线覆盖区域)内所有节点到同一锚节点的跳数为邻域值,折半查找法可以用实测获得的邻域值计算出梯度,梯度可以消除量化误差,是更好的距离比例值。邻域泛洪法,基于一次普通泛洪法,不增加通信量即可实测获得邻域值。仿真证实,可以用DV-Hop算法50.0%通信量,达到16%定位精度。
5. Fuzzy算法,定位精度高,可以用较小改造代价提高已有算法性能。以已有算法粗测位置作为模糊聚类核心,去共轭干扰并确定候选位置概率分布统计参数。用候选位置正态分布概率密度作为模糊隶属度加权定位,降低不可靠锚节点和路径影响。梯度泛洪法,对已有算法增加附加通信阶段,较小改造即可获得邻域值,拟合曲线可以简化邻域值到梯度的计算。仿真证实,可以用DV-Hop算法54.4%通信量,达到13%定位精度
This paper studied the distributed range-free localization algorithms for WSN (Wireless Sensor Networks).
WSN is an ad hoc network composed of many cheap nodes with capability of detections, calculations and wireless communications. Nodes' locations are usually random and unknown. Unfortunately, observation informations without location parameters are mostly useless. Therefore, node self-localization algorithm, especially distributed range-free one leading to low cost and well robustness, is one of WSN's key techniques. There are four major algorithms. DV-Hop has the best performance with precision 33% and traffic twice floods.
This paper discussed the localization principle and quantization error, proposed four new algorithms. Simulations by OPNET showed that the precisions are 16-20 percentages higher than DV-Hop's and the traffics are near once flood.
The main research results are as follows:
1. From analysis of distributed range-free localization algorithms' principle, the paper constructed theoretical models, and derived average connectivity formula and traffic estimate formula.Theoretical research showed that quantization error, which comes from using the radio range to quantize the distance, is the fundamental reason for localization error.
2. LSHop has the good integrated performance and may instead of DV-Hop. Equivalent hop counts and AHS (Average Hop Size) can reduce the impact of quantization error. Iterative Positioning strategy promotes precision by redundant informations. Memorial Flood avoids forwarding useless packets. Simulations showed that precision can be increased to approach to 15% and traffic can be decreased to 52.0% of DV-Hop's.
3. Cluster can self-exclude unreliable anchors or paths. Trilateral Proportion strategy calculates a pair of conjugate candidate positions from two points'positions and trilateral proportions. Simple Density Clustering strategy defines the number of candidates located within the clustering radius as density and uses centroid of the densest cluster as the node's location. Vote Flood avoids twice floods by the shortest path tree. Precision can be increased to close to 17% with 52.3% traffic of DV-Hop's, from the simulations.
4. HopScale has only one communication stage and adapts to the mobile anchors scenarios. Proportion Localization strategy can locate nodes without AHS calculation needed. Neighborhood Flood can keep the same process of a normal flood and obtain the Neighborhood Value. Gradient is a better distance proportion and may be calculated from Neighborhood Value by BinarySearch. Simulation showed that precision can be increased to about 16% with once flood traffic.
5. Fuzzy has the highest precision. Using the result of other existing algorithms as the clustering core and the statistics of distribution as the fuzzy membership grades, nodes may localize bypassing clustering. Gradient Flood simplifies the gradient calculation by Fitting Curve and avoids big changes to existing algorithms. Simulation showed that precision can be increased to near 13%, higher than that of all similar algorithms including the others put forward in this paper.
引文
[1]Warneke Brett, Last Matt, Liebowitz Brian, et al., Smart dust:communicating with a cubic-millimeter computer, Computer,34(1),2001:44-51.
[2]Kahn J. M., Katz R. H,, Pister K. S.J., Next century challenges:Mobile networking for'Smart Dust', Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM,1999:271-278.
[3]Manges Wayne W., Allgood Glenn 0., Smith Stephen F., It's time for sensors to go wireless. Part 1:Technological underpinnings, Sensors (Peterborough, NH),16(4), 1999:8.
[4]Allgood Glenn 0., Manges Wayne W, Smith Stephen F., It's time for sensors to go wireless. Part 2:Take a good technology and make it an economic success, Sensors (Peterborough, NH),16(5),1999:10.
[5]Pottie G. J., Kaiser W J., Wireless integrated network sensors, Communications of the ACM,43(5),2000:51-58.
[6]Zheng ZHOU, Self-localization Technologies for Wireless Sensor Network Nodes, ZTE COMMUNICATIONS,11(4),2005:52-56.
[7]WINS. http://www.ianet.ucla.edu/WINS/biblio.htm
[8]Smart Dust. http://robotics.eecs.berkelev.edu/-pister/SmartDust/
[9]SensIT. http://www. sensit.com/
[10]SeaWeb. http://www.seaweb.org
[11]Code Blue. http://www.eecs.harvard.edu/-mdw/proi/codeblue/
[121 EYES. http://www.eves.eu.org
[131 SEWING http://www.sewing.mixdes.org
[14]Li Zheng, ZigBee wireless sensor network in industrial applications, In:2006 SICE-ICASE International Joint Conference, Busan, South Korea,2006: 1067-1070.
[15]Jin Ming-Hui, Yu Chih-Hao, Lai Hung-Ren, et al., Zigbee positioning system for smart home application, In:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Niagara Falls, ON, Canada,2007:183-192.
[16]Zdenek Bradac, Petr Fiedler, Ondrej Hyncica, Design of ZigBee device, WSEAS Transactions and Communications,4(5),2005:224-234.
[17]Wheeler Andrew, Commercial applications of wireless sensor networks using ZigBee, IEEE Communications Magazine,45(4),2007:70-77.
[18]孙佩刚,赵海,罗玎玎,et al,,智能空间中RSSI定位问题研究,电子学报,35(7),2007：1240-1245.
[19]中国传感器网络学会,http://www. sensornet. org.cn
[20]Want Roy, Hopper Andy, Falcao Veronica, et al., Active badge location system, ACM Transactions of Information Systems,10(1),1992:91-102.
[21]Hopper Andy, Harter Andy, Blackie Tom, Active badge system, In:Conference Proceedings on Human Factors in Computing Systems, Amsterdam, Neth,1993: 533-534.
[22]Harter Andy, Hopper Andy, Steggles Pete, et al., The anatomy of a context-aware application, Wireless Networks,8(2-3),2002:187-197.
[23]Harter Andy, Hopper Andy, Steggles Pete, et al., Anatomy of a context-aware application, Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM,1999:59-68.
[24]Bahl Paramvir, Padmanabhan Venkata N., RADAR:An in-building RF-based user location and tracking system, In:Proceedings-IEEE INFOCOM, Tel Aviv, Isr, 2000:775-784.
[25]Niculescu Dragos, Nath Badri, Ad hoc positioning system (APS), In:Conference Record/IEEE Global Telecommunications Conference, San Antonio, TX,2001: 2926-2931.
[26]Bulusu Nirupama, Heidemann John, Estrin Deborah, GPS-less low-cost outdoor localization for very small devices, IEEE Personal Communications,7(5),2000: 28-34.
[27]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,软件学报,16(5),2005：857-868.
[28]Hightower Jeffrey, Boriello Gaetanu, Want Roy, SpotON:An indoor 3D Location Sensing Technology Based on BF Signal Strength, In:Technical Report UW CSE 00-02-02, Seatle. WA. USA,2000.
[29]Rabaey Jan M., Ammer M. Josie, da Silva Julio L., Jr., et al., PicoRadio supports ad hoc ultra-low power wireless networking, Computer,33(7),2000:42-48.
[30]Priyantha Nissanka B., Chakraborty Anit, Balakrishnan Hari, Cricket location-support system, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Boston, MA, USA,2000:32-43.
[31]Capkun S., Hamdi M., Hubaux J., GPS-free positioning in mobile ad-hoc networks, In:Proceedings of the Hawaii International Conference on System Sciences, Maui, HI,2001:255.
[32]Hightower J., Borriello G., Location systems for ubiquitous computing, Computer, 34(8),2001:57-66.
[33]Easy Living, http://www.research.microsoft.com/easyliving/
[34]Abelson Harold, Allen Don, Coore Daniel, et al., Amorphous, Communications of the ACM,43(5),2000:74-82.
[35]He Tian, Huang Chengdu, Blum Brian M., et al., Range-Free Localization Schemes for Large Scale Sensor Networks, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, San Diego, CA., United States,2003:81-95.
[36]Doherty L., Pister K. S. J., El Ghaoui L., Convex position estimation in wireless sensor networks, In:Proceedings-IEEE INFOCOM, Anchorage, AK,2001: 1655-1663.
[37]Shang Yi, Ruml Wheeler, Zhang Ying, et al., Localization from mere connectivity, In:Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Annapolis, MD, United States,2003:201-212.
[38]Rao Ananth, Ratnasamy Sylvia, Papadimitriou Christos, et al., Geographic Routing without Location Information, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, San Diego, CA., United States,2003:96-108.
[39]Niculescu Dragos, Nath Badri, Localized positioning in ad hoc networks, Ad Hoc Networks,1(2-3),2003:247-259.
[40]Savvides Andreas, Park Heemin, Srivastava Mani B., The bits and flops of the n-hop multilateration primitive for node localization problems, In:Proceedings of the ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, United States,2002:112-121.
[41]3D-iD, http://www.pinpointco.com/
[42]Anon, Walk this way for the smart floor, IEE Electronics Systems and Software, 1(3),2003:5.
[43]Savvides A., Han C. C., Strivastava M. B., Dynamic fine-grained localization in ad-hoc networks of sensors, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Rome,2001:166-179.
[44]Hazas Mike, Ward Andy, A High Performance Privacy-Oriented Location System, In:Proceedings of the first IEEE International Conference on Pervasive Computing and Communications(PerCom), Dallas-Fort worth, USA,2001:216-233.
[45]Hazas Mike, Ward Andy, A Novel Broadband Ultrasonic Location System, In: Gaetano Bomello and Lars Erik Hotmquist eds Proceedings of UbiComp 2002 oteborg Sweden,2002:264-280.
[46]Want Roy, Schilit Bill N., Adams Norman I., et al., Overview of the ParcTab ubiquitous computing experiment, IEEE Personal Communications,2(6),1995: 28-43.
[47]Schilit Bill, Adams Norman, Want Roy, Context-aware computing applications, In: Mobile Computing Systems and Applications-Workshop Proceedings, Santa Cruz, CA, USA,1995:85-90.
[48]Hightower Jeffrey, Borrielio Gaetano, Location Sensing Techinques, IEEE Computer,34(8),2001:57-66.
[49]Busso Carlos, Hernanz Sergi, Chu Chi-Wei, et al., Smart room:Participant and speaker localization and identification, In:ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, Philadelphia, PA, United States,2005:1117-1120.
[50]Harter Andy, Hopper Andy, Distributed location system for the active office, IEEE Network,8(1),1994:62-70.
[51]Zhou Yong, Ao Xin, Xia Shixiong, An improved APIT node self-localization algorithm in WSN, In:Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Chongqing, China,2008:7576-7581.
[52]Niculescu Dragos, Positioning in ad hoc sensor networks, IEEE Network,18(4), 2004:24-29.
[53]Niculescu Dragos, Nath Badri, Ad hoc positioning system (APS) using AOA, In: Proceedings-IEEE INFOCOM, San Francisco, CA, United States,2003: 1734-1743.
[54]张翰,刘锋,无线传感器网络基于凸规划的改进定位算法：Convex-PIT,传感技术学报20(5),2007：1129-1133.
[55]Meguerdichian Seapahn, Slijepcevic Sasa, Karayan Vahag, et al., Localized algorithms in wireless ad-hoc networks:Location discovery and sensor exposure, In:Proceedings of the 2001 ACM International Symposium on Mobile Ad Hoc Networking and Computing:MobiHoc 2001, Long Beach, CA, United States,2001: 106-116.
[56]肖玲,李仁发,罗娟,基于非度量多维标度的无线传感器网络节点定位算法,计算机研究与发展,44(3),2007：399-405.
[57]李善仓,张德运,马富海,et al.,基于分布式加权多维定标的节点自身定位算法,西安交通大学学报,40(12),2006：1388-1392.
[58]Iyengar Rajagopal, Sikdar Biplab, Scalable and distributed GPS free positioning for sensor networks, In:IEEE International Conference on Communications, Anchorage, AK, United States,2003:338-342.
[59]Savarese C., Rabaey J. M., Beutel J., Locationing in distributed ad-hoc wireless sensor networks, In:ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, Salt Lake, UT,2001:2037-2040.
[60]Savarese C, Rabay J, Langendoen K, Robust positioning algorithms for distributed ad-hoc wireless sensor networks, In:Proc. of the USENIX Technical Annual Conf., Monterey,CA, USA,2002:317-327.
[61]彭刚,曹元大,孙利民,无线传感器网络节点定位机制的研究,计算机工程与应用,40(35),2004：27-29.
[62]Huang Qiqian, Selvakennedy S., A range-free localization algorithm for wireless sensor networks, In:IEEE Vehicular Technology Conference, Melbourne, Australia, 2006:349-353.
[63]马祖长,孙怡宁,无线传感器网络节点的定位算法,计算机工程,30(7),2004：13-14,48.
[641 OPNET, http://www.opnet.com/
[65]Chen Hongyang, Sezaki Kaoru, Deng Ping, et al., An improved DV-hop localization algorithm for wireless sensor networks, In:2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, Singapore,. Singapore, 2008:1557-1561.
[66]Ning Yu, Jiangwen Wan, Qing Song, et al., An improved DV-Hop localization algorithm in wireless sensor networks, In:Proceedings of IEEE ICIA 2006-2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China,2006:638-643.
[67]Ji Wei-Wei, Liu Zhong, An improvement of DV-Hop algorithm in wireless sensor networks, In:2006 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2006, Wuhan, China,2007:4149434.
[68]刘锋,张翰,杨骥,一种基于加权处理的无线传感器网络平均跳距离估计算法,电子与信息学报,30(5),2008：1222-1225.
[69]Selim Shokri Z., Ismail M. A., K-MEANS-TYPE ALGORITHMS:A GENERALIZED CONVERGENCE THEOREM AND CHARACTERIZATION OF LOCAL OPTIMALITY, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(1),1984:81-87.
[70]Ng Raymond T., Han Jiawei, Efficient and effective clustering methods for spatial data mining, In:Very Large Data Bases, International Conference Proceedings, Santiago, Chile,1994:144.
[71]Zhang Tian, Ramakrishnan Raghu, Livny Miron, BIRCH:An efficient data clustering method for very large databases, In:SIGMOD Record (ACM Special Interest Group on Management of Data), Montreal, Can,1996:103-114.
[72]Guha Sudipto, Rastogi Rajeev, Shim Kyuseok, CURE:An efficient clustering algorithm for large databases, In:SIGMOD Record (ACM Special Interest Group on Management of Data), Seattle, WA, USA,1998:73-84.
[73]Ester M, Kriegel H P, xu x, A density-based algorithm for discovering clusters in large spatial databases In:Proc.1996 Int. Conf. Knowledge Discovery and Data Mining (KDD'96),, Portland,OR,1996:226-231.
[74]Ankerst Mihael, Breunig Markus M., Kriegel Hans-Peter, et al., OPTICS:ordering points to identify the clustering structure, In:Proceedings of the ACM SIGMOD International Conference on Management of Data,1999:49-60.
[75]Wang Wei, Yang Jiong, Muntz Richard, STING+:an approach to active spatial data mining, Proceedings-International Conference on Data Engineering,1999: 116-125.
[76]Yu Dantong, Chatterjee Surojit, Zhang Aidong, Efficiently detecting arbitrary shaped clusters in image databases, Proceedings of the International Conference on Tools with Artificial Intelligence,1999:187-194.
[77]Agrawal Rakesh, Gehrke Johannes, Gunopulos Dimitrios, et al., Automatic subspace clustering of high dimensional data for data mining applications, In: SIGMOD Record (ACM Special Interest Group on Management of Data), Seattle, WA, USA,1998:94-105.
[78]Fisher D, Improving inherence through conceptual clustering, In:Proc.1987 AAAI Conf,1987:461-465.
[79]孙雨耕,李桂丹,武晓光,et al.,基于基站辅助定位的无线传感器网络通信协议,天津大学学报,40(1),2007：98-103.
[80]Xiao Bin, Chen Hekang, Zhou Shuigeng, Distributed localization using a moving beacon in wireless sensor networks, IEEE Transactions on Parallel and Distributed Systems,19(5),2008:587-600.
[81]Zhang Baoli, Yu Fengqi, An energy efficient localization algorithm for wireless sensor networks using a mobile anchor node, In:Proceedings of the 2008 IEEE International Conference on Information and Automation, ICIA 2008, Zhangjiajie, Hunan, China,2008:215-219.
[82]Ganggang Yu, Fengqi Yu, A localization algorithm for mobile wireless sensor networks, In:IEEE ICIT 2007-2007 IEEE International Conference on Integration Technology, Shenzhen, China,2007:623-627.
[83]Ssu Kuo-Feng, Ou Chia-Ho, Jiau Hewijin Christine, Localization with mobile anchor points in wireless sensor networks, IEEE Transactions on Vehicular Technology,54(3),2005:1187-1197.
[84]张正勇,孙智,王刚,et al.,基于移动锚节点的无线传感器网络节点定位清华大学学报(自然科学版)47(4),2007：534-537.
[85]石为人,许磊,徐扬生,一种基于移动锚节点的静态无线传感器网络定位算法,仪器仪表学报,28(3),2007：385-393.
[86]Li Shanliang, Huang Liusheng, Wu Junmin, et al., NBLS: Neighbor-information-based localization system for wireless sensor networks, In: 3rd IEEE/Create-Net International Conference on Communication System Software and Middleware, COMSWARE, Bangalore, India,2007:91-94.
[87]冯少荣,肖文俊,DBSCAN聚类算法的研究与改进,中国矿业大学学报37(1),2008：105-111.
[88]冯少荣,肖文俊,一种提高DBSCAN聚类算法质量的新方法,西安电子科技大学学报(自然科学版),35(3),2008：523-529.
[89]Birant Derya, Kut Alp, ST-DBSCAN:An algorithm for clustering spatial-temporal data, Data and Knowledge Engineering,60(1),2007:208-221.
[90]Xu Wei-Jiang, Huang Liu-Sheng, Luo Yong-Long, et al., Privacy-preserving DBSCAN clustering over vertically partitioned data, In:Proceedings-2007 International Conference on Multimedia and Ubiquitous Engineering, MUE 2007, Seoul, South Korea,2007:850-856.
[91]Pedrycz Witold, Rai Partab, Collaborative clustering with the use of Fuzzy C-Means and its quantification, Fuzzy Sets and Systems,159(18),2008: 2399-2427.
[92]Wang Weina, Zhang Yunjie, On fuzzy cluster validity indices, Fuzzy Sets and Systems,158(19),2007:2095-2117.
[93]Jia Zhiping, Yu Zhiqiang, Zhang Chenghui, Fuzzy C-means clustering algorithm based on incomplete data, In:Proceedings of IEEE ICIA 2006-2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China, 2006:600-604.
[94]曾翎,王美玲,陈华富,遗传模糊C-均值聚类算法应用于MRI分割,电子科技大学学报,37(4),2008：627-629.
[95]Ohyama Isei, Suzuki Yukinori, Saga Sato, et al., Fuzzy modeling based on noise cluster and possibilistic clustering, In:2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006, Logan, Utah, United States,2006:225-230.
[96]Lee JiHsian, Liu Ruijie, A fuzzy clustering algorithm based on fuzzy distance norms for asynchronously sampled data, In:Proceedings-2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008, Sao Paulo, SP, Brazil,2008:361-368.
[97]Furong Liu, Gao X. Z., Changhong Wang, et al., A fuzzy clustering algorithm based on artificial immune principles, In:Proceedings-2007 International Conference on Computational Intelligence and Security, CIS 2007, Harbin, Heilongjiang, China, 2007:475-479.
[98]Agrawal Shubham, Panigrahi B. K., Tiwari Manoj Kumar, Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch, IEEE Transactions on Evolutionary Computation,12(5),2008:529-541.
[99]Wang Jiun-Hau, Lee Wan-Jui, Lee Shie-Jue, A Kernel-based fuzzy clustering algorithm, In:First International Conference on Innovative Computing, Information and Control 2006, ICICIC'06, Beijing, United States,2006:550-553.
[100]崔逊学,方红雨,朱徐来,传感器网络定位问题的概率特征,计算机研究与发展,44(4),2007：630-635.
[101]吕科,张军,王钢,et al.,基于序列Monte Carlo技术的动态节点定位,北京航空航天大学学报,33(8),2007：886-889.
[102]于宁,万江文,马万兴,无线传感器网络三维抽样定位,北京邮电大学学报,31(3),2008：13-18.
[103]吕良彬,曹阳,高洵,et al.,基于球壳交集的传感器网络三维定位算法,北京邮电大学学报,29(S1),2006：48-51.
[104]Yu Kegen, Guo Y. Jay, Improving anchor position accuracy for 3-D localization in wireless sensor networks, In:IEEE International Conference on Communications, Beijing, China,2008:951-955.
[105]Sheu Jang-Ping, Chen Pei-Chun, Hsu Chih-Shun, A Distributed Localization Scheme for Wireless Sensor Networks with Improved Grid-Scan and Vector-Based Refinement, IEEE TRANSACTIONS ON MOBILE COMPUTING,7(9),2008: 1110-1123.
[2]Kahn J. M., Katz R. H,, Pister K. S.J., Next century challenges:Mobile networking for'Smart Dust', Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM,1999:271-278.
[3]Manges Wayne W., Allgood Glenn 0., Smith Stephen F., It's time for sensors to go wireless. Part 1:Technological underpinnings, Sensors (Peterborough, NH),16(4), 1999:8.
[4]Allgood Glenn 0., Manges Wayne W, Smith Stephen F., It's time for sensors to go wireless. Part 2:Take a good technology and make it an economic success, Sensors (Peterborough, NH),16(5),1999:10.
[5]Pottie G. J., Kaiser W J., Wireless integrated network sensors, Communications of the ACM,43(5),2000:51-58.
[6]Zheng ZHOU, Self-localization Technologies for Wireless Sensor Network Nodes, ZTE COMMUNICATIONS,11(4),2005:52-56.
[7]WINS. http://www.ianet.ucla.edu/WINS/biblio.htm
[8]Smart Dust. http://robotics.eecs.berkelev.edu/-pister/SmartDust/
[9]SensIT. http://www. sensit.com/
[10]SeaWeb. http://www.seaweb.org
[11]Code Blue. http://www.eecs.harvard.edu/-mdw/proi/codeblue/
[121 EYES. http://www.eves.eu.org
[131 SEWING http://www.sewing.mixdes.org
[14]Li Zheng, ZigBee wireless sensor network in industrial applications, In:2006 SICE-ICASE International Joint Conference, Busan, South Korea,2006: 1067-1070.
[15]Jin Ming-Hui, Yu Chih-Hao, Lai Hung-Ren, et al., Zigbee positioning system for smart home application, In:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Niagara Falls, ON, Canada,2007:183-192.
[16]Zdenek Bradac, Petr Fiedler, Ondrej Hyncica, Design of ZigBee device, WSEAS Transactions and Communications,4(5),2005:224-234.
[17]Wheeler Andrew, Commercial applications of wireless sensor networks using ZigBee, IEEE Communications Magazine,45(4),2007:70-77.
[18]孙佩刚,赵海,罗玎玎,et al,,智能空间中RSSI定位问题研究,电子学报,35(7),2007：1240-1245.
[19]中国传感器网络学会,http://www. sensornet. org.cn
[20]Want Roy, Hopper Andy, Falcao Veronica, et al., Active badge location system, ACM Transactions of Information Systems,10(1),1992:91-102.
[21]Hopper Andy, Harter Andy, Blackie Tom, Active badge system, In:Conference Proceedings on Human Factors in Computing Systems, Amsterdam, Neth,1993: 533-534.
[22]Harter Andy, Hopper Andy, Steggles Pete, et al., The anatomy of a context-aware application, Wireless Networks,8(2-3),2002:187-197.
[23]Harter Andy, Hopper Andy, Steggles Pete, et al., Anatomy of a context-aware application, Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM,1999:59-68.
[24]Bahl Paramvir, Padmanabhan Venkata N., RADAR:An in-building RF-based user location and tracking system, In:Proceedings-IEEE INFOCOM, Tel Aviv, Isr, 2000:775-784.
[25]Niculescu Dragos, Nath Badri, Ad hoc positioning system (APS), In:Conference Record/IEEE Global Telecommunications Conference, San Antonio, TX,2001: 2926-2931.
[26]Bulusu Nirupama, Heidemann John, Estrin Deborah, GPS-less low-cost outdoor localization for very small devices, IEEE Personal Communications,7(5),2000: 28-34.
[27]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,软件学报,16(5),2005：857-868.
[28]Hightower Jeffrey, Boriello Gaetanu, Want Roy, SpotON:An indoor 3D Location Sensing Technology Based on BF Signal Strength, In:Technical Report UW CSE 00-02-02, Seatle. WA. USA,2000.
[29]Rabaey Jan M., Ammer M. Josie, da Silva Julio L., Jr., et al., PicoRadio supports ad hoc ultra-low power wireless networking, Computer,33(7),2000:42-48.
[30]Priyantha Nissanka B., Chakraborty Anit, Balakrishnan Hari, Cricket location-support system, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Boston, MA, USA,2000:32-43.
[31]Capkun S., Hamdi M., Hubaux J., GPS-free positioning in mobile ad-hoc networks, In:Proceedings of the Hawaii International Conference on System Sciences, Maui, HI,2001:255.
[32]Hightower J., Borriello G., Location systems for ubiquitous computing, Computer, 34(8),2001:57-66.
[33]Easy Living, http://www.research.microsoft.com/easyliving/
[34]Abelson Harold, Allen Don, Coore Daniel, et al., Amorphous, Communications of the ACM,43(5),2000:74-82.
[35]He Tian, Huang Chengdu, Blum Brian M., et al., Range-Free Localization Schemes for Large Scale Sensor Networks, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, San Diego, CA., United States,2003:81-95.
[36]Doherty L., Pister K. S. J., El Ghaoui L., Convex position estimation in wireless sensor networks, In:Proceedings-IEEE INFOCOM, Anchorage, AK,2001: 1655-1663.
[37]Shang Yi, Ruml Wheeler, Zhang Ying, et al., Localization from mere connectivity, In:Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Annapolis, MD, United States,2003:201-212.
[38]Rao Ananth, Ratnasamy Sylvia, Papadimitriou Christos, et al., Geographic Routing without Location Information, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, San Diego, CA., United States,2003:96-108.
[39]Niculescu Dragos, Nath Badri, Localized positioning in ad hoc networks, Ad Hoc Networks,1(2-3),2003:247-259.
[40]Savvides Andreas, Park Heemin, Srivastava Mani B., The bits and flops of the n-hop multilateration primitive for node localization problems, In:Proceedings of the ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, United States,2002:112-121.
[41]3D-iD, http://www.pinpointco.com/
[42]Anon, Walk this way for the smart floor, IEE Electronics Systems and Software, 1(3),2003:5.
[43]Savvides A., Han C. C., Strivastava M. B., Dynamic fine-grained localization in ad-hoc networks of sensors, In:Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, Rome,2001:166-179.
[44]Hazas Mike, Ward Andy, A High Performance Privacy-Oriented Location System, In:Proceedings of the first IEEE International Conference on Pervasive Computing and Communications(PerCom), Dallas-Fort worth, USA,2001:216-233.
[45]Hazas Mike, Ward Andy, A Novel Broadband Ultrasonic Location System, In: Gaetano Bomello and Lars Erik Hotmquist eds Proceedings of UbiComp 2002 oteborg Sweden,2002:264-280.
[46]Want Roy, Schilit Bill N., Adams Norman I., et al., Overview of the ParcTab ubiquitous computing experiment, IEEE Personal Communications,2(6),1995: 28-43.
[47]Schilit Bill, Adams Norman, Want Roy, Context-aware computing applications, In: Mobile Computing Systems and Applications-Workshop Proceedings, Santa Cruz, CA, USA,1995:85-90.
[48]Hightower Jeffrey, Borrielio Gaetano, Location Sensing Techinques, IEEE Computer,34(8),2001:57-66.
[49]Busso Carlos, Hernanz Sergi, Chu Chi-Wei, et al., Smart room:Participant and speaker localization and identification, In:ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, Philadelphia, PA, United States,2005:1117-1120.
[50]Harter Andy, Hopper Andy, Distributed location system for the active office, IEEE Network,8(1),1994:62-70.
[51]Zhou Yong, Ao Xin, Xia Shixiong, An improved APIT node self-localization algorithm in WSN, In:Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Chongqing, China,2008:7576-7581.
[52]Niculescu Dragos, Positioning in ad hoc sensor networks, IEEE Network,18(4), 2004:24-29.
[53]Niculescu Dragos, Nath Badri, Ad hoc positioning system (APS) using AOA, In: Proceedings-IEEE INFOCOM, San Francisco, CA, United States,2003: 1734-1743.
[54]张翰,刘锋,无线传感器网络基于凸规划的改进定位算法：Convex-PIT,传感技术学报20(5),2007：1129-1133.
[55]Meguerdichian Seapahn, Slijepcevic Sasa, Karayan Vahag, et al., Localized algorithms in wireless ad-hoc networks:Location discovery and sensor exposure, In:Proceedings of the 2001 ACM International Symposium on Mobile Ad Hoc Networking and Computing:MobiHoc 2001, Long Beach, CA, United States,2001: 106-116.
[56]肖玲,李仁发,罗娟,基于非度量多维标度的无线传感器网络节点定位算法,计算机研究与发展,44(3),2007：399-405.
[57]李善仓,张德运,马富海,et al.,基于分布式加权多维定标的节点自身定位算法,西安交通大学学报,40(12),2006：1388-1392.
[58]Iyengar Rajagopal, Sikdar Biplab, Scalable and distributed GPS free positioning for sensor networks, In:IEEE International Conference on Communications, Anchorage, AK, United States,2003:338-342.
[59]Savarese C., Rabaey J. M., Beutel J., Locationing in distributed ad-hoc wireless sensor networks, In:ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, Salt Lake, UT,2001:2037-2040.
[60]Savarese C, Rabay J, Langendoen K, Robust positioning algorithms for distributed ad-hoc wireless sensor networks, In:Proc. of the USENIX Technical Annual Conf., Monterey,CA, USA,2002:317-327.
[61]彭刚,曹元大,孙利民,无线传感器网络节点定位机制的研究,计算机工程与应用,40(35),2004：27-29.
[62]Huang Qiqian, Selvakennedy S., A range-free localization algorithm for wireless sensor networks, In:IEEE Vehicular Technology Conference, Melbourne, Australia, 2006:349-353.
[63]马祖长,孙怡宁,无线传感器网络节点的定位算法,计算机工程,30(7),2004：13-14,48.
[641 OPNET, http://www.opnet.com/
[65]Chen Hongyang, Sezaki Kaoru, Deng Ping, et al., An improved DV-hop localization algorithm for wireless sensor networks, In:2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, Singapore,. Singapore, 2008:1557-1561.
[66]Ning Yu, Jiangwen Wan, Qing Song, et al., An improved DV-Hop localization algorithm in wireless sensor networks, In:Proceedings of IEEE ICIA 2006-2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China,2006:638-643.
[67]Ji Wei-Wei, Liu Zhong, An improvement of DV-Hop algorithm in wireless sensor networks, In:2006 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2006, Wuhan, China,2007:4149434.
[68]刘锋,张翰,杨骥,一种基于加权处理的无线传感器网络平均跳距离估计算法,电子与信息学报,30(5),2008：1222-1225.
[69]Selim Shokri Z., Ismail M. A., K-MEANS-TYPE ALGORITHMS:A GENERALIZED CONVERGENCE THEOREM AND CHARACTERIZATION OF LOCAL OPTIMALITY, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(1),1984:81-87.
[70]Ng Raymond T., Han Jiawei, Efficient and effective clustering methods for spatial data mining, In:Very Large Data Bases, International Conference Proceedings, Santiago, Chile,1994:144.
[71]Zhang Tian, Ramakrishnan Raghu, Livny Miron, BIRCH:An efficient data clustering method for very large databases, In:SIGMOD Record (ACM Special Interest Group on Management of Data), Montreal, Can,1996:103-114.
[72]Guha Sudipto, Rastogi Rajeev, Shim Kyuseok, CURE:An efficient clustering algorithm for large databases, In:SIGMOD Record (ACM Special Interest Group on Management of Data), Seattle, WA, USA,1998:73-84.
[73]Ester M, Kriegel H P, xu x, A density-based algorithm for discovering clusters in large spatial databases In:Proc.1996 Int. Conf. Knowledge Discovery and Data Mining (KDD'96),, Portland,OR,1996:226-231.
[74]Ankerst Mihael, Breunig Markus M., Kriegel Hans-Peter, et al., OPTICS:ordering points to identify the clustering structure, In:Proceedings of the ACM SIGMOD International Conference on Management of Data,1999:49-60.
[75]Wang Wei, Yang Jiong, Muntz Richard, STING+:an approach to active spatial data mining, Proceedings-International Conference on Data Engineering,1999: 116-125.
[76]Yu Dantong, Chatterjee Surojit, Zhang Aidong, Efficiently detecting arbitrary shaped clusters in image databases, Proceedings of the International Conference on Tools with Artificial Intelligence,1999:187-194.
[77]Agrawal Rakesh, Gehrke Johannes, Gunopulos Dimitrios, et al., Automatic subspace clustering of high dimensional data for data mining applications, In: SIGMOD Record (ACM Special Interest Group on Management of Data), Seattle, WA, USA,1998:94-105.
[78]Fisher D, Improving inherence through conceptual clustering, In:Proc.1987 AAAI Conf,1987:461-465.
[79]孙雨耕,李桂丹,武晓光,et al.,基于基站辅助定位的无线传感器网络通信协议,天津大学学报,40(1),2007：98-103.
[80]Xiao Bin, Chen Hekang, Zhou Shuigeng, Distributed localization using a moving beacon in wireless sensor networks, IEEE Transactions on Parallel and Distributed Systems,19(5),2008:587-600.
[81]Zhang Baoli, Yu Fengqi, An energy efficient localization algorithm for wireless sensor networks using a mobile anchor node, In:Proceedings of the 2008 IEEE International Conference on Information and Automation, ICIA 2008, Zhangjiajie, Hunan, China,2008:215-219.
[82]Ganggang Yu, Fengqi Yu, A localization algorithm for mobile wireless sensor networks, In:IEEE ICIT 2007-2007 IEEE International Conference on Integration Technology, Shenzhen, China,2007:623-627.
[83]Ssu Kuo-Feng, Ou Chia-Ho, Jiau Hewijin Christine, Localization with mobile anchor points in wireless sensor networks, IEEE Transactions on Vehicular Technology,54(3),2005:1187-1197.
[84]张正勇,孙智,王刚,et al.,基于移动锚节点的无线传感器网络节点定位清华大学学报(自然科学版)47(4),2007：534-537.
[85]石为人,许磊,徐扬生,一种基于移动锚节点的静态无线传感器网络定位算法,仪器仪表学报,28(3),2007：385-393.
[86]Li Shanliang, Huang Liusheng, Wu Junmin, et al., NBLS: Neighbor-information-based localization system for wireless sensor networks, In: 3rd IEEE/Create-Net International Conference on Communication System Software and Middleware, COMSWARE, Bangalore, India,2007:91-94.
[87]冯少荣,肖文俊,DBSCAN聚类算法的研究与改进,中国矿业大学学报37(1),2008：105-111.
[88]冯少荣,肖文俊,一种提高DBSCAN聚类算法质量的新方法,西安电子科技大学学报(自然科学版),35(3),2008：523-529.
[89]Birant Derya, Kut Alp, ST-DBSCAN:An algorithm for clustering spatial-temporal data, Data and Knowledge Engineering,60(1),2007:208-221.
[90]Xu Wei-Jiang, Huang Liu-Sheng, Luo Yong-Long, et al., Privacy-preserving DBSCAN clustering over vertically partitioned data, In:Proceedings-2007 International Conference on Multimedia and Ubiquitous Engineering, MUE 2007, Seoul, South Korea,2007:850-856.
[91]Pedrycz Witold, Rai Partab, Collaborative clustering with the use of Fuzzy C-Means and its quantification, Fuzzy Sets and Systems,159(18),2008: 2399-2427.
[92]Wang Weina, Zhang Yunjie, On fuzzy cluster validity indices, Fuzzy Sets and Systems,158(19),2007:2095-2117.
[93]Jia Zhiping, Yu Zhiqiang, Zhang Chenghui, Fuzzy C-means clustering algorithm based on incomplete data, In:Proceedings of IEEE ICIA 2006-2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China, 2006:600-604.
[94]曾翎,王美玲,陈华富,遗传模糊C-均值聚类算法应用于MRI分割,电子科技大学学报,37(4),2008：627-629.
[95]Ohyama Isei, Suzuki Yukinori, Saga Sato, et al., Fuzzy modeling based on noise cluster and possibilistic clustering, In:2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006, Logan, Utah, United States,2006:225-230.
[96]Lee JiHsian, Liu Ruijie, A fuzzy clustering algorithm based on fuzzy distance norms for asynchronously sampled data, In:Proceedings-2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008, Sao Paulo, SP, Brazil,2008:361-368.
[97]Furong Liu, Gao X. Z., Changhong Wang, et al., A fuzzy clustering algorithm based on artificial immune principles, In:Proceedings-2007 International Conference on Computational Intelligence and Security, CIS 2007, Harbin, Heilongjiang, China, 2007:475-479.
[98]Agrawal Shubham, Panigrahi B. K., Tiwari Manoj Kumar, Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch, IEEE Transactions on Evolutionary Computation,12(5),2008:529-541.
[99]Wang Jiun-Hau, Lee Wan-Jui, Lee Shie-Jue, A Kernel-based fuzzy clustering algorithm, In:First International Conference on Innovative Computing, Information and Control 2006, ICICIC'06, Beijing, United States,2006:550-553.
[100]崔逊学,方红雨,朱徐来,传感器网络定位问题的概率特征,计算机研究与发展,44(4),2007：630-635.
[101]吕科,张军,王钢,et al.,基于序列Monte Carlo技术的动态节点定位,北京航空航天大学学报,33(8),2007：886-889.
[102]于宁,万江文,马万兴,无线传感器网络三维抽样定位,北京邮电大学学报,31(3),2008：13-18.
[103]吕良彬,曹阳,高洵,et al.,基于球壳交集的传感器网络三维定位算法,北京邮电大学学报,29(S1),2006：48-51.
[104]Yu Kegen, Guo Y. Jay, Improving anchor position accuracy for 3-D localization in wireless sensor networks, In:IEEE International Conference on Communications, Beijing, China,2008:951-955.
[105]Sheu Jang-Ping, Chen Pei-Chun, Hsu Chih-Shun, A Distributed Localization Scheme for Wireless Sensor Networks with Improved Grid-Scan and Vector-Based Refinement, IEEE TRANSACTIONS ON MOBILE COMPUTING,7(9),2008: 1110-1123.