无线传感器网络节点定位算法研究
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摘要
物联网作为国家五大新兴战略性产业之一,在国内受到了广泛的关注。无线传感器网络技术作为物联网技术重要的基础性研究工作,也将继续成为学术界的一个研究热点。无线传感器网络是由密集部署在监测区域内的大量的微型传感器节点通过无线自组织方式组成的分布式多跳网络,其目的是协作地感知、采集和处理监测区域内感知对象的信息,并报告给观察者。目前,无线传感器网络已经在军事国防、医疗卫生、环境监测、智能家居等领域得到了广泛的应用。
无线传感器网络往往通过将成百上千的传感器节点撒入相应的应用场景,从而获得监测场景中的特定信息。对于大多数无线传感器网络的应用,不知道传感器的位置而感知的数据是没有意义的。此外,通过节点的位置信息,可以完成目标跟踪、辅助路由、网络的负载均衡等工作。但是,人工部署节点或者为每个传感器节点都安装定位设备会受到成本、功耗、扩展性等问题的限制,甚至在某些场合根本无法实现。因此,如何通过部分知道位置的节点确定未知节点的绝对位置或者相对位置一直是无线传感器网络理论研究中的一个重要课题。
本文通过对无线传感器网络现有的经典定位方法的研究,特别是对质心算法及现有的加权质心算法的分析,结合功率控制方法在定位中的应用,提出两种新的基于功率控制的定位方法。
基于功率控制和邻居节点协作的无线传感器网络质心定位算法充分利用了功率控制的思想,通过锚节点分级广播的方式有效地区分了处于不同位置上的锚节点并以此设置合理的定位修正权重。在未知节点的锚节点连通度较低的情况下,算法通过优先定位一些连通度较好的未知节点并将其转化为辅助锚节点来辅助定位,在降低定位误差的同时提升了定位比例。
一种新的基于功率控制的无线传感器网络质心定位算法借助功率控制的思想通过锚节点分级广播的方式将未知节点划归到一个虚拟网格中,通过计算网格的顶点坐标并为之赋予相应的权值进行加权质心运算来估算未知节点的位置。此外,算法还可以通过合理地设置分级广播的数目可以对未知节点的位置进行进一步修正。改进算法大幅度地提高了定位的精度。
As one of the state's five new strategic industry, the Internet of things has been received widespread concern in domestic. As one of the important basic research work of the Internet of things, Wireless Sensor Network (WSN) will continue to be a hot spot of research in academic circles. WSN is a distributed, multi-hop network composed of massive micro-sensor placed densely in the monitoring area in wireless, self-organizing manner, aiming to collaboratively sense, collect and process the information from the sensed objects in the monitoring area, and then report to the observer. Currently, WSN has played a great role in many applications such as military and national defense, health care, environmental monitoring, smart home, etc.
WSN often distributes hundreds of sensor nodes into the corresponding application scene, so as to achieve specific monitoring information. Because sensor node is often distributed by random, its position cannot be determined beforehand. For most applications of wireless sensor networks, the monitoring information without location information is meaningless. In addition, the node's location information can be used to complete tracking, routing, balancing the network load and so on. However, the manual deployment of nodes would be limited by money, power consumption, scalability and other issues. Even, it might not be realized in certain situations. So, how to know the absolute position or the relative position of unknown nodes by the anchors is one of the most important issues in WSN.
This thesis studies the existing classic localization algorithms, especially the centroid algorithm, the existing weighted centroid algorithms. Then, the thesis puts forward two new centroid algorithms based on power control.
The Weighted Centroid Localization Algorithm Based on Power Control and Cooperative Theory makes full use of the power control theory to distinguish the anchors at different position by adjusting the anchors' transmit power. And every anchor will be set the reasonable weight for the every specific unknown node. In the situation of low nodes connectivity, the algorithm chooses some unknown nodes met the corresponding conditions to be auxiliary anchors, so as to reduce estimation error and promote the localization proportion.
A Novel Weighted Centroid Localization Algorithm Based on Power Control uses the thoughts of power control to allot unknown nodes in the virtual grid by anchor's graded broadcasting. The algorithm calculates the vertex of the virtual grid and set the appropriate weighted for every vertex. Then, it calculates the weighted centroid of the vertex as the estimated location of unknown node. In addition, the position of unknown nodes can be further correction by properly setting the number of grading radio. The novel algorithm greatly improves the accuracy of positioning.
无线传感器网络往往通过将成百上千的传感器节点撒入相应的应用场景,从而获得监测场景中的特定信息。对于大多数无线传感器网络的应用,不知道传感器的位置而感知的数据是没有意义的。此外,通过节点的位置信息,可以完成目标跟踪、辅助路由、网络的负载均衡等工作。但是,人工部署节点或者为每个传感器节点都安装定位设备会受到成本、功耗、扩展性等问题的限制,甚至在某些场合根本无法实现。因此,如何通过部分知道位置的节点确定未知节点的绝对位置或者相对位置一直是无线传感器网络理论研究中的一个重要课题。
本文通过对无线传感器网络现有的经典定位方法的研究,特别是对质心算法及现有的加权质心算法的分析,结合功率控制方法在定位中的应用,提出两种新的基于功率控制的定位方法。
基于功率控制和邻居节点协作的无线传感器网络质心定位算法充分利用了功率控制的思想,通过锚节点分级广播的方式有效地区分了处于不同位置上的锚节点并以此设置合理的定位修正权重。在未知节点的锚节点连通度较低的情况下,算法通过优先定位一些连通度较好的未知节点并将其转化为辅助锚节点来辅助定位,在降低定位误差的同时提升了定位比例。
一种新的基于功率控制的无线传感器网络质心定位算法借助功率控制的思想通过锚节点分级广播的方式将未知节点划归到一个虚拟网格中,通过计算网格的顶点坐标并为之赋予相应的权值进行加权质心运算来估算未知节点的位置。此外,算法还可以通过合理地设置分级广播的数目可以对未知节点的位置进行进一步修正。改进算法大幅度地提高了定位的精度。
As one of the state's five new strategic industry, the Internet of things has been received widespread concern in domestic. As one of the important basic research work of the Internet of things, Wireless Sensor Network (WSN) will continue to be a hot spot of research in academic circles. WSN is a distributed, multi-hop network composed of massive micro-sensor placed densely in the monitoring area in wireless, self-organizing manner, aiming to collaboratively sense, collect and process the information from the sensed objects in the monitoring area, and then report to the observer. Currently, WSN has played a great role in many applications such as military and national defense, health care, environmental monitoring, smart home, etc.
WSN often distributes hundreds of sensor nodes into the corresponding application scene, so as to achieve specific monitoring information. Because sensor node is often distributed by random, its position cannot be determined beforehand. For most applications of wireless sensor networks, the monitoring information without location information is meaningless. In addition, the node's location information can be used to complete tracking, routing, balancing the network load and so on. However, the manual deployment of nodes would be limited by money, power consumption, scalability and other issues. Even, it might not be realized in certain situations. So, how to know the absolute position or the relative position of unknown nodes by the anchors is one of the most important issues in WSN.
This thesis studies the existing classic localization algorithms, especially the centroid algorithm, the existing weighted centroid algorithms. Then, the thesis puts forward two new centroid algorithms based on power control.
The Weighted Centroid Localization Algorithm Based on Power Control and Cooperative Theory makes full use of the power control theory to distinguish the anchors at different position by adjusting the anchors' transmit power. And every anchor will be set the reasonable weight for the every specific unknown node. In the situation of low nodes connectivity, the algorithm chooses some unknown nodes met the corresponding conditions to be auxiliary anchors, so as to reduce estimation error and promote the localization proportion.
A Novel Weighted Centroid Localization Algorithm Based on Power Control uses the thoughts of power control to allot unknown nodes in the virtual grid by anchor's graded broadcasting. The algorithm calculates the vertex of the virtual grid and set the appropriate weighted for every vertex. Then, it calculates the weighted centroid of the vertex as the estimated location of unknown node. In addition, the position of unknown nodes can be further correction by properly setting the number of grading radio. The novel algorithm greatly improves the accuracy of positioning.
引文
[1]温家宝.2010年度政府工作报告.http://www.gov.cn/20101h/content_1555767.htm.
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[2]温家宝.2011年度政府工作报告.http://www.gov.cn/2011lh/content_1825233.htm.
[3]孙利民,李建中,陈渝,朱红松.无线传感器网络[M].北京：清华大学出版社,2005.
[4]Wade R, Mitehel W M, Peter F. Ten emerging technologies that will change the world [J]. Technology Review. Feb.2003, Vol.106, No.1:22-49.
[5]Byrne J A.21 ideas for 21st Century [J].Business Week,1999,8:78-167.
[6]Chong C Y, kumar S P. Sensor Networks:evolution, opportunities and challenges[A]. Proc. of the IEEE[C],2003,91(8):1247-1256.
[7]Capkun S, Hamdi M, Hubaux J-P. GPS-Free positioning in mobile ad-hoc networks. Cluster Computing,2002,5(2):157-167.
[8]Meguerdichian S, Koushanfar F, Potkonjak M, Srivastava MB. Coverage problems in wireless ad-hoc sensor networks. In:Proc. of the IEEE INFOCOM 2001. Vol.3, Anchorage:IEEE Computer and Communications Societies,2001:1380-1387.
[9]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.
[10]A. Cerpa, J. Elson, D. Estrin. Habit Monitoring:Application Driver for Wireless Communications Technology. In Proc of the ACM SIGOMM Workshop on Data Communications in Latin America and the Caribbean, San Jose, Costa Rica,2001.
[11]JASON LESTER HILL. System Architecture for Wireless Sensor Networks [D. UCA, Berkeley,2003.
[12]DEEPAK GANESAN, ALBERTO CERPA, WEI YE. Networking Issues in Wireless Sensor Networks [J]. Journal Parallel and Distributed Computing,2004,64: 799-814.
[13]Asre J, Clare L. An integrated architecture for cooperative sensing networks [J]. IEEE Computer Magazine.2000,33(5):106-108.
[14]Pottie G. J and Kaiser W J. Wireless integrated network sensors [J]. Communication of the ACM, May 2000,43(5):51-58.
[15]Ping S. Delay measurement time synchronization for wireless sensor networks. Intel Research Berkeley lab,June 2003.
[16]Elson J, Romer K. Wireless sensor networks:A new regime for time synchronization. In:Proc 1st Workshop on Hot Topics in Networks, Princeton, NJ. October 2002.
[17]Ganeriwal S, Kumar R, Adlakha S. Network-wide time synchronization in sensor networks. Networked and Embedded Systems Lab. UCLA Technical Report 2003.
[18]Pering T A, Burd T D, and Brodersen R W. The simulation and evaluation of dynamic voltage scaling algorithm[C], Proc. ISLPED,1998:76-81.
[19]Benini L, DeMicheli G. Dynamic Power Management:Design Techniques & CAD Tools[M], Kluwer Academic Publishers, Norwell, MA,1997.
[20]Chan H W, Szewczyk R. SPINS:Security protocols for sensor networks. Wireless Networks,2002,8(5):521-534.
[21]王殊,阎毓杰,胡富平等.无线传感器网络的理论及应用[M].北京：北京航空航天大学出版社,2007.
[22]于宏毅,李欧,张效义.无线传感器网络理论、技术与实现[M].北京：国防工业出版社,2008.
[23]He T, Huang C, Blum B M, Stankovic J A, Abdelzaher T. Range-free localization schemes for large scale sensor networks. In:Proc 9th Annual Int'l Conf on Mobile Computing and Networking (MobiCom), San Diego, CA.2003:81-95
[24]Priyantha NB, Chakraborty A, Balakrishnan H. The cricket location-support system. In:Proc. of the 6th Annual Int'l Conf. on Mobile Computing and Networking. Boston:ACM Press,2000:32-43.
[25]Savvides A, Han C-C, Srivastava MB. Dynamic fine-grained localization in ad-hoc networks of sensors. In:Proc. of the 7th Annual Int'l Conf. on Mobile Computing and Networking. Rome:ACM Press,2001:166-179.
[26]Niculescu D, Nath B. Ad Hoc Positioning System (APS) Using AoA. In Proceeding of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2003), San Francisco, CA, March 2003,3:1734-1743.
[27]Bahl P, Venkata N. Padmanabhan. RADAR:An in-Building RF-based User Location and Tracking System. In Proceeding of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom 2000), Tel Aviv, Israel, March 2000:775-784.
[28]Wang Shu, Liu Kezhong, Hu Fuping, et al. A Distributed Sensor Network Localization Scheme Motivated by Graph Rigidity Theory. The Moditerranean Journal of Measurement and Control,2005,1(4):185-190.
[29]De Oliveira H A B F, Nakamura E F, Loureiro A A F, et al. Directed Position Estimation:a Recursive Localization Approach for WirelessSensor Networks. In Proceeding of the 14th International Conference on Computer Communications and Netoworks, October 2005:557-562.
[30]Nguyen X, Jordan M. and Sinopoli B. A Kernel-Based Learning Approach to Ad Hoc Sensor Network Localization. ACM Transactions on Sensor Netwoks, Vol.1, No.1. August 2005:134-152.
[31]Roos T, Myllymaki P, and Tirri H. A Statistical Modeling Approach to Location Estimation. IEEE TRANSACTIONS ON MOBILE COMPUTION, Vol.1, No.1. January-March 2002:59-69.
[32]Priyantha N, Balakrishnam H, Demaine E, Teller S. Anchor-free distributed localization in sensor networks. Technical Report MIT-LCS-TR-892, MIT Lab for Computer Science, April 2003.
[33]Girod L, Estrin D. Robust range estimation using acoustic and multimodal sensing. In:Proc IEEE/RSJ Int'l Conf Intelligent Robots and Systems (IROS'01), Vol.3, Maui, Hawaii, USA.2001:1312-1320.
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