无线传感器网络分布式节点定位方法研究
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摘要
近年来,微机电系统、无线通信和数字电子技术的进步促进了具有低功耗、低代价与多功能特点的微型传感器制造技术的发展。大量具有传感单元、数据处理单元和通信单元的传感器节点引发了无线传感器网络的概念,即将大量的传感器密集地散布在感知区域,传感器间以自组织的方式构成无线通信网络,有效实现远程信息的采集、处理和传输。无线传感器网络在国防军事、环境监测、交通管理、医疗卫生、建筑和结构监测及反恐抗灾等领域具有广泛的应用前景。
无线传感器网络及其相关领域的研究引起了人们广泛的关注,主要包括各种网络协议、时间同步、协同信息处理、网络拓扑控制等。在众多相关研究领域中,传感器节点的位置信息作为网络无缝协调的基础成为了必需解决的关键问题之一。对无线传感器网络而言,设计一个可行的节点定位方案面临诸多挑战,主要表现为复杂的物理环境和有限的网络资源。
本文旨在探讨符合无线传感器网络特点和要求的分布式节点定位方法,主要工作概括如下:
本文探讨了大规模、自组织无线传感器网络实现节点定位的主要挑战。阐述了本文的选题背景及意义,综述了无线传感器网络的节点定位算法的研究进展。此外,论文简要介绍了无线传感器网络其他领域的主要研究现状。
针对典型的DV-Hop定位算法,详细分析了该算法特性,利用Cramer-Rao边界定理对定位误差特性从理论上进行了分析和探讨,然后从理论和实验两方面分析了算法中使用的跳距估计误差。在此基础上,本文提出了一种改进方法,其基本思想是根据导标节点和未知节点间的相互位置关系有选择性地利用导标节点,主要创新在于导标节点共线度概念的引入。在算法的实现中,提出了自适应共线度阈值确定方法。通过仿真实验对定位性能比较,改进算法较传统算法在平均误差和误差方差方面分别降低10~45%和35~50%。
针对无线传感器网络拓扑结构特点及经典无须测距定位方法的局限性,提出了分布式的多跳导标节点定位方法(Multi-Hop Beacon Based Localization,简称MHB定位方法)。该方法的主要创新在于应用距离矢量路由法获得邻近导标节点的同时,充分利用了三角形内点的特性及相关几何性质,在选择参与定位的导标节点集时考虑了导标节点共线度及未知节点与导标三角形的几何位置关系,并在此基础上提出了不依赖于复杂优化计算的扩展质心位置估算策略。MHB算法具有很好的自适应性、分布性和可扩展性,特别是在计算复杂度及定位精度鲁棒性等方面表现出了很好的性能。当网络密度从4到14变化过程中,该算法最大定位误差和定位误差方差分别为DV-Hop的1/6~1/2和1/3~1/2。
本文也探讨了高精度的节点定位方法,该方法的探讨是基于测距技术而展开的,刚性图理论为本方法提供了理论支撑。受随机图论中刚性图理论的启发,本文提出了基于定位协作体的节点定位方法,该方法主要思想是根据网络局部拓扑自适应形成可实现节点位置估计的定位协作体,然后通过优化计算实现未知节点的位置估计。这种基于协作模式的定位策略的主要优点在于充分利用了多跳导标节点的位置信息,同时还可有效避免了定位误差在网络中恶性传播和积累。论文重点描述了基于协作模式的节点定位方法的基本原理及定位协作体的生成算法。仿真结果表明,提出的定位策略具有较好的自适应性、鲁棒形和可靠性,当测距误差方差从0.025~0.30倍通信视距时,定位误差为0.02~0.36倍通信视距,该定位精度能满足多数场合下无线传感器网络对节点位置信息的精度要求。
在上述定位方法的研究基础上,本文分析了定位服务质量的相关问题,提出了区分定位服务策略,即在实际的应用中可以根据定位需求和定位场景提供不同服务质量的定位支持。这种策略的提出可以使定位算法在满足系统定位精度的前提下大大降低系统能耗,这为无线传感器网络定位问题提供了新的解决思路。
论文最后总结了所作的工作,并就进一步的研究方向进行了简单探讨。
Recent advances in micro-electro-mechanical system (MEMS) technology, wireless communications and digital electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered in short distances. These tiny sensor nodes which consist of sensing, data processing, and communicaiting components, leverage the idea of wireless sensor networks based on collaborative effort of a large number of nodes. There are many potential applications of sensor nodes of wireless sensor networks: military surveillance, environmental monitoring, traffic surveillance, medical treatment, building and structures monitoring, even anti-terrorism, etc.
Through distributed coordination, wireless sensor networks are expected to revolutionize the ways in which we understand and construct complex physical systems. Fundamental to such coordination is localization, or the ability to establish spatial relationships among objects. Certainly, a viable solution to the node localization problem imposes many challenges. At physical level, changes in the surrounding environment introduce error in the sensor measurements. At network level, nodes need to determine their locations in a reliable manner while operating under stringent constraints in computation, communication and energy resource.
In this dissertation, we discuss the distributed localization scheme suitable to wireless sensor networks. The main contributions of this dissertation are: this dissertation addresses the background of localization and the challenges involved in localization for very large, ad hoc deployed wireless sensor networks, summarizes the proposed localization algorithms. As well as, it briefly introduces other open research issues in wireless sensor networks.In this dissertation, we address the challenges involved in localization for very large, ad hoc deployed wireless sensor networks. Although several localization technologies have been proposed in the past few years, none currently satisfies all our requirements because no single localization system is simultaneously scalable, ad hoc deployable and accommodating of the hardware constraints of very small devices. Our thesis is that all these issues can be solved simultaneously by a self-configuring localization system that autonomously adapts to its environmental dynamics and differentiated service quality. Our approach is based on localized adaptive algorithms that self-configure to exploit both the local processing on each sensor node, as well as the redundancy across densely-deployed sensor nodes.
For the typical range-free localization algorithm, DV-Hop, we detailedly analyze the characteristics of localization errors utilizing the Cramer-Rao Bound theorem, and compute the errors of hop distance estimation theoretically and experimentally. Based on this work, an improved scheme for this algorithm is proposed. The principle of the improved scheme is to introduce the concept of beacons collinearity degree to the phase of beacons selection. Furthermore, in the implemention of the improved scheme, a way of chosing collinearity degree adaptively is put forward. Simulation results show that the improved scheme reduces 10 to 45 percents in average localization error, and 35 to 50 percents in variance of localization error.Based on the characteristics of the DV-Hop localization algorithm, an improved scheme for this typical range-free localization algorithm is proposed in this thesis. The principle of the improved scheme is to introduce the beacons collinearity degree to the phase of beacons selection. Furthermore, an adaptive collinearity degree based on the localized network topology and simulated annealing based location estimates are proposed. Not only the topology of beacons but also the relation of unknown nodes and beacons are considered in the phase of beacons selection. Through simulation studies, we demonstrate that the improved algorithm is more reliable and robust to irregular network topology than previous algorithm, especially when the beacon ratio is relatively low or the topology is sparse.
A novel distributed acquiring sensors positions approach approach based on multi-hop beacon nodesto the localization of sensors in wireless sensor networks is proposed in this thesis. The principles of proposed algorithm are acquiring the beacons utilizing the distance vector routing scheme, and then selecting some beacons as references according to the collinearity of beacons and the relative location relation of the self-node and beacons. Finally, a weighted-based location estimate strategy is utilized, which is independent of the complex optimize computation. The extensive simulation study shows that the proposed algorithm is self-adaptive, distributed, and scalable, and robust, especially the computation complexity and the robustness. It also exhibits fine performances on computation complexity and variance of localization, which is suitable for the node localization in large-scale wireless sensor network. As the network density varies form 4 to 14, the maximum localization errors and variance reduce form 1/6 to 1/2, 1/3 to 1/2 respectively, comparing to DV-Hop.
Fine-Grained localization algorithm is also discussed. As Introudcing the rigidity graph theory into the localization problem, a category based on the localizable corroborative body is proposed. A novel localization algorithm based on the localizable corroborative body is proposed in this thesis. The advantage of this strategy is that the localization information of the multi-hop beacons can be utilized as well as spread and accumulation of localization error are avoided. In this dissertation, we addressed the theorem foundation of the proposed algorithm and the algorithm for the construction of localizable corroborative body. Simulation results show that the proposed algorithm is self-adaptive, distributed, scalable and robustness. As the distance estimation error waries form 0.025 to 0.30 times communication range, the average localization error varies from 0.02 to 0.36 times communication range, It also exhibits fine performances on localization error, which is suitable for the node localization in large-scale wireless sensor network.
Based on the localization scheme described above, we firstly propound the concept of differential localizing services. That is, different QoS of localization are supported demand on the localization requisition and localization scene. In this way, the power consumption can be reduced greatly while keeping the requried localization accuracy, which proposes a novelty approach for sysmetical solution of localization in wireless sensor networks.
This thesis finally summarizes the research work, and discusses those potential research topics in future.
无线传感器网络及其相关领域的研究引起了人们广泛的关注,主要包括各种网络协议、时间同步、协同信息处理、网络拓扑控制等。在众多相关研究领域中,传感器节点的位置信息作为网络无缝协调的基础成为了必需解决的关键问题之一。对无线传感器网络而言,设计一个可行的节点定位方案面临诸多挑战,主要表现为复杂的物理环境和有限的网络资源。
本文旨在探讨符合无线传感器网络特点和要求的分布式节点定位方法,主要工作概括如下:
本文探讨了大规模、自组织无线传感器网络实现节点定位的主要挑战。阐述了本文的选题背景及意义,综述了无线传感器网络的节点定位算法的研究进展。此外,论文简要介绍了无线传感器网络其他领域的主要研究现状。
针对典型的DV-Hop定位算法,详细分析了该算法特性,利用Cramer-Rao边界定理对定位误差特性从理论上进行了分析和探讨,然后从理论和实验两方面分析了算法中使用的跳距估计误差。在此基础上,本文提出了一种改进方法,其基本思想是根据导标节点和未知节点间的相互位置关系有选择性地利用导标节点,主要创新在于导标节点共线度概念的引入。在算法的实现中,提出了自适应共线度阈值确定方法。通过仿真实验对定位性能比较,改进算法较传统算法在平均误差和误差方差方面分别降低10~45%和35~50%。
针对无线传感器网络拓扑结构特点及经典无须测距定位方法的局限性,提出了分布式的多跳导标节点定位方法(Multi-Hop Beacon Based Localization,简称MHB定位方法)。该方法的主要创新在于应用距离矢量路由法获得邻近导标节点的同时,充分利用了三角形内点的特性及相关几何性质,在选择参与定位的导标节点集时考虑了导标节点共线度及未知节点与导标三角形的几何位置关系,并在此基础上提出了不依赖于复杂优化计算的扩展质心位置估算策略。MHB算法具有很好的自适应性、分布性和可扩展性,特别是在计算复杂度及定位精度鲁棒性等方面表现出了很好的性能。当网络密度从4到14变化过程中,该算法最大定位误差和定位误差方差分别为DV-Hop的1/6~1/2和1/3~1/2。
本文也探讨了高精度的节点定位方法,该方法的探讨是基于测距技术而展开的,刚性图理论为本方法提供了理论支撑。受随机图论中刚性图理论的启发,本文提出了基于定位协作体的节点定位方法,该方法主要思想是根据网络局部拓扑自适应形成可实现节点位置估计的定位协作体,然后通过优化计算实现未知节点的位置估计。这种基于协作模式的定位策略的主要优点在于充分利用了多跳导标节点的位置信息,同时还可有效避免了定位误差在网络中恶性传播和积累。论文重点描述了基于协作模式的节点定位方法的基本原理及定位协作体的生成算法。仿真结果表明,提出的定位策略具有较好的自适应性、鲁棒形和可靠性,当测距误差方差从0.025~0.30倍通信视距时,定位误差为0.02~0.36倍通信视距,该定位精度能满足多数场合下无线传感器网络对节点位置信息的精度要求。
在上述定位方法的研究基础上,本文分析了定位服务质量的相关问题,提出了区分定位服务策略,即在实际的应用中可以根据定位需求和定位场景提供不同服务质量的定位支持。这种策略的提出可以使定位算法在满足系统定位精度的前提下大大降低系统能耗,这为无线传感器网络定位问题提供了新的解决思路。
论文最后总结了所作的工作,并就进一步的研究方向进行了简单探讨。
Recent advances in micro-electro-mechanical system (MEMS) technology, wireless communications and digital electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered in short distances. These tiny sensor nodes which consist of sensing, data processing, and communicaiting components, leverage the idea of wireless sensor networks based on collaborative effort of a large number of nodes. There are many potential applications of sensor nodes of wireless sensor networks: military surveillance, environmental monitoring, traffic surveillance, medical treatment, building and structures monitoring, even anti-terrorism, etc.
Through distributed coordination, wireless sensor networks are expected to revolutionize the ways in which we understand and construct complex physical systems. Fundamental to such coordination is localization, or the ability to establish spatial relationships among objects. Certainly, a viable solution to the node localization problem imposes many challenges. At physical level, changes in the surrounding environment introduce error in the sensor measurements. At network level, nodes need to determine their locations in a reliable manner while operating under stringent constraints in computation, communication and energy resource.
In this dissertation, we discuss the distributed localization scheme suitable to wireless sensor networks. The main contributions of this dissertation are: this dissertation addresses the background of localization and the challenges involved in localization for very large, ad hoc deployed wireless sensor networks, summarizes the proposed localization algorithms. As well as, it briefly introduces other open research issues in wireless sensor networks.In this dissertation, we address the challenges involved in localization for very large, ad hoc deployed wireless sensor networks. Although several localization technologies have been proposed in the past few years, none currently satisfies all our requirements because no single localization system is simultaneously scalable, ad hoc deployable and accommodating of the hardware constraints of very small devices. Our thesis is that all these issues can be solved simultaneously by a self-configuring localization system that autonomously adapts to its environmental dynamics and differentiated service quality. Our approach is based on localized adaptive algorithms that self-configure to exploit both the local processing on each sensor node, as well as the redundancy across densely-deployed sensor nodes.
For the typical range-free localization algorithm, DV-Hop, we detailedly analyze the characteristics of localization errors utilizing the Cramer-Rao Bound theorem, and compute the errors of hop distance estimation theoretically and experimentally. Based on this work, an improved scheme for this algorithm is proposed. The principle of the improved scheme is to introduce the concept of beacons collinearity degree to the phase of beacons selection. Furthermore, in the implemention of the improved scheme, a way of chosing collinearity degree adaptively is put forward. Simulation results show that the improved scheme reduces 10 to 45 percents in average localization error, and 35 to 50 percents in variance of localization error.Based on the characteristics of the DV-Hop localization algorithm, an improved scheme for this typical range-free localization algorithm is proposed in this thesis. The principle of the improved scheme is to introduce the beacons collinearity degree to the phase of beacons selection. Furthermore, an adaptive collinearity degree based on the localized network topology and simulated annealing based location estimates are proposed. Not only the topology of beacons but also the relation of unknown nodes and beacons are considered in the phase of beacons selection. Through simulation studies, we demonstrate that the improved algorithm is more reliable and robust to irregular network topology than previous algorithm, especially when the beacon ratio is relatively low or the topology is sparse.
A novel distributed acquiring sensors positions approach approach based on multi-hop beacon nodesto the localization of sensors in wireless sensor networks is proposed in this thesis. The principles of proposed algorithm are acquiring the beacons utilizing the distance vector routing scheme, and then selecting some beacons as references according to the collinearity of beacons and the relative location relation of the self-node and beacons. Finally, a weighted-based location estimate strategy is utilized, which is independent of the complex optimize computation. The extensive simulation study shows that the proposed algorithm is self-adaptive, distributed, and scalable, and robust, especially the computation complexity and the robustness. It also exhibits fine performances on computation complexity and variance of localization, which is suitable for the node localization in large-scale wireless sensor network. As the network density varies form 4 to 14, the maximum localization errors and variance reduce form 1/6 to 1/2, 1/3 to 1/2 respectively, comparing to DV-Hop.
Fine-Grained localization algorithm is also discussed. As Introudcing the rigidity graph theory into the localization problem, a category based on the localizable corroborative body is proposed. A novel localization algorithm based on the localizable corroborative body is proposed in this thesis. The advantage of this strategy is that the localization information of the multi-hop beacons can be utilized as well as spread and accumulation of localization error are avoided. In this dissertation, we addressed the theorem foundation of the proposed algorithm and the algorithm for the construction of localizable corroborative body. Simulation results show that the proposed algorithm is self-adaptive, distributed, scalable and robustness. As the distance estimation error waries form 0.025 to 0.30 times communication range, the average localization error varies from 0.02 to 0.36 times communication range, It also exhibits fine performances on localization error, which is suitable for the node localization in large-scale wireless sensor network.
Based on the localization scheme described above, we firstly propound the concept of differential localizing services. That is, different QoS of localization are supported demand on the localization requisition and localization scene. In this way, the power consumption can be reduced greatly while keeping the requried localization accuracy, which proposes a novelty approach for sysmetical solution of localization in wireless sensor networks.
This thesis finally summarizes the research work, and discusses those potential research topics in future.
引文
[1] Kahn J. M., Katz R. H., Pister K. S. J. Next Century Challenges:Mobile Networking for Smart Dust. In: Proceedings of the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 99). WA, USA:ACM Press, 1999. 263~270
[2] Weiser M. The computer of the 21st century. Scientific American,1991,265 (3): 94~104
[3] 徐光佑,史元春,谢伟凯. 普适计算.计算机学报,2003,26(9):1042~1052
[4] Weiser M. Some Computer Science Issues in Ubiquitous Computing. Communications of t he ACM, 1993, 36 (7): 75~84
[5] 普适计算:无处不在的个性服务:http://tech.icxo.com/htmlnews/2005/-05/-25/ 601628.htm
[6] Akyildiz I. F., Su W., Sankarasubramaniam Y., et al. A survey on sensor networks. IEEE Communications Magazine, 2002, 40(8): 102~114
[7] 马祖长,孙怡宁,梅涛.无线传感器网络综述.通信学报.2004,25 (4):114~124
[8] 崔莉,鞠海玲,苗勇等.无线传感器网络研究进展.计算机研究与发展.2005,42(1):163~174
[9] Liu K., Wang S., Hu F., et al. Wireless Sensor Networks Solution for Space Environmental Monitoring. In: Proceeding of International Conference on Space Information Technology. Wuhan, China: SPIE, 2005, 4R1~4R5
[10] Peter C., Neil G. 21 Ideas for the 21st Century. Business Week. 1999, 30(8): 78~167
[11] Terry J. 10 Emerging Technologies that Will Change the World. MIT Enterprise Technology Review,2003, 106(1):33~49
[12] Robert D. The Future of Technology. Business Week, 2003, 25(8): 1~50
[13] Chee-Yee C, Kumar S P. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 2003, 91(8): 1247~1256
[14] Kumar S., Shepherd D. SensIT: Sensor information technology for the warfighter. In: Proceedings of the 4th International Conference on Information Fusion. 2001. TuC1-3~TuC1-9
[15] Asada G, Dong M, Lin T., et al. Wireless integrated network sensors (WINS) for tactical information systems. In: Proceedings of the 1998 European Solid State Circuits Conference. New York: ACM Press, 1998. 15~20
[16] Nagpal R, Organizing a Global Coordinate System from Local Information on anAmorphous Computer. Tech. Report AI Memo No. 1666, MIT Artificial Intelligence Laboratory, 1999
[17] Kahn J M, Katz R H, Pister K S. Next century challenges: mobile networking for "Smart Dust". In: Proceedings of the 5th Annual Joint ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM’99), Seattle, WA, USA: ACM Press, 1999. 271~278
[18] http://www.millennium.berkeley.edu/sensornets/
[19] Demirkol I., Ersoy C., Alagoz, F. MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 2006, 44(4): 115~121
[20] Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 2005, 3(3): 325~349
[21] http://www.nsfc.gov.cn
[22] Kostrzewski A A, Wang W, Jannson T P. WLAN visual sensor networking. Proceedings of the SPIE, 2002, 4787: 174~178
[23] Raghunathan V, Schurgers C, Park S, et al. Energy-aware wireless microsensor networks. IEEE Signal Processing Magazine, 2002, 19(2): 40~50
[24] Tanenbaum A S, Gamage C, Crispo B. Taking sensor networks from the lab to the jungle computer. 2006, 39(8): 98~100
[25] Jeffrey Hightower, Gaetano Borriello. Location systems for ubiquitous computing. IEEE Computer, August 2001, 34(8): 57~66
[26] 关沫,赵海,韩光洁,张文波. 普适计算中定位服务的参考点选择及误差分析.电子学报,2005,33(8):1425~4431
[27] Rappaport T. S., Reed J. H., Woerner B. D. Position location using wireless communications on highways of the future. IEEE Communication Magazine, 1996, 34(10): 33~41
[28] Chan Y T. A simple and efficient estimate for hyperbolic location. IEEE Trans on Signal Processing, 1994, 42 (8): 1095~115
[29] Savvides A, Han C, Srivastava M. Dynamic fine-grained localization in ad hoc networks of sensors. In: Proceedings of the 7th ACM International Conference on Mobile Computing and Networking (Mobicom). Rome, Italy: ACM press, 2001. 166~179
[30] Andy Harter, Andy Hopper, Pete Steggles, et al. The anatomy of a context-aware application. In: Proceedings of the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 99). Seattle, WA, USA: ACM Press, 1999. 59~68
[31] Tezcan H, Cayirci E, Coskun V. A distributed scheme for 3D space coverage intactical underwater sensor networks In: Proceedings of 2004 IEEE Military Communications Conference, MILCOM. 2004. 697~703
[32] Orpilla M, Palumbo J, Thomas S, et al. Geographically-aware, reliable multicast protocol for tactical sensor networks. In: Proceedings of 2005 IEEE Military Communications Conference. MILCOM. 2005. 590~595
[33] Nirupama Bulusu, Deborah Estrin, Lewis Girod et al. Scalable Coordination for Wireless Sensor Networks: Self-Configuring Localization Systems. In: Proceedings of the 6th International Symposium on Communication Theory and Applications (ISCTA 01). Ambleside, Lake District, UK, 2001. II-1797~II-1800
[34] Hong X., Xu K., Gerla M. Scalable routing protocols for mobile ad hoc networks. IEEE Network magazine, 2002, 16(4): 11-21
[35] Niculescu D. Communication paradigms for sensor networks. IEEE Communications Magazine, 2005, 43(3):116~122
[36] He T., Stankovic J. A., Lu C., et al. SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks. In: Proceedings of IEEE ICDCS ’03. Providence, RI, 2003.
[37] Ko Y. B., Vaidya N. H. Location-Aided Routing (LAR) in Mobile Ad Hoc Networks. Wireless Networks, 2000, 6(4), 307~321
[38] Xu Y., Heideman J., Estrin D. Geography-informed Energy Conservation for Ad Hoc Routing. In: Proceedings of MOBICOM’01. Rome, Italy: ACM Press, 2001. 70~84
[39] Elliott d K. GPS 原理与应用.邱致和王万义译. 北京: 电子工业出版社, 2002.
[40] 韩保民,欧吉坤,曲国庆. GPS 观测数据的模拟研究. 武汉大学学报(信息科学版), 2005, 30(03): 246~250
[41] Crossbow Corp. Wireless sensor networks getting started guide. Technical Report Document 7430-0022-05, Rev. B, August 2004.
[42] Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 2002, 1(4): 660~670
[43] Lee J. An Experiment on Performance Study of IEEE 802.15.4 Wireless Networks. In: Proceeding of the 10th IEEE Emerging Technologies and Factory Automation. Catania, Italy, 2005: 19~22
[44] Callaway E, Gorday P, Hester L, Gutierrez J A, et al. Home networking with IEEE 802.15.4: a developing standard for low-rate wireless personal area networks. IEEE Communications Magazine, 2002, 40(8): 70~77
[45] Huang H, Huang Y, Ding J. An implementation of battery-aware wireless sensor network using ZigBee for multimedia service. In: proceedings of 2006 InternationalConference on Consumer Electronics, 2006. ICCE’06. 2006. 369~370
[46] Bahl P. and Padmanabhan V. RADAR: An in-building RF-based user location and tracking system. In: Proc. of Infocom’2000, Tel Aviv, Israel, 2000. 775~584
[47] Theodore S. Rappapport. Wireless communications: Principles and practice. New Jersey: Prentice Hall, 1996. 193~194
[48] N. Priyantha, A. Chakraborthy, H. Balakrishnan. The cricket location-support system. In: Proc. of International Conference on Mobile Computing and Networking, Boston, M A: ACM Press, 2000. 32~43
[50] Bahl P., Padmanabhan N. Enhancements to the RADAR user location and tracking system. Technical Report MSR-TR-2000-12, Microsoft Research, Redmond, WA, USA, 2000
[51] Savvides A., Han C. C., Srivastava M. B. Dynamic fine-grained localization in ad-hoc networks of sensors. in Proceeding of Mobicom’2001. Rome, Italy: ACM Press. 2001. 166~179
[52] Kaplan E. Understanding GPS principles and application. Boston: Artech House, 1996. 98~100
[53] Girod L. Estrin D. Robust range estimation using acoustic and multi-modal sensing. In: proceedings of the IEEE/RJS international conference on intelligent robots and systems (IROS 2001). Maui, Hawaii, 2001. 1312~1320
[54] Fontana R., Gunderson S., Ultra wideband precision asset location system. In: proceedings of IEEE conference on ultra wideband systems and technologies. 2002. 147~150
[55] Hazas M. A novel broadband ultrasonic location system. In: proceedings of the 4th international conference on buquitous computing lecture notes in computer science, Goteborg, Sweden: Springer-Verlag, 2002. 264~280
[56] Hazas A., Ward A. A high performance privacy-oriented location system. In: proceedings of the 1st conference on pervasive computing and communications. 216~223
[57] Priyanha N., Miu A. H. Balacrishnan, S. Teller, The cricket compass for context-aware mobile applications. In: Proceedings of the ACM SIGMOBILE 8th annual international conference on mobile computing and networking. New York, USA: ACM Press, 2002. 32~43
[58] Wang H., Yip L., Maniezzo D., et al. A wireless time-synchoronized COTS sensor platform: application to beamforming. In: proceedings of IEEE CAS.
[59] Leang D, Kalis A. Smart SensorDVB: sensor network development boards with smart antennas. In: proceedings of 2004 International Conference on Communications,Circuits and Systems, New York, US. IEEE Press, 2004. 1476~1480
[60] Bulusu N, Heidemann J, Estrin D. GPS-Less low cost outdoor localization for very small devices. IEEE Personal Communications, 2000, 7(5):28~34
[61] Bulusu N. Self-Configuring localization systems [Ph.D. Thesis]. Los Angeles: University of California, 2002.
[62] Niculescu D, Nath B. DV based positioning in ad hoc networks. Journal of Telecommunication Systems, 2003, 22(1/4): 267~280
[63] Nicolescu D, Nath B. Ad-Hoc positioning systems (APS). In: Proc. of the 2001 IEEE Global Telecommunications Conference. San Antonio: IEEE Communications Society, 2001. 2926~2931
[64] He T, Huang CD, Blum BM, et al. Range-Free localization schemes in large scale sensor networks. In: Proc. of the 9th Annual Int’l Conf. on Mobile Computing and Networking. San Diego: ACM Press, 2003. 81~95
[65] Priyantha NB, Miu AKL, Balakrishnan H, et al. The cricket compass for context-aware mobile applications. In: Proc. of the 7th Annual International Conference on Mobile Computing and Networking. Rome: ACM Press, 2001. 1~14
[66] Doherty L, Pister KSJ, Ghaoui LE. Convex position estimation in wireless sensor networks. In: Proc. of the IEEE INFOCOM 2001. Anchorage, US: IEEE Computer and Communications Societies, 2001. 1655~1663
[67] Doherty L. Algorithms for position and data recovery in wireless sensor networks [MS. Thesis]. Berkeley: University of California, 2000.
[68] Shang Y, Ruml W, Zhang Y, Fromherz MPJ. Localization from mere connectivity. In: Proc. of the 4th ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing. Annapolis, US: ACM Press, 2003. 201~212
[69] Shang, Y., Ruml, W.; Zhang, Y; Fromherz, M., Localization from connectivity in sensor networks, IEEE Transactions on Parallel and Distributed Systems. 2004, 15(11): 961~974
[70] Harter A, Hopper A, Steggles P, et al. The anatomy of a context-aware application. In: Proc. of the 5th Annual ACM/IEEE Int’l Conf. on Mobile Computing and Networking. Seattle, US: ACM Press, 1999. 59~68
[71] Niculescu D, Nath B. Ad hoc positioning system (APS) using AoA. In: Proc. of the IEEE INFOCOM 2003. San Francisco: IEEE Computer and Communications Societies, 2003. 1734~1743
[72] Meguerdichian S, Slijepcevic S, Karayan V, Potkonjak M. Localized algorithms in wireless ad-hoc networks: Location discovery and sensor exposure. In: Proc. of the 2nd ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing. Long Beach:ACM Press, 2001. 106~116
[73] Shuguang C, Madan R. Joint Routing, MAC, and Link Layer Optimization in Sensor Networks with Energy Constraints. In: Proceedings of the IEEE International Conference on Communications (ICC), Sourh Korea: IEEE Press, 2005. 725~729
[74] Akkaya K, Younis M. An energy-aware QoS routing protocol for wireless sensor networks. In: Proceedings of the IEEE 23rd International Conference on Distributed Computing Systems Workshops. Piscataway, NJ, US:IEEE Press, 2005. 710~715
[75] Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 2005, 3(3): 325~349
[76] Al-Karaki J N, Kamal A E. Routing techniques in wireless sensor networks: a survey. IEEE Wireless Communications, 2004, 11(6): 6~28
[77] Braginsky D, Estrin D. Rumor routing algorithm for sensor networks. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, US: ACM Press, 2002. 22~31
[78] Cerpa A, Estrin D. ASCENT: adaptive self-configuring sensor networks topologies. IEEE Transactions on Mobile Computing, 2004, 3(3): 272~285
[79] Chang J-H, Tassiulas L. Energy conserving routing in wireless ad-hoc networks. In: Proceedings of IEEE INFOCOM2000. Piscataway, NJ, US:IEEE Press, 2000. 22~31
[80] Chang J-H, Tassiulas L. Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking, 2004, 12(4): 609~619.
[81] Chu M, Haussecker H, Feng Z. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. International Journal of High-Performance Computing Applications, 2002, 16(3): 90~110
[82] Intanagonwiwat C, Govindan R, Estrin D, et al. Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking, 2003, 11(1): 2~16
[83] Kumar S., Zhao F., and Shepherd D. Collaborative signal and information processing in microsensor networks. IEEE Signal Processing Magazine, 2002, 19(2): 13~14
[84] Zhao F., Shin J., Reich J. Information-driven dynamic sensor collaboration for tracking applications. IEEE Signal Processing Magagazine, 2002, 19(2): 61~72
[85] Hairong Qi, Yingyue Xu, Xiaoling Wang. Mobile-Agent-Based Collaborative Signal and Information Processing in Sensor Networks. Proceedings of the IEEE, 2003, 91(8):1172~1183
[86] Pradhan S S, Kusuma J, Ramchandran K. Distributed compression in a dense microsensor network. IEEE Signal Processing Magazine, 2002, 19(2): 51~60
[87] Chu M, Haussecker H, Feng Z. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. International Journal ofHigh-Performance Computing Applications, 2002, 16(3): 90~110
[88] Scaglione A, Servetto S. On the interdependence of routing and data compression in multi-hop sensor networks. Wireless Networks, 2005, 11(1-2): 149~160
[89] Scaglione A, Servetto S D. On the interdependence of routing and data compression in multi-hop sensor networks. In: Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (MOBICOM’02). Atlanta, GA, US: ACM Press, 2002. 140~147
[90] Elson J, Girod L, Estrin D. Fine-grained network time synchronization using reference broadcasts. In: Proceedings of 2002 Usenix Symposium on Operating Systems Design and Implementation (OSDI’02). Berkeley, CA, US:USENIX Assoc Press., 2002. 147~163
[91] Elson J, Romer K. Wireless sensor networks: a new regime for time synchronization. Computer Communication Review, 2003, 33(1): 149~154
[92] Sichitiu M L, Veerarittiphan C. Simple, accurate time synchronization for wireless sensor networks. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC’03). Piscataway, NJ, US:IEEE Press, 2003. 1266~1273
[93] Sivrikaya F, Yener B. Time synchronization in sensor networks: A survey. IEEE Network, 2004, 18(4): 45~50
[94] Su W, Akyildiz I F. Time-diffusion synchronization protocol for wireless sensor networks. IEEE/ACM Transactions on Networking, 2005, 13(2): 384~397
[95] Philips T. K., Panwar S. S., Tantawi A. N. Connectivity Properties of a Packet Radio Network Model. IEEE Transaction on Information Theory, 1989, 35(5): 1044~1047
[96] Pottie G. J., Kaiser W. J. Wireless Integrated Network Sensors. Communications of the ACM, 2000, 43(5): 51~58
[97] Ramanathan R., Rosales-Hain R. Topology Control of Multihop Wireless Networks using Transmit Power Adjustment. In: Proc. IEEE Infocom 2000, Tel Aviv: ACM press, 2000. 404~413
[98] Rodoplu V., Meng T. H. Minimum Energy Mobile Wireless Networks. IEEE Journal of Selected Areas in Comm., 1999, 17(8): 1333~1344
[99] Santi P., Blough D. M., Vainstein F. A Probabilistic Analysis for the Range Assignment Problem in Ad Hoc Networks. In: Proc. of ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Long Beach CA: ACM Press, 2001. 212~220
[100] Cheng Y.-C, Robertazzi T. G.. Critical connectivity phenomena in multihop radio models. IEEE Transaction on Communications, 1989, 37(7): 770~777
[101] Philips T. K., Panwar S. S., Tantawi A. N. Connectivity properties of a packet radionetwork model. IEEE Transaction on Information Theory, 1989, 35(5): 1044~1047
[102] 王树和, 图论及其算法,合肥:中国科学技术大学出版社, 1990. 47~48
[103] Bettstetter C., On the Minimum Node Degree and Connectivity of a Wireless Multi-hop Network, MOBIHOC’02. New York, US: ACM Press, 2002. 80~91
[104] Chlamtac I., Farago A. A new approach to the design and analysis of peer-to-peer mobile networks, ACM Wireless Networks, 1999, 5(3): 149~156
[105] Park S., Savvides A., Srivastava M.B. SensorSim: a simulation framework for sensor networks. In: Proceedings of the 3rd ACM International DRAFT workshop on Modeling, analysis and simulation of wireless and mobile systems. New York, US: ACM Press, 2000. 104~111
[106] Vargas A. OMNET++ Discrete Event Simulation System. In Proc. of the 15th European Simulation Multiconference. Prague, Czech Republic: European Publishing House, 2001. 319~324
[107] Zeng X., Bagrodia R., Gerla M. Glomosim: A library for parallel simulation of large-scale wireless networks. In Workshop on Parallel and Distributed Simulation. Los Alamitos, CA, USA: IEEE Comp Soc., 1998. 154~161
[108] Eren T., Whiteley W., Morse A. S., et al. Sensor and network topologies of formations with distance-direction-angle constraints. In: proceedings of the 42nd IEEE Conference on Decision and Control. Hawaii, 2002. 3064~3069
[109] Eren T., Goldenberg D., Whiteley W., et al. Computation, and Randomization in Network Localization. In: IEEE INFOCOM, Piscataway, NJ, US: IEEE Press, 2004. 2673~2684
[110] Laman G.. On graphs and rigidity of plane skeletal structures. Journal of Engineering Mathematics, 2002, 4(1): 331~340
[111] Bill J., Tibor J. Connected rigidity matroids and unique realizations of graphs. Tech. Rep. TR-2002-12, Eotvos University, Budapest, Hungary, 2003
[112] Langendoen K., Reijers N. Distributed localization in wireless sensor networks: a quantitative comparison. Journal of Computer Networks, 2003, 43(1): 499~518
[113] Wattenhofer R., Bahl P. Li L., et al. Distributed Topology Control for Power Efficient Operation in Multihop Wireless Ad Hoc Networks. In: Proceedings of INFOCOM. Piscataway, NJ, US: IEEE Press, 2001. 1388~1397
[114] Ramanathan R. and Rosales-Hain R. Topology control of multihop wireless networks using transmit power adjustment. In: Proceedings of INFOCOM. Piscataway, NJ, US: IEEE Press, 2000. 404~413
[115] Santi P., Blough D. M., Vainstein F. A Probabilistic Analysis for the Range Assignment Problem in Ad Hoc Networks. In: Proceedings of MobiHoc. Long Beach:Association for Computing Machinery, 2001. 212~220
[116] Rodoplu V., Meng T. H.. Minimum energy mobile wireless networks. IEEE Journal on Selected Areas in Communications, 1999, 17(8): 1333~1344
[2] Weiser M. The computer of the 21st century. Scientific American,1991,265 (3): 94~104
[3] 徐光佑,史元春,谢伟凯. 普适计算.计算机学报,2003,26(9):1042~1052
[4] Weiser M. Some Computer Science Issues in Ubiquitous Computing. Communications of t he ACM, 1993, 36 (7): 75~84
[5] 普适计算:无处不在的个性服务:http://tech.icxo.com/htmlnews/2005/-05/-25/ 601628.htm
[6] Akyildiz I. F., Su W., Sankarasubramaniam Y., et al. A survey on sensor networks. IEEE Communications Magazine, 2002, 40(8): 102~114
[7] 马祖长,孙怡宁,梅涛.无线传感器网络综述.通信学报.2004,25 (4):114~124
[8] 崔莉,鞠海玲,苗勇等.无线传感器网络研究进展.计算机研究与发展.2005,42(1):163~174
[9] Liu K., Wang S., Hu F., et al. Wireless Sensor Networks Solution for Space Environmental Monitoring. In: Proceeding of International Conference on Space Information Technology. Wuhan, China: SPIE, 2005, 4R1~4R5
[10] Peter C., Neil G. 21 Ideas for the 21st Century. Business Week. 1999, 30(8): 78~167
[11] Terry J. 10 Emerging Technologies that Will Change the World. MIT Enterprise Technology Review,2003, 106(1):33~49
[12] Robert D. The Future of Technology. Business Week, 2003, 25(8): 1~50
[13] Chee-Yee C, Kumar S P. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 2003, 91(8): 1247~1256
[14] Kumar S., Shepherd D. SensIT: Sensor information technology for the warfighter. In: Proceedings of the 4th International Conference on Information Fusion. 2001. TuC1-3~TuC1-9
[15] Asada G, Dong M, Lin T., et al. Wireless integrated network sensors (WINS) for tactical information systems. In: Proceedings of the 1998 European Solid State Circuits Conference. New York: ACM Press, 1998. 15~20
[16] Nagpal R, Organizing a Global Coordinate System from Local Information on anAmorphous Computer. Tech. Report AI Memo No. 1666, MIT Artificial Intelligence Laboratory, 1999
[17] Kahn J M, Katz R H, Pister K S. Next century challenges: mobile networking for "Smart Dust". In: Proceedings of the 5th Annual Joint ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM’99), Seattle, WA, USA: ACM Press, 1999. 271~278
[18] http://www.millennium.berkeley.edu/sensornets/
[19] Demirkol I., Ersoy C., Alagoz, F. MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 2006, 44(4): 115~121
[20] Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 2005, 3(3): 325~349
[21] http://www.nsfc.gov.cn
[22] Kostrzewski A A, Wang W, Jannson T P. WLAN visual sensor networking. Proceedings of the SPIE, 2002, 4787: 174~178
[23] Raghunathan V, Schurgers C, Park S, et al. Energy-aware wireless microsensor networks. IEEE Signal Processing Magazine, 2002, 19(2): 40~50
[24] Tanenbaum A S, Gamage C, Crispo B. Taking sensor networks from the lab to the jungle computer. 2006, 39(8): 98~100
[25] Jeffrey Hightower, Gaetano Borriello. Location systems for ubiquitous computing. IEEE Computer, August 2001, 34(8): 57~66
[26] 关沫,赵海,韩光洁,张文波. 普适计算中定位服务的参考点选择及误差分析.电子学报,2005,33(8):1425~4431
[27] Rappaport T. S., Reed J. H., Woerner B. D. Position location using wireless communications on highways of the future. IEEE Communication Magazine, 1996, 34(10): 33~41
[28] Chan Y T. A simple and efficient estimate for hyperbolic location. IEEE Trans on Signal Processing, 1994, 42 (8): 1095~115
[29] Savvides A, Han C, Srivastava M. Dynamic fine-grained localization in ad hoc networks of sensors. In: Proceedings of the 7th ACM International Conference on Mobile Computing and Networking (Mobicom). Rome, Italy: ACM press, 2001. 166~179
[30] Andy Harter, Andy Hopper, Pete Steggles, et al. The anatomy of a context-aware application. In: Proceedings of the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 99). Seattle, WA, USA: ACM Press, 1999. 59~68
[31] Tezcan H, Cayirci E, Coskun V. A distributed scheme for 3D space coverage intactical underwater sensor networks In: Proceedings of 2004 IEEE Military Communications Conference, MILCOM. 2004. 697~703
[32] Orpilla M, Palumbo J, Thomas S, et al. Geographically-aware, reliable multicast protocol for tactical sensor networks. In: Proceedings of 2005 IEEE Military Communications Conference. MILCOM. 2005. 590~595
[33] Nirupama Bulusu, Deborah Estrin, Lewis Girod et al. Scalable Coordination for Wireless Sensor Networks: Self-Configuring Localization Systems. In: Proceedings of the 6th International Symposium on Communication Theory and Applications (ISCTA 01). Ambleside, Lake District, UK, 2001. II-1797~II-1800
[34] Hong X., Xu K., Gerla M. Scalable routing protocols for mobile ad hoc networks. IEEE Network magazine, 2002, 16(4): 11-21
[35] Niculescu D. Communication paradigms for sensor networks. IEEE Communications Magazine, 2005, 43(3):116~122
[36] He T., Stankovic J. A., Lu C., et al. SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks. In: Proceedings of IEEE ICDCS ’03. Providence, RI, 2003.
[37] Ko Y. B., Vaidya N. H. Location-Aided Routing (LAR) in Mobile Ad Hoc Networks. Wireless Networks, 2000, 6(4), 307~321
[38] Xu Y., Heideman J., Estrin D. Geography-informed Energy Conservation for Ad Hoc Routing. In: Proceedings of MOBICOM’01. Rome, Italy: ACM Press, 2001. 70~84
[39] Elliott d K. GPS 原理与应用.邱致和王万义译. 北京: 电子工业出版社, 2002.
[40] 韩保民,欧吉坤,曲国庆. GPS 观测数据的模拟研究. 武汉大学学报(信息科学版), 2005, 30(03): 246~250
[41] Crossbow Corp. Wireless sensor networks getting started guide. Technical Report Document 7430-0022-05, Rev. B, August 2004.
[42] Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 2002, 1(4): 660~670
[43] Lee J. An Experiment on Performance Study of IEEE 802.15.4 Wireless Networks. In: Proceeding of the 10th IEEE Emerging Technologies and Factory Automation. Catania, Italy, 2005: 19~22
[44] Callaway E, Gorday P, Hester L, Gutierrez J A, et al. Home networking with IEEE 802.15.4: a developing standard for low-rate wireless personal area networks. IEEE Communications Magazine, 2002, 40(8): 70~77
[45] Huang H, Huang Y, Ding J. An implementation of battery-aware wireless sensor network using ZigBee for multimedia service. In: proceedings of 2006 InternationalConference on Consumer Electronics, 2006. ICCE’06. 2006. 369~370
[46] Bahl P. and Padmanabhan V. RADAR: An in-building RF-based user location and tracking system. In: Proc. of Infocom’2000, Tel Aviv, Israel, 2000. 775~584
[47] Theodore S. Rappapport. Wireless communications: Principles and practice. New Jersey: Prentice Hall, 1996. 193~194
[48] N. Priyantha, A. Chakraborthy, H. Balakrishnan. The cricket location-support system. In: Proc. of International Conference on Mobile Computing and Networking, Boston, M A: ACM Press, 2000. 32~43
[50] Bahl P., Padmanabhan N. Enhancements to the RADAR user location and tracking system. Technical Report MSR-TR-2000-12, Microsoft Research, Redmond, WA, USA, 2000
[51] Savvides A., Han C. C., Srivastava M. B. Dynamic fine-grained localization in ad-hoc networks of sensors. in Proceeding of Mobicom’2001. Rome, Italy: ACM Press. 2001. 166~179
[52] Kaplan E. Understanding GPS principles and application. Boston: Artech House, 1996. 98~100
[53] Girod L. Estrin D. Robust range estimation using acoustic and multi-modal sensing. In: proceedings of the IEEE/RJS international conference on intelligent robots and systems (IROS 2001). Maui, Hawaii, 2001. 1312~1320
[54] Fontana R., Gunderson S., Ultra wideband precision asset location system. In: proceedings of IEEE conference on ultra wideband systems and technologies. 2002. 147~150
[55] Hazas M. A novel broadband ultrasonic location system. In: proceedings of the 4th international conference on buquitous computing lecture notes in computer science, Goteborg, Sweden: Springer-Verlag, 2002. 264~280
[56] Hazas A., Ward A. A high performance privacy-oriented location system. In: proceedings of the 1st conference on pervasive computing and communications. 216~223
[57] Priyanha N., Miu A. H. Balacrishnan, S. Teller, The cricket compass for context-aware mobile applications. In: Proceedings of the ACM SIGMOBILE 8th annual international conference on mobile computing and networking. New York, USA: ACM Press, 2002. 32~43
[58] Wang H., Yip L., Maniezzo D., et al. A wireless time-synchoronized COTS sensor platform: application to beamforming. In: proceedings of IEEE CAS.
[59] Leang D, Kalis A. Smart SensorDVB: sensor network development boards with smart antennas. In: proceedings of 2004 International Conference on Communications,Circuits and Systems, New York, US. IEEE Press, 2004. 1476~1480
[60] Bulusu N, Heidemann J, Estrin D. GPS-Less low cost outdoor localization for very small devices. IEEE Personal Communications, 2000, 7(5):28~34
[61] Bulusu N. Self-Configuring localization systems [Ph.D. Thesis]. Los Angeles: University of California, 2002.
[62] Niculescu D, Nath B. DV based positioning in ad hoc networks. Journal of Telecommunication Systems, 2003, 22(1/4): 267~280
[63] Nicolescu D, Nath B. Ad-Hoc positioning systems (APS). In: Proc. of the 2001 IEEE Global Telecommunications Conference. San Antonio: IEEE Communications Society, 2001. 2926~2931
[64] He T, Huang CD, Blum BM, et al. Range-Free localization schemes in large scale sensor networks. In: Proc. of the 9th Annual Int’l Conf. on Mobile Computing and Networking. San Diego: ACM Press, 2003. 81~95
[65] Priyantha NB, Miu AKL, Balakrishnan H, et al. The cricket compass for context-aware mobile applications. In: Proc. of the 7th Annual International Conference on Mobile Computing and Networking. Rome: ACM Press, 2001. 1~14
[66] Doherty L, Pister KSJ, Ghaoui LE. Convex position estimation in wireless sensor networks. In: Proc. of the IEEE INFOCOM 2001. Anchorage, US: IEEE Computer and Communications Societies, 2001. 1655~1663
[67] Doherty L. Algorithms for position and data recovery in wireless sensor networks [MS. Thesis]. Berkeley: University of California, 2000.
[68] Shang Y, Ruml W, Zhang Y, Fromherz MPJ. Localization from mere connectivity. In: Proc. of the 4th ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing. Annapolis, US: ACM Press, 2003. 201~212
[69] Shang, Y., Ruml, W.; Zhang, Y; Fromherz, M., Localization from connectivity in sensor networks, IEEE Transactions on Parallel and Distributed Systems. 2004, 15(11): 961~974
[70] Harter A, Hopper A, Steggles P, et al. The anatomy of a context-aware application. In: Proc. of the 5th Annual ACM/IEEE Int’l Conf. on Mobile Computing and Networking. Seattle, US: ACM Press, 1999. 59~68
[71] Niculescu D, Nath B. Ad hoc positioning system (APS) using AoA. In: Proc. of the IEEE INFOCOM 2003. San Francisco: IEEE Computer and Communications Societies, 2003. 1734~1743
[72] Meguerdichian S, Slijepcevic S, Karayan V, Potkonjak M. Localized algorithms in wireless ad-hoc networks: Location discovery and sensor exposure. In: Proc. of the 2nd ACM Int’l Symp. on Mobile Ad Hoc Networking & Computing. Long Beach:ACM Press, 2001. 106~116
[73] Shuguang C, Madan R. Joint Routing, MAC, and Link Layer Optimization in Sensor Networks with Energy Constraints. In: Proceedings of the IEEE International Conference on Communications (ICC), Sourh Korea: IEEE Press, 2005. 725~729
[74] Akkaya K, Younis M. An energy-aware QoS routing protocol for wireless sensor networks. In: Proceedings of the IEEE 23rd International Conference on Distributed Computing Systems Workshops. Piscataway, NJ, US:IEEE Press, 2005. 710~715
[75] Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 2005, 3(3): 325~349
[76] Al-Karaki J N, Kamal A E. Routing techniques in wireless sensor networks: a survey. IEEE Wireless Communications, 2004, 11(6): 6~28
[77] Braginsky D, Estrin D. Rumor routing algorithm for sensor networks. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, US: ACM Press, 2002. 22~31
[78] Cerpa A, Estrin D. ASCENT: adaptive self-configuring sensor networks topologies. IEEE Transactions on Mobile Computing, 2004, 3(3): 272~285
[79] Chang J-H, Tassiulas L. Energy conserving routing in wireless ad-hoc networks. In: Proceedings of IEEE INFOCOM2000. Piscataway, NJ, US:IEEE Press, 2000. 22~31
[80] Chang J-H, Tassiulas L. Maximum lifetime routing in wireless sensor networks. IEEE/ACM Transactions on Networking, 2004, 12(4): 609~619.
[81] Chu M, Haussecker H, Feng Z. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. International Journal of High-Performance Computing Applications, 2002, 16(3): 90~110
[82] Intanagonwiwat C, Govindan R, Estrin D, et al. Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking, 2003, 11(1): 2~16
[83] Kumar S., Zhao F., and Shepherd D. Collaborative signal and information processing in microsensor networks. IEEE Signal Processing Magazine, 2002, 19(2): 13~14
[84] Zhao F., Shin J., Reich J. Information-driven dynamic sensor collaboration for tracking applications. IEEE Signal Processing Magagazine, 2002, 19(2): 61~72
[85] Hairong Qi, Yingyue Xu, Xiaoling Wang. Mobile-Agent-Based Collaborative Signal and Information Processing in Sensor Networks. Proceedings of the IEEE, 2003, 91(8):1172~1183
[86] Pradhan S S, Kusuma J, Ramchandran K. Distributed compression in a dense microsensor network. IEEE Signal Processing Magazine, 2002, 19(2): 51~60
[87] Chu M, Haussecker H, Feng Z. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. International Journal ofHigh-Performance Computing Applications, 2002, 16(3): 90~110
[88] Scaglione A, Servetto S. On the interdependence of routing and data compression in multi-hop sensor networks. Wireless Networks, 2005, 11(1-2): 149~160
[89] Scaglione A, Servetto S D. On the interdependence of routing and data compression in multi-hop sensor networks. In: Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (MOBICOM’02). Atlanta, GA, US: ACM Press, 2002. 140~147
[90] Elson J, Girod L, Estrin D. Fine-grained network time synchronization using reference broadcasts. In: Proceedings of 2002 Usenix Symposium on Operating Systems Design and Implementation (OSDI’02). Berkeley, CA, US:USENIX Assoc Press., 2002. 147~163
[91] Elson J, Romer K. Wireless sensor networks: a new regime for time synchronization. Computer Communication Review, 2003, 33(1): 149~154
[92] Sichitiu M L, Veerarittiphan C. Simple, accurate time synchronization for wireless sensor networks. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC’03). Piscataway, NJ, US:IEEE Press, 2003. 1266~1273
[93] Sivrikaya F, Yener B. Time synchronization in sensor networks: A survey. IEEE Network, 2004, 18(4): 45~50
[94] Su W, Akyildiz I F. Time-diffusion synchronization protocol for wireless sensor networks. IEEE/ACM Transactions on Networking, 2005, 13(2): 384~397
[95] Philips T. K., Panwar S. S., Tantawi A. N. Connectivity Properties of a Packet Radio Network Model. IEEE Transaction on Information Theory, 1989, 35(5): 1044~1047
[96] Pottie G. J., Kaiser W. J. Wireless Integrated Network Sensors. Communications of the ACM, 2000, 43(5): 51~58
[97] Ramanathan R., Rosales-Hain R. Topology Control of Multihop Wireless Networks using Transmit Power Adjustment. In: Proc. IEEE Infocom 2000, Tel Aviv: ACM press, 2000. 404~413
[98] Rodoplu V., Meng T. H. Minimum Energy Mobile Wireless Networks. IEEE Journal of Selected Areas in Comm., 1999, 17(8): 1333~1344
[99] Santi P., Blough D. M., Vainstein F. A Probabilistic Analysis for the Range Assignment Problem in Ad Hoc Networks. In: Proc. of ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Long Beach CA: ACM Press, 2001. 212~220
[100] Cheng Y.-C, Robertazzi T. G.. Critical connectivity phenomena in multihop radio models. IEEE Transaction on Communications, 1989, 37(7): 770~777
[101] Philips T. K., Panwar S. S., Tantawi A. N. Connectivity properties of a packet radionetwork model. IEEE Transaction on Information Theory, 1989, 35(5): 1044~1047
[102] 王树和, 图论及其算法,合肥:中国科学技术大学出版社, 1990. 47~48
[103] Bettstetter C., On the Minimum Node Degree and Connectivity of a Wireless Multi-hop Network, MOBIHOC’02. New York, US: ACM Press, 2002. 80~91
[104] Chlamtac I., Farago A. A new approach to the design and analysis of peer-to-peer mobile networks, ACM Wireless Networks, 1999, 5(3): 149~156
[105] Park S., Savvides A., Srivastava M.B. SensorSim: a simulation framework for sensor networks. In: Proceedings of the 3rd ACM International DRAFT workshop on Modeling, analysis and simulation of wireless and mobile systems. New York, US: ACM Press, 2000. 104~111
[106] Vargas A. OMNET++ Discrete Event Simulation System. In Proc. of the 15th European Simulation Multiconference. Prague, Czech Republic: European Publishing House, 2001. 319~324
[107] Zeng X., Bagrodia R., Gerla M. Glomosim: A library for parallel simulation of large-scale wireless networks. In Workshop on Parallel and Distributed Simulation. Los Alamitos, CA, USA: IEEE Comp Soc., 1998. 154~161
[108] Eren T., Whiteley W., Morse A. S., et al. Sensor and network topologies of formations with distance-direction-angle constraints. In: proceedings of the 42nd IEEE Conference on Decision and Control. Hawaii, 2002. 3064~3069
[109] Eren T., Goldenberg D., Whiteley W., et al. Computation, and Randomization in Network Localization. In: IEEE INFOCOM, Piscataway, NJ, US: IEEE Press, 2004. 2673~2684
[110] Laman G.. On graphs and rigidity of plane skeletal structures. Journal of Engineering Mathematics, 2002, 4(1): 331~340
[111] Bill J., Tibor J. Connected rigidity matroids and unique realizations of graphs. Tech. Rep. TR-2002-12, Eotvos University, Budapest, Hungary, 2003
[112] Langendoen K., Reijers N. Distributed localization in wireless sensor networks: a quantitative comparison. Journal of Computer Networks, 2003, 43(1): 499~518
[113] Wattenhofer R., Bahl P. Li L., et al. Distributed Topology Control for Power Efficient Operation in Multihop Wireless Ad Hoc Networks. In: Proceedings of INFOCOM. Piscataway, NJ, US: IEEE Press, 2001. 1388~1397
[114] Ramanathan R. and Rosales-Hain R. Topology control of multihop wireless networks using transmit power adjustment. In: Proceedings of INFOCOM. Piscataway, NJ, US: IEEE Press, 2000. 404~413
[115] Santi P., Blough D. M., Vainstein F. A Probabilistic Analysis for the Range Assignment Problem in Ad Hoc Networks. In: Proceedings of MobiHoc. Long Beach:Association for Computing Machinery, 2001. 212~220
[116] Rodoplu V., Meng T. H.. Minimum energy mobile wireless networks. IEEE Journal on Selected Areas in Communications, 1999, 17(8): 1333~1344