军事应用中无线传感器网络的定位、数据分发和收集方法研究
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摘要
无线传感器网络(也简称为传感器网络)是由多个传感器节点通过无线自组织的方式形成的网络。它具有隐蔽性强、可快速部署、可自组织、成本低廉、受天气影响小等优点,将成为未来军事信息系统中不可或缺的一部分,能用来获取丰富的战场态势数据,为占据信息优势提供强有力的支持。目前,我军对无线传感器网络的研究还处于起步阶段,该方向大部分公开的研究成果都集中在民用领域。在军事应用中照搬这些成果,将无法达到所期望的效果。本文分析了军事应用对无线传感器网络带来的要求和挑战,关注无线传感器网络在军事应用中所面临的几项技术难题,包括面向定位的网络调整、数据分发、非实时和实时数据收集。针对这四个问题,本文以降低无线传感器网络的能量消耗、延长网络的工作寿命、提高网络的应变能力和工作的可靠性、强化感知数据传输到用户的实时性为目的,分别展开了研究。
本文首先提出了可定位性辅助下的网络调整方法LAL,使得调整后的网络中所有节点都可被定位。定位是无线传感器网络应用中的一项关键服务,但所有的定位算法都无法确保能定位网络中的所有节点。实际应用中部署的无线传感器网络和大量仿真实验都表明,网络中可能存在一部分节点是理论上无法被定位的。为了定位网络中所有节点,主要的手段是通过改变网络的设置,使整个网络满足可被定位的要求。已往的调整方法没有区分可被定位节点和不可被定位节点,因此在调整粒度上比较粗,引入了大量不必要的网络调整和与之相伴的各种开销。与已有的方法相比,LAL利用了节点可定位性信息,具有更高的效率,降低了定位时测距所需的能量消耗,延长了网络的工作寿命。在LAL调整后的网络中,仍存在一些在定位中不需要的边。为了逼近最优结果,本文进一步提出了基于路径的调整方法,在强化了部分假设的前提下,将调整具有2-连通性网络时需要添加的边的数量限制在2|V_N|以内,其中|V_N|为网络中不可被定位节点的数量。
军事应用的需求可能随着战场态势的变化而改变,这就需要数据分发协议通过分发配置参数,对传感器网络中各节点的行为进行方便快捷的控制。数据分发的关键是如何保证全网数据版本的一致性。根据系统的需求变化,可能需要在网络中进行多次数据分发。此时,为了确保所有节点行为的一致性,必须保证所有节点上的参数都被更新到最新的版本。本文提出了一种基于Bloom滤波的无线传感器网络数据分发协议BDP。BDP采用Bloom滤波这一数据结构作为压缩存储数据项元数据信息的工具,能快速识别出不同节点上具有相同键值的数据项间的版本差异,加快更新的过程。通过分析节点交互过程以及协议实现的细节,本文提出了能约束假阳性误判概率的解决方案,将其控制在比较低的水平,从而能以比较高的概率保证全网范围内数据项的版本一致性。
在对实时性要求不高的应用中,或部署条件受限的情况下,可以将传感器网络相对孤立的部署在无人看管的区域。经过一段时间后,再利用无人飞行器等移动设备收集网络中的感知数据。这种非实时的数据收集方式在军事应用中存在两方面的挑战:一是节点容易损毁,造成感知数据的丢失;二是数据收集时容易造成节点负载分布不均,部分节点容易很快耗尽能量停止工作。对前者,本文提出了一种基于虚拟节点的分布式数据存储方法,利用Fountain编码和随机行走在网络内散步数据。该方法不仅保留了编码机制的优点,还能充分利用网络中所有的传感器节点进行感知和存储,大大提高了已有方法对节点的利用效率。即使网络中大部分传感器节点意外损毁,用户仍可从剩余节点上编码过的数据恢复出网络被损毁之前所有节点感知到的原始数据。对后者,本文提出了一种使用移动基站和单跳通信机制的数据收集方法。首先在网络中分布式构造一个支配集,然后移动基站沿一条较短的路径经过所有支配集中的节点。支配集中节点的位置作为基站的检查点。基站在每个检查点处以单跳通信的方式获取通信范围内节点的感知数据。相比已有方法,该方法具有很低的复杂度和很高的实用性,大大降低了多跳路由带来的通信开销,并平衡了各节点的负载,避免了网络中出现少量负载较重的“热点”。
在对实时性要求比较高的应用中,为了延长传感器网络的工作寿命,节点通常采用周期性工作机制,即控制节点在睡眠状态和工作状态间进行循环切换,从而取得实时性和能耗两者间的平衡。节点间的状态切换通常也不是同步的。现有的异步周期性工作机制下的数据收集方法多依赖于时间同步机制,既占用了宝贵的程序体积,又带来了额外的通信开销。此外,这些方法的性能也严重依赖于时间同步协议的精度和稳定性。本文尝试摆脱对时间同步协议的依赖,利用多跳节点局部范围内的信息共享和协同,发现由多个同时处于工作状态的节点所组成的序列,将其作为快速传递数据的捷径。这种方法降低了数据收集的时间延迟,提高了数据的实时性。
Wireless sensor networks (also known as sensor networks, or WSNs) are the net-works that consist of multiple sensor nodes. These nodes are self-organized with wirelesscommunications. Sensor networks have a number of advantages, such as good obscurity,easy deployment, self-organization, low cost and invulnerability to bad weathers. Theywill be used to collect all kinds of battlefield situation information to achieve informationdominance, and play an important role in future military information system. However,the research on sensor networks has just started recently in our army. Many publicationsonsensornetworksfocusoncivilapplications. Ifthetechnologiesincivilapplicationsarecopiedtomilitaryones, theymaynotperformwellasanticipated. Thisthesisanalyzestherequirements and challenges of sensor networks in military applications, and focuses onsome associated technical problems, including localization-oriented network adjustment,data dissemination, non-real-time and real-time data collection. In order to reduce theenergy consumption, improve the network lifetime, adaptability and reliability, and con-solidate the real time property of data routing from sensors to users, this thesis researchthe aforementioned four problems, respectively.
The thesis first proposes a localizability-aided network adjustment method namedLAL, after the execution of which all nodes can be localized. Localization is an importantservice in sensor network applications, however, no localization algorithm can guaranteethat all nodes in the network be localized. Facts in both working sensor networks and ex-tensive simulations have shown that part of the network may not be localizable in theory.To localize those nodes, the primary solution is to change the network configurations soas to make the whole network localizable. Previous adjustment methods areconsidered tobecoarse-grained, sincetheydonotdistinguishthelocalizablenodeswithnon-localizableones, which induce much unnecessary adjustment and accompanying overhead. In con-trast, LAL utilizes node localizability information, improves the efficiency of adjustmentandreducesdistancemeasurementoverheadandenergyconsumptioninlocalization. Thisalso helps to extend the network lifetime. After the adjustment of LAL, there may still ex-ist some unnecessary edges in the network. To approach optimal results, the thesis bringsforward a path-based adjustment method. With some additional hypothesis, the methodcan constrain the number of edges added into the network below 2|V_N|, where |V_N| is the number of non-localizable nodes in the network.
Therequirementsofmilitaryapplicationsmayvaryasthebattlefieldsituationchanges.This motivates the need to disseminate configuration parameters, so as to control the op-erations of sensor nodes quickly and conveniently. The key of dissemination is how tokeep the consistence of the operations of all nodes. According to the variance of systemrequirements, more than one rounds of dissemination may be conducted in the network.In this case, users must assure that data items on different nodes must have the same ver-sion number. The thesis proposes a Bloom filters based dissemination protocol namedBDP for sensor networks. BDP uses Bloom filters as the compact data structure to storemetadata of all data items, and can find the version difference between two items withthe same key on different nodes, so as to accelerate the process of updating. Through theanalysis of node interactions and the detail of protocol implementation, the thesis pro-poses solutions for controlling the false positive rate, which can constrain the probabilitywithin a relative small value. So the protocol can guarantee the version consistence ofdata items throughout the network.
In applications that does not have real-time requirement, or good deployment condi-tions are not available, users can deploy the sensor networks in unattended area. After aperiod of time, users can use mobile devices such as unmanned aerial vehicles to collectthe sensing data. As far as military applications are concerned, there are two challengeslies in the aforementioned scheme: first, sensor nodes are prone to damages, which re-sult in the lost of data; second, the data collection process is likely to cause unbalancedload distribution, and some nodes will soon exhaust their energy and stop working. Toovercome the former challenge, the thesis proposes a virtual node based distributed stor-age method, which uses Fountain code and random walk to distribute data throughout thenetwork. The method not only holds the advantage of network coding, but also make fulluse of all the sensor nodes for sensing and storage. This significantly improves the effi-ciency of nodes utilization. Even if most of the nodes are damaged, all raw sensing datacouldalsoberetrievedbydecodingtheremainingcodeddata. Forthelatterchallenge, thethesis proposes a data collection method that utilizes mobile sink with single-hop com-munications. The method first builds a dominating set in a distributed way. Then the basestation finds a relatively short path and passes all dominating nodes. These dominatingnodes act as checkpoints, at whose location the base station collects all the sensing data of neighbor nodes. In contrast to previous works, the method has very low complexityand good practicability. It not only significantly reduces the communication overhead ofmultihop routing, but also balances the load of all nodes, which avoids the existence of“hot spot”with high load.
In real-time applications, nodes usually use duty cycling to extend the lifetime ofsensor networks. In duty cycling scheme, each node periodically switches its radio onandoff, soastogetatradeoffbetweenreal-timeperformanceandenergyconsumption. Inmost cases, nodes schedules are not synchronized. Most existing data collection methodsforasynchronousdutycyclingmainlyrelyontimesynchronizationmechanism,whichnotonly consumes precious program volume, but also brings in additional communicationoverhead. Moreover, the performances of those methods rely heavily on the accuracy andstability of time synchronization protocols. This thesis tries to remove the dependenceof time synchronization protocols, and proposes a method that uses multihop negotiationand collaboration to discover shortcuts constituted by a sequence of active nodes. Theseshortcuts are used to pass messages. The method reduces the latency of data collection,so as to improve the real-time property.
本文首先提出了可定位性辅助下的网络调整方法LAL,使得调整后的网络中所有节点都可被定位。定位是无线传感器网络应用中的一项关键服务,但所有的定位算法都无法确保能定位网络中的所有节点。实际应用中部署的无线传感器网络和大量仿真实验都表明,网络中可能存在一部分节点是理论上无法被定位的。为了定位网络中所有节点,主要的手段是通过改变网络的设置,使整个网络满足可被定位的要求。已往的调整方法没有区分可被定位节点和不可被定位节点,因此在调整粒度上比较粗,引入了大量不必要的网络调整和与之相伴的各种开销。与已有的方法相比,LAL利用了节点可定位性信息,具有更高的效率,降低了定位时测距所需的能量消耗,延长了网络的工作寿命。在LAL调整后的网络中,仍存在一些在定位中不需要的边。为了逼近最优结果,本文进一步提出了基于路径的调整方法,在强化了部分假设的前提下,将调整具有2-连通性网络时需要添加的边的数量限制在2|V_N|以内,其中|V_N|为网络中不可被定位节点的数量。
军事应用的需求可能随着战场态势的变化而改变,这就需要数据分发协议通过分发配置参数,对传感器网络中各节点的行为进行方便快捷的控制。数据分发的关键是如何保证全网数据版本的一致性。根据系统的需求变化,可能需要在网络中进行多次数据分发。此时,为了确保所有节点行为的一致性,必须保证所有节点上的参数都被更新到最新的版本。本文提出了一种基于Bloom滤波的无线传感器网络数据分发协议BDP。BDP采用Bloom滤波这一数据结构作为压缩存储数据项元数据信息的工具,能快速识别出不同节点上具有相同键值的数据项间的版本差异,加快更新的过程。通过分析节点交互过程以及协议实现的细节,本文提出了能约束假阳性误判概率的解决方案,将其控制在比较低的水平,从而能以比较高的概率保证全网范围内数据项的版本一致性。
在对实时性要求不高的应用中,或部署条件受限的情况下,可以将传感器网络相对孤立的部署在无人看管的区域。经过一段时间后,再利用无人飞行器等移动设备收集网络中的感知数据。这种非实时的数据收集方式在军事应用中存在两方面的挑战:一是节点容易损毁,造成感知数据的丢失;二是数据收集时容易造成节点负载分布不均,部分节点容易很快耗尽能量停止工作。对前者,本文提出了一种基于虚拟节点的分布式数据存储方法,利用Fountain编码和随机行走在网络内散步数据。该方法不仅保留了编码机制的优点,还能充分利用网络中所有的传感器节点进行感知和存储,大大提高了已有方法对节点的利用效率。即使网络中大部分传感器节点意外损毁,用户仍可从剩余节点上编码过的数据恢复出网络被损毁之前所有节点感知到的原始数据。对后者,本文提出了一种使用移动基站和单跳通信机制的数据收集方法。首先在网络中分布式构造一个支配集,然后移动基站沿一条较短的路径经过所有支配集中的节点。支配集中节点的位置作为基站的检查点。基站在每个检查点处以单跳通信的方式获取通信范围内节点的感知数据。相比已有方法,该方法具有很低的复杂度和很高的实用性,大大降低了多跳路由带来的通信开销,并平衡了各节点的负载,避免了网络中出现少量负载较重的“热点”。
在对实时性要求比较高的应用中,为了延长传感器网络的工作寿命,节点通常采用周期性工作机制,即控制节点在睡眠状态和工作状态间进行循环切换,从而取得实时性和能耗两者间的平衡。节点间的状态切换通常也不是同步的。现有的异步周期性工作机制下的数据收集方法多依赖于时间同步机制,既占用了宝贵的程序体积,又带来了额外的通信开销。此外,这些方法的性能也严重依赖于时间同步协议的精度和稳定性。本文尝试摆脱对时间同步协议的依赖,利用多跳节点局部范围内的信息共享和协同,发现由多个同时处于工作状态的节点所组成的序列,将其作为快速传递数据的捷径。这种方法降低了数据收集的时间延迟,提高了数据的实时性。
Wireless sensor networks (also known as sensor networks, or WSNs) are the net-works that consist of multiple sensor nodes. These nodes are self-organized with wirelesscommunications. Sensor networks have a number of advantages, such as good obscurity,easy deployment, self-organization, low cost and invulnerability to bad weathers. Theywill be used to collect all kinds of battlefield situation information to achieve informationdominance, and play an important role in future military information system. However,the research on sensor networks has just started recently in our army. Many publicationsonsensornetworksfocusoncivilapplications. Ifthetechnologiesincivilapplicationsarecopiedtomilitaryones, theymaynotperformwellasanticipated. Thisthesisanalyzestherequirements and challenges of sensor networks in military applications, and focuses onsome associated technical problems, including localization-oriented network adjustment,data dissemination, non-real-time and real-time data collection. In order to reduce theenergy consumption, improve the network lifetime, adaptability and reliability, and con-solidate the real time property of data routing from sensors to users, this thesis researchthe aforementioned four problems, respectively.
The thesis first proposes a localizability-aided network adjustment method namedLAL, after the execution of which all nodes can be localized. Localization is an importantservice in sensor network applications, however, no localization algorithm can guaranteethat all nodes in the network be localized. Facts in both working sensor networks and ex-tensive simulations have shown that part of the network may not be localizable in theory.To localize those nodes, the primary solution is to change the network configurations soas to make the whole network localizable. Previous adjustment methods areconsidered tobecoarse-grained, sincetheydonotdistinguishthelocalizablenodeswithnon-localizableones, which induce much unnecessary adjustment and accompanying overhead. In con-trast, LAL utilizes node localizability information, improves the efficiency of adjustmentandreducesdistancemeasurementoverheadandenergyconsumptioninlocalization. Thisalso helps to extend the network lifetime. After the adjustment of LAL, there may still ex-ist some unnecessary edges in the network. To approach optimal results, the thesis bringsforward a path-based adjustment method. With some additional hypothesis, the methodcan constrain the number of edges added into the network below 2|V_N|, where |V_N| is the number of non-localizable nodes in the network.
Therequirementsofmilitaryapplicationsmayvaryasthebattlefieldsituationchanges.This motivates the need to disseminate configuration parameters, so as to control the op-erations of sensor nodes quickly and conveniently. The key of dissemination is how tokeep the consistence of the operations of all nodes. According to the variance of systemrequirements, more than one rounds of dissemination may be conducted in the network.In this case, users must assure that data items on different nodes must have the same ver-sion number. The thesis proposes a Bloom filters based dissemination protocol namedBDP for sensor networks. BDP uses Bloom filters as the compact data structure to storemetadata of all data items, and can find the version difference between two items withthe same key on different nodes, so as to accelerate the process of updating. Through theanalysis of node interactions and the detail of protocol implementation, the thesis pro-poses solutions for controlling the false positive rate, which can constrain the probabilitywithin a relative small value. So the protocol can guarantee the version consistence ofdata items throughout the network.
In applications that does not have real-time requirement, or good deployment condi-tions are not available, users can deploy the sensor networks in unattended area. After aperiod of time, users can use mobile devices such as unmanned aerial vehicles to collectthe sensing data. As far as military applications are concerned, there are two challengeslies in the aforementioned scheme: first, sensor nodes are prone to damages, which re-sult in the lost of data; second, the data collection process is likely to cause unbalancedload distribution, and some nodes will soon exhaust their energy and stop working. Toovercome the former challenge, the thesis proposes a virtual node based distributed stor-age method, which uses Fountain code and random walk to distribute data throughout thenetwork. The method not only holds the advantage of network coding, but also make fulluse of all the sensor nodes for sensing and storage. This significantly improves the effi-ciency of nodes utilization. Even if most of the nodes are damaged, all raw sensing datacouldalsoberetrievedbydecodingtheremainingcodeddata. Forthelatterchallenge, thethesis proposes a data collection method that utilizes mobile sink with single-hop com-munications. The method first builds a dominating set in a distributed way. Then the basestation finds a relatively short path and passes all dominating nodes. These dominatingnodes act as checkpoints, at whose location the base station collects all the sensing data of neighbor nodes. In contrast to previous works, the method has very low complexityand good practicability. It not only significantly reduces the communication overhead ofmultihop routing, but also balances the load of all nodes, which avoids the existence of“hot spot”with high load.
In real-time applications, nodes usually use duty cycling to extend the lifetime ofsensor networks. In duty cycling scheme, each node periodically switches its radio onandoff, soastogetatradeoffbetweenreal-timeperformanceandenergyconsumption. Inmost cases, nodes schedules are not synchronized. Most existing data collection methodsforasynchronousdutycyclingmainlyrelyontimesynchronizationmechanism,whichnotonly consumes precious program volume, but also brings in additional communicationoverhead. Moreover, the performances of those methods rely heavily on the accuracy andstability of time synchronization protocols. This thesis tries to remove the dependenceof time synchronization protocols, and proposes a method that uses multihop negotiationand collaboration to discover shortcuts constituted by a sequence of active nodes. Theseshortcuts are used to pass messages. The method reduces the latency of data collection,so as to improve the real-time property.
引文
[1]克劳塞维茨.战争论[M].陕西人民出版社, 2001.
[2] Akyildiz I, Su W, Sankarasubramaniam Y, et al. Wireless Sensor Networks: ASurvey [J]. Computer Networks. 2002, 38: 393–422.
[3] Chong C, Kumar S. Sensor Networks: Evolution, Opportunisties, and Chal-lenges [J]. Proceedings of the IEEE. 2003, 91 (8): 1247–1256.
[4] http://robotics.eecs.berkeley.edu/~pister/SmartDust/.
[5] 21 Ideas for the 21st Century [J]. Business Week. 1999(Aug. 30): 78–167.
[6] Phoha S, LaPorta T, Griffin C. Sensor Network Operations [M]. IEEE Press, 2006.
[7] Hara T, Zadorozhny V I, Buchmann E. Wireless Sensor Network Technologies forthe Information Explosion Era [M]. Springer, 2010.
[8] Ten Emerging Technologies That Will Change The World. Technology Re-view [EB/OL]. 2003. http://www.techreview.com/articles/emerging0203.asp.
[9] Ricadela A. Sensors Everywhere. Information Week [EB/OL]. 2005. http://www.informationweek.com/news/global-cio/showArticle.jhtml?articleID=57702816&pgno=2&queryText=&isPrev=.
[10]刘云浩.物联网导论[M].科学出版社,2010.
[11] http://www.gov.cn/ldhd/2009-11/23/content_1471208.htm.
[12] ProceedingsoftheDistributedSensorNetsWorkshop,Pittsburgh,PA:Departmentof Computer Science, Carnegie Mellon University, 1978 [C]. 1978.
[13] http://www.xbow.com/Products/Wireless_Sensor_Networks.htm.
[14] http://bwrc.eecs.berkeley.edu/Research/Pico_Radio/Default.htm.
[15] http://nesl.ee.ucla.edu/projects/ahlos/.
[16] http://www.intel.com/research/exploratory/motes.htm.
[17] http://www.tinyos.net/.
[18] http://nesl.ee.ucla.edu/projects/sos/.
[19]任丰原,黄海宁,林闯.无线传感器网络[J].软件学报.2003,14 (7):1282–1291.
[20]余旭涛,张在琛.无线传感器网络[J].数字通信世界.2006 (5):66–68.
[21]孙利民.无线传感器网络[M].清华大学出版社,2005.
[22] Yick J, Mukherjee B, Ghosal D. Wireless Sensor Network Survey [J]. ComputerNetworks. 2008, 52 (12): 2292–2330.
[23] Juang P, Oki H, Wang Y, et al. Energy-Efficient Computing for Wildlife Tracking:Design Tradeoffs and Early Experiences with ZebraNet [C]. In Proceedings of the10th annual conference on Architectural Support for Programming Languages andOperating Systems. 2002: 96–107.
[24] SzewczykR,MainwaringA,PolastreJ,etal.AnAnalysisofALargeScaleHabitatMonitoring Application [C]. In Proceedings of the 2nd International Conferenceon Embedded Networked Sensor Systems (SenSys). 2004: 214–226.
[25] Markham A, Trigoni N, Ellwood S, et al. Revealing the Hidden Lives of Under-ground Animals Using Magneto-inductive Tracking [C]. In Proceedings of the8th ACM Conference on Embedded Networked Sensor Systems (SenSys). 2010:281–294.
[26] Tolle G, Polastre J, Szewczyk R, et al. A Macroscope in The Redwoods [C]. InProceedings of the 3rd International Conference on Embedded Networked SensorSystems (SenSys). 2005: 51–63.
[27] Mo L, He Y, Liu Y, et al. Canopy Closure Estimates with GreenOrbs: Sustain-able Sensing in The Forest [C]. In Proceedings of the 7th ACM Conference onEmbedded Networked Sensor Systems (SenSys). 2009: 99–112.
[28] Langendoen K, Baggio A, Visser O. Murphy Loves Potatoes: Experiences from aPilot Sensor Network Deployment in Precision Agriculture [C]. In Proceedings ofthe 20th International Parallel and Distributed Processing Symposium. 2006: 8.
[29] Wark T, Corke P, Sikka P, et al. Transforming Agriculture Through PervasiveWireless Sensor Networks [J]. IEEE Pervasive Computing. 2007, 6 (2): 50–57.
[30] Cardell-Oliver R, Smettem K, Kranz M, et al. A Reactive Soil Moisture SensorNetwork: Design and Field Evaluation [J]. International Journal of DistributedSensor Networks. 2005, 1 (2): 149–162.
[31] Bonnet P, Gehrke J, Seshadri P. Querying the Physical World [J]. IEEE PersonalCommunications. 2000: 10–15.
[32] http://www.alertsystems.org.
[33] Barrenetxea G, Ingelrest F, Schaefer G, et al. The Hitchhiker’s Guide to Success-ful Wireless Sensor Network Deployments [C]. In Proceedings of the 6th ACMConference on Embedded Network Sensor Systems. 2008: 43–56.
[34] Werner-Allen G, Lorincz K, Johnson J, et al. Fidelity and Yield in a Volcano Mon-itoring Sensor Network [C]. In Proceedings of the 7th Symposium on OperatingSystems Design and Implementation. 2006.
[35] Shnayder V, Chen B, Lorincz K, et al. Sensor Networks for Medical Care [R].2005.
[36] Milenkovic A, Otto C, Jovanov E. Wireless Sensor Networks for Personal HealthMonitoring: Issues and an Implementation [J]. Computer Communications. 2006,29: 2521–2533.
[37] Chipara O, Lu C, Bailey T C, et al. Reliable Clinical Monitoring using WirelessSensorNetworks:ExperiencesinaStep-downHospitalUnit[C].InProceedingsofthe 8th International Conference on Embedded Networked Sensor Systems (Sen-Sys). 2010: 155–168.
[38] Essa I. Ubiquitous Sensing for Smart and Aware Environments [J]. IEEE PersonalCommunications. 2000 (10): 47–49.
[39] Liang C-J M, Liu J, Luo L, et al. RACNet: A High-Fidelity Data Center SensingNetwork [C]. In Proceedings of 7th ACM Conference on Embedded NetworkedSensor Systems (SenSys). 2009: 15–28.
[40] Lu J, Sookoor T, Srinivasan V, et al. The Smart Thermostat: Using OccupancySensors to Save Energy in Homes [C]. In Proceedings of the 8th InternationalConference on Embedded Networked Sensor Systems (SenSys). 2010: 211–224.
[41]李栋,回春立,黄希,等.无线传感器网络应用集锦(2):在故宫环境监测中的应用[J].中国计算机学会通讯.2006,2 (5):70–73.
[42] XuN,RangwalaS,ChintalapudiK,etal.AWirelessSensorNetworkforStructuralMonitoring [C]. In Proceedings of the 2nd International Conference on EmbeddedNetworked Sensor Systems (SenSys). 2004: 13–24.
[43] Chintalapudi K, Fu T, Paek J, et al. Monitoring Civil Structures With A WirelessSensor Network [J]. IEEE Internet Computing. 2006: 26–34.
[44] Kim S, Pakzad S, Culler D, et al. Health Monitoring of Civil Infrastructures UsingWireless Sensor Networks [C]. In Poceedings of the 6th International Conferenceon Information Processing in Sensor Networks. 2007: 254–263.
[45] CeriottiM,MottolaL,PiccoG,etal.MonitoringHeritageBuildingswithWirelessSensor Networks: The Torre Aquila Deployment [C]. In Proceedings of the 2009International Conference on Information Processing in Sensor Networks. 2009:277–288.
[46] Li M, Liu Y. Underground Structure Monitoring with Wireless Sensor Network-s [C]. In Proceedings of the 6th Information Processing in Sensor Networks (IP-SN). 2007: 69–78.
[47] Li M, Liu Y, Wang J, et al. Sensor Network Navigation without Locations [C]. InProceedings of 28th Annual Joint Conference of the IEEE Computer and Commu-nications Societies (INFOCOM). 2009: 2419–2427.
[48] YangZ,LiM,LiuY.SeaDepthMeasurementwithRestrictedFloatingSensors[C].In Proceedings of IEEE RTSS. 2007: 469–478.
[49] Pottie G, Kaiser W. Wireless Integrated Network Sensors [J]. Communications ofthe ACM. 2000, 43 (5): 551–558.
[50] Connolly M, O’Reilly F. Sensor Networks and the Food Industry [C]. In Proceed-ings of Workshop on Real-World Wireless Sensor Networks. 2005.
[51] Lai T, Chen Y, Huang P, et al. PipeProbe: A Mobile Sensor Droplet for MappingHidden Pipeline [C]. In Proceedings of the 8th International Conference on Em-bedded Networked Sensor Systems (SenSys). 2010: 113–126.
[52] http://sensorwebs.jpl.nasa.gov/.
[53] United States Department of Defense. Joint Vision 2020 [EB/OL]. 2000. http://www.dtic.mil/doctrine/jel/jfq_pubs/1225.pdf.
[54] Alberts D S, Garstka J J, Stein F P. Network Centric Warfare: Developing andLeveraging Information Superiority [EB/OL]. 1999. http://www.dodccrp.org/files/Alberts_NCW.pdf.
[55] The Cooperative Engagement Capability [EB/OL]. http://techdigest.jhuapl.edu/td1604/APLteam.pdf.
[56] Carts-Powell Y. Unattended Ground Sensors Stop and Analyze the Ros-es [EB/OL]. 2000. http://www.spie.org/web/oer/april/apr00/cover2.html.
[57] Kumar S, Shepherd D. SensIT: Sensor Information Technology for the Warfight-er [C]. In Proceedings of the 4th International Conference on Information Fusion.2001.
[58] Paul J. Smart Sensor Web: Web-based Exploitation of Sensor Fusion for Visual-ization of the Tactical Battlefield [C]. In Proceedings of the Third InternationalConference on Information Fusion. 2000: 16–23.
[59] http://nest.netlab.ohio-state.edu/.
[60] Simon G, Maróti M, Lédecziá, et al. Sensor Network-Based Countersniper Sys-tem [C]. In Proceedings of the 2nd international conference on Embedded net-worked sensor systems (SenSys). 2004: 1–12.
[61] https://www.fbo.gov/?tab=documents&tabmode=form&subtab=core&tabid=b4bebef33b4ac559de101e1831f19292.
[62] He T, Huang C, Blum B, et al. Range-free Localization Schemes for Large ScaleSensor Networks [C]. In Proceedings of the 9th annual International Conferenceon Mobile Computing and Networking (MobiCom). 2003: 81–95.
[63] Niculescu D, Nath B. DV Based Positioning in Ad Hoc Networks [J]. Telecom-munication Systems. 2003, 22 (1): 267–280.
[64] Bahl P, Padmanabhan V N. RADAR: An In-building RF-based User Location andTracking system [C]. In Proceedings of 19th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM). 2000: 775–784.
[65] PriyanthaNB,ChakrabortyA,BalakrishnanH.TheCricketLocation-supportSys-tem[C].InProceedingsofthe6thannualinternationalconferenceonMobilecom-puting and networking (MobiCom). 2000: 32–43.
[66] Seidel S Y, Rappaport T S. 914 MHz Path Loss Prediction Models for IndoorWireless Communications in Multifloored Buildings [J]. IEEE Transactions onAntennas and Propagation. 1992, 40: 207–217.
[67] Goldenberg D K, Bihler P, Cao M, et al. Localization in Sparse Networks UsingSweeps [C]. In Proceedings of the 12th Annual International Conference on Mo-bile Computing and Networking (MobiCom). 2006: 110–121.
[68] Savvides A, Han C-C, Strivastava M B. Dynamic Fine-grained Localization inAd-Hoc Networks of Sensors [C]. In Proceedings of the 7th annual internationalconference on Mobile computing and networking (MobiCom). 2001: 166–179.
[69] Wang X, Luo J, Li S, et al. Component Based Localization in Sparse WirelessAd Hoc and Sensor Networks [C]. In Proceedings of the 16th IEEE InternationalConference on Network Protocols (ICNP). 2008: 288–297.
[70] Whitehouse K, Culler D. Calibration as parameter estimation in sensor network-s [C]. In Proceedings of the 1st ACM international workshop on Wireless sensornetworks and applications (WSNA). 2002: 59–67.
[71] Peng C, Shen G, Zhang Y, et al. BeepBeep: A High Accuracy Acoustic RangingSystem Using COTS Mobile Devices [C]. In Proceedings of the 5th internationalconference on Embedded networked sensor systems (SenSys). 2007: 1–14.
[72] Goldenberg D, Krishnamurthy A, Maness W, et al. Network Localization in Par-tially Localizable Networks [C]. In Proceedings of 24th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2005:313–326.
[73] Eren T, Goldenberg D K, Whiteley W, et al. Rigidity, Computation, and Random-ization in Network Localization [C]. In Proceedings of 23th Annual IEEE Confer-ence on Computer Communications (INFOCOM). April 2004: 2673–2684.
[74] Yang Z, Liu Y. Understanding Node Localizability of Wireless Ad-Hoc Network-s [C]. In Proceedings of 29th Annual Joint Conference of the IEEE Computer andCommunications Societies (INFOCOM). San Diego, CA, USA, 2010: 1–9.
[75] Laman G. On Graphs and Rigidity of Plane Skeletal Structures [J]. Journal of En-gineering Mathematics. 1970, 4: 231–340.
[76] Jacobs D J, Hendrickson B. An Algorithm for Two-dimensional Rigidity Percola-tion:ThePebbleGame[J].JournalofComputationalPhysics.1997,137:346–365.
[77] Jackson B, Jordán T. Connected Rigidity Matroids and Unique Realizations ofGraphs [J]. J. Comb. Theory Ser. B. 2005, 94 (1): 1–29.
[78] Eren T, Whiteley W, Belhumeur P N. Further Results on Sensor Network Local-ization Using Rigidity [C]. In Proceedings of the Second European Workshop onWireless Sensor Networks. 2005: 405–409.
[79] Pathirana P, Bulusu N, Savkin A, et al. Node Localization Using Mobile Robotsin Delay-tolerant Sensor Networks [J]. IEEE Transactions on Mobile Computing.2005: 285–296.
[80] SichitiuM,RamaduraiV.LocalizationofWirelessSensorNetworkswithaMobileBeacon[C].InProceedingsof2004IEEEInternationalConferenceonMobileAd-hoc and Sensor Systems. 2004: 174–183.
[81] Priyantha N B, Balakrishnan H, Demaine E D, et al. Mobile-assisted Localizationin Wireless Sensor Networks [C]. In Proceedings of 24th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2005:172–183.
[82] Wu C, Zhang Y, Sheng W, et al. Rigidity Guided Localization for Mobile RoboticSensor Networks [J]. International Journal of Ad Hoc and Ubiquitous Computing.2010, 6 (2): 114–128.
[83] Anderson B D, Belhumeur P N, Eren T, et al. Graphical Properties of Easily Lo-calizable Sensor Networks [J]. Wirel. Netw. 2009, 15 (2): 177–191.
[84] Naik V, Arora A, Sinha P, et al. Sprinkler: A Reliable and Energy Efficient DataDissemination Service for Wireless Embedded Devices [C]. In Proceedings of the26th IEEE International Real-Time Systems Symposium (RTSS),. 2005: 277–286.
[85] Huang L, Setia S. CORD: Energy-efficient Reliable Bulk Data Dissemination inSensorNetworks[C].InProceedingsof27thAnnualJointConferenceoftheIEEEComputer and Communications Societies (INFOCOM). 2008: 574–582.
[86] Tolle G, Culler D. Design of An Application-cooperative Management System forWireless Sensor Networks [C]. In Proceedings of the Second European Workshopof Wireless Sensor Networks (EWSN). 2005: 121–132.
[87] Lin K, Levis P. Data Discovery and Dissemination with DIP [C]. In Proceedingsof the 7th Information Processing in Sensor Networks (IPSN). 2008: 433–444.
[88] Levis P, Patel N, Culler D, et al. Trickle: A Self-regulating Algorithm for CodeMaintenance and Propagation in Wireless Sensor Networks [C]. In Proceedings ofthe First USENIX/ACM Symposium on Network Systems Design and Implemen-tation (NSDI). 2004: 2.
[89] Stann F, Heidemann J, Shroff R, et al. RBP: Robust Broadcast Propagation inWirelessNetworks [C].InProceedingsofthe4ndinternational conferenceonEm-bedded networked sensor systems (SenSys). 2006: 85–98.
[90] Wang F, Liu J. Duty-cycle-aware Broadcast in Wireless Sensor Networks [C]. InProceedings of 28th Annual Joint Conference of the IEEE Computer and Commu-nications Societies (INFOCOM). 2009: 468–476.
[91] Sun Y, Gurewitz O, Du S, et al. ADB: an efficient multihop broadcast protocolbased on asynchronous duty-cycling in wireless sensor networks [C]. In Proceed-ings of the 7th ACM Conference on Embedded Networked Sensor Systems (Sen-Sys). 2009: 43–56.
[92] Guo S, Gu Y, Jiang B, et al. Opportunistic Flooding in Low-duty-cycle WirelessSensor Networks with Unreliable Links [C]. In Proceedings of the 15th Annual In-ternational Conference on Mobile Computing and Networking(MobiCom). 2009:133–144.
[93] Bonomi F, Mitzenmacher M, Panigrahy R. Beyond Bloom Filters: From Approx-imate Membership Checks to Approximate State Machines [C]. In Proceedings ofACM SIGCOMM. 2006: 315–326.
[94]李志刚.无线传感器网络分布数据存储策略研究[D].长沙:国防科技大学,2010.
[95] Ratnasamy S, Karp B, Estrin D, et al. GHT: A Geographic Hash Table for Data-Centric Storage [C]. In Proceedings of 1st ACM Workshop on Wireless SensorNetworks and Applications. 2002: 78–87.
[96] Sarkar R, Zhu X, Gao J. Double Rulings for Information Brokerage in Sensor Net-works [C]. In Proceedings of the 12th Annual International Conference on MobileComputing and Networking (MobiCom). 2006: 286–297.
[97] Liu X, Huang Q, Zhang Y. Combs, Needles, Haystacks: Balancing Push and Pul-l for Discovery in Large-Scale Sensor Networks [C]. In Proceedings of the 2ndACM Conference on Embedded Network Sensor Systems. 2004: 122–133.
[98] Braginsky D, Estrin D. Rumor Routing Algorithm for Sensor Networks [C]. InProceedings of the 8th annual international conference on Mobile computing andnetworking (MobiCom). 2002: 22–31.
[99]彭绍亮,李姗姗,彭宇行,等.无线传感器网络中一种实时高效的数据存储和查询方法[J].通信学报.2008,29 (11):128–138.
[100] Govindan R, Hellerstein J M, Hong W, et al. The Sensor Network as A Database,USC Technical Report No. 02-771 [R]. 2002.
[101] Madden S, Franklin M J, Hong W, et al. TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks [C]. In Proceedings of the 5th Operating Systems Designand Implementation (OSDI). 2002: 171–182.
[102] Dimakis A, Sarwate A, Wainwright M. Geographic Gossip: Efficient Aggregationfor Sensor Networks [C]. In Proceedings of the 5th International Symposium onInformation Processing in Sensor Networks (IPSN). 2006: 69–76.
[103] Kamra A, Misra V, Feldman J, et al. Growth Codes: Maximizing Sensor Net-work Data Persistence [C]. In Proceedings of the 2006 Conference on Applica-tions, Technologies, Architectures, and Protocols for Computer Communications(SIGCOMM). 2006: 255–266.
[104] Lin Y, Liang B, Li B. Data Persistence in Large-scale Sensor Networks withDecentralized Fountain Codes [C]. In Proceedings of 26th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2007:1658–1666.
[105] DimakisA,PrabhakaranV,RamchandranK.DecentralizedErasureCodesforDis-tributed Networked Storage [J]. IEEE/ACM Transactions on Networking (TON).2006, 52: 2809–2816.
[106] Aly S, Kong Z, Soljanin E. Fountain Codes Based Distributed Storage Algo-rithms for Large-scale Wireless Sensor Networks [C]. In Proceedings of Interna-tional Conference on Information Processing in Sensor Networks (IPSN). 2008:171–182.
[107] Gandhi S, Suri S, Welzl E. Catching eElephants with Mice: Sparse Sampling forMonitoring Sensor Networks [C]. In Proceedings of the ACM Conference on Em-bedded Networked Sensor Systems (Sensys). 2007: 261–274.
[108] Shah R, Roy S, Jain S, et al. Data Mules: Modeling and Analysis of A Three-tierArchitecture for Sparse Sensor Networks [J]. Ad Hoc Networks. 2003, 1 (2-3):215–233.
[109] Kinalis A, Nikoletseas S. Scalable Data Collection Protocols for Wireless Sen-sor Networks with Multiple Mobile Sinks [C]. In Proceedings of the 40th AnnualSimulation Symposium. 2007: 60–72.
[110] ChakrabartiA,SabharwalA,AazhangB.UsingPredictableObserverMobilityforPowerEfficientDesignofSensorNetworks[C].InProceedingsofthe2ndinterna-tional conference on Information processing in sensor networks. 2003: 129–145.
[111] Luo J, Hubaux J. Joint Mobility and Routing for Lifetime Elongation in WirelessSensorNetworks[C].InProceedingsof24thAnnualJointConferenceoftheIEEEComputer and Communications Societies (INFOCOM). 2005: 1735–1746.
[112] Xing G, Wang T, Jia W, et al. Rendezvous Design Algorithms for Wireless SensorNetworks with A Mobile Base Station [C]. In Proceedings of the 9th ACM Inter-national Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc).2008: 231–240.
[113] Lee J, Kusy B, Azim T, et al. Whirlpool Routing for Mobility [C]. In Proceedingsof the 11th ACM International Symposium on Mobile Ad Hoc Networking andComputing (MobiHoc). 2010: 131–140.
[114] Li Z, Li M, Wang J, et al. Ubiquitous Data Collection for Mobile Users in WirelessSensorNetworks[C].InProceedingsof30thAnnualJointConferenceoftheIEEEComputer and Communications Societies (INFOCOM). 2011: 2246–2254.
[115] Couto D, Aguayo D, Bicket J, et al. A highthroughput path metric for multi-hopwireless routing [C]. In Proceedings of MobiCom. 2003: 134–146.
[116] Sang L, Arora A, Zhang H. On Exploiting Asymmetric Wireless Links via One-way Estimation [C]. In Proceedings of MobiHoc. 2007: 11–21.
[117] Wang J, Liu Y, Li M, et al. QoF: Towards Comprehensive Path Quality Measure-ment in Wireless Sensor Networks [C]. In Proceedings of 30th Annual Joint Con-ferenceoftheIEEEComputerandCommunicationsSocieties(INFOCOM).2011:775–783.
[118] Gnawali O, Fonseca R, Jamieson K, et al. Collection Tree Protocol [C]. In Pro-ceedings of the 7th ACM Conference on Embedded Networked Sensor Systems(SenSys). 2009: 1–14.
[119] GreenOrbs Project. http://www.greenorbs.org.
[120] Ye W, Heidemann J S, Estrin D. An Energy-Efficient MAC Protocol for Wire-less Sensor Network [C]. In Proceedings of the 21st Annual IEEE Conference onComputer Communications(INFOCOM). 2002: 1567–1576.
[121] vanDamT,LangendoenK.AnAdaptiveEnergy-efficientMACProtocolforWire-less Sensor Networks [C]. In Proceedings of the 1st International Conference OnEmbedded Networked Sensor System(SenSys). 2003: 171–180.
[122] Du S, Saha A K, Johnson D B. RMAC: A Routing-enhanced Duty-cycle MACProtocol for Wireless Sensor Networks [C]. In Proceedings of the 26th AnnualIEEEConference onComputerCommunications (INFOCOM).2007:1478–1486.
[123] Cao Q, Abdelzaher T, He T, et al. Towards Optimal Sleep Scheduling in SensorNetworks for Rare-event Detection [C]. In Proceedings of the 4th InternationalSymposium on Information Processing in Sensor Networks(IPSN). 2005: 22–31.
[124] Keshavarzian A, Lee H, Venkatraman L. Wakeup Scheduling in Wireless SensorNetworks[C].InProceedingsofthe7thACMInternationalSymposiumonMobileAd hoc Networking and Computing(MobiHoc). 2006: 322–333.
[125] Ma J, Lou W, Wu Y, et al. Energy Efficient TDMA Sleep Scheduling in WirelessSensor Networks [C]. In Proceedings of the 28th Annual IEEE Conference onComputer Communications (INFOCOM). 2009: 630–638.
[126] Gu Y, He T. Data Forwarding in Extremely Low Duty-cycle Sensor Networkswith Unreliable Communication Links [C]. In Proceedings of the 5th internationalConference on Embedded Networked Sensor Systems(SenSys). 2007: 321–334.
[127] PolastreJ,HillJ,CullerD.VersatileLowPowerMediaAccessforWirelessSensorNetworks [C]. In Proceedings of the 2nd International Conference On EmbeddedNetworked Sensor Systems (SenSys). 2004: 95–107.
[128] Sun Y, Gurewitz O, Johnson D B. RI-MAC: A Receiver-Initiated AsynchronousDuty Cycle MAC Protocol for Dynamic Traffic Loads in Wireless Sensor Net-works [C]. In Proceedings of the 6th ACM Conference on Embedded NetworkedSensor Systems(SenSys). 2008: 1–14.
[129] Brown P, Bovey J, Chen X. Context-aware applications: from the laboratory to themarketplace [J]. Personal Communications, IEEE. 1997, 4 (5): 58–64.
[130] Lam S S, Qian C. Geographic Routing in d-dimensional Spaces with GuaranteedDeliveryandLowStretch[C].InProceedingsofInternationalConferenceonMea-surement and Modeling of Computer Systems. 2011: 257–268.
[131] Ding M, Chen D, Xing K, et al. Localized fault-tolerant event boundary detectionin sensor networks [C]. In Proceedings of 24th Annual Joint Conference of theIEEE Computer and Communications Societies (INFOCOM). 2005: 902–913.
[132] Meguerdichian S, Koushanfar F, Potkonjak M, et al. Coverage problems in wire-less ad-hoc sensor networks [C]. In Proceedings of 20th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2001:1380–1387.
[133] OceanSense Project. http://www.cse.ust.hk/ liu/Ocean/index.html.
[134] Ssu K, Ou C, Jiau H. Localization with Mobile Anchor Points in Wireless Sen-sor Networks [J]. IEEE Transactions on Vehicular Technology. 2005, 54 (3):1187–1197.
[135] Moore D, Leonard J, Rus D, et al. Robust Distributed Network Localization withNoisy Range Measurements [C]. In Proceedings of the 2nd international confer-ence on Embedded networked sensor systems (SenSys). 2004: 50–61.
[136] AspnesJ,ErenT,GoldenbergD,etal.ATheoryofNetworkLocalization[J].IEEETransactions on Mobile Computing. 2006: 1663–1678.
[137] Hendrickson B. Conditions for Unique Graph Realizations [J]. SIAM Journal ofComputing. 1992, 21 (1): 65–84.
[138] Hopcroft J, Tarjan R. Algorithm 447: Efficient Algorithms for Graph Manipula-tion [J]. Commun. ACM. 1973, 16 (6): 372–378.
[139] LiuY,LiuK,LiM.Passivediagnosisforwirelesssensornetworks[J].IEEE/ACMTransactions on Networking. 2010, 18: 1132–1144.
[140] Pham T L, Lavallee I, Bui M, et al. A distributed algorithm for the maximum flowproblem [C]. In Proceedings of the 4th International Symposium on Parallel andDistributed Computing. 2005: 131–138.
[141] Kirousis L, Kranakis E, Krizanc D, et al. Power consumption in packet radio net-works [J]. Theoretical Computer Science. 2000, 243 (1-2): 289–305.
[142] Crossbow. Mote in Nnetwork Programming User Reference [EB/OL]. http://www.tinyos.net/tinyos-1.x/doc/xnp.pdf.
[143] Stathopoulos T, Heidemann J, Estrin D. A Remote Code Update Mechanism forWireless Sensor Networks [R]. 2003.
[144] Hui J, Culler D. The Dynamic Behavior of A Data Dissemination Protocol forNetwork Programming at Scale [C]. In Proceedings of the 2nd international con-ference on Embedded networked sensor systems (SenSys). 2004: 81–94.
[145] Wang L. MNP: Multihop Network Reprogramming Service for Sensor Network-s [C]. In Proceedings of the 2nd international conference on Embedded networkedsensor systems (SenSys). 2004: 285–286.
[146] Liang C, Musaloiu-Elefteri R, Terzis A. Typhoon: A Reliable Data DisseminationProtocol for Wireless Sensor Networks [C]. In Proceedings of the 5th EuropeanConference on Wireless Sensor Networks (EWSN). 2008: 268–285.
[147] Levis P, Gay D, Culler D. Active Sensor Networks [C]. In Proceedings of the Sec-ond USENIX/ACM Symposium on Network Systems Design and Implementation(NSDI). 2005: 343–356.
[148] GnawaliO,GreensteinB,JangK,etal.TheTENETArchitectureforTieredSensorNetworks [C]. In Proceedings of the 4nd international conference on Embeddednetworked sensor systems (SenSys). 2006: 153–166.
[149] BroderA,MitzenmacherM.NetworkApplicationsofBloomFilters:ASurvey[J].Internet Mathematics. 2004, 1: 485–509.
[150] CC2420 Packet Formats [EB/OL]. http://docs.tinyos.net/index.php/CC2420_Packet_Formats.
[151] Levis P, Lee N, Welsh M, et al. TOSSIM: Simulating Large Wireless Sensor Net-works of TinyOS Motes [C]. In Proceedings of the 1st ACM Conference on Em-bedded Networked Sensor Systems (SenSys). 2003: 381–396.
[152]俞建新,杨小虎.网络存储新技术评析[J].计算机工程.2006,32(20):120–122.
[153] Byers J, Luby M, Mitzenmacher M, et al. A Digital Fountain Approach to ReliableDistribution of Bulk Data [C]. In Proceedings of the Conference on Applications,Technologies, Architectures, and Protocols for Computer Communications (SIG-COMM). 1998: 56–67.
[154] Luby M. LT Codes [C]. In Proceedings of the 43rd Symposium on Foundations ofComputer Science (FOCS). 2002: 271–280.
[155] Servetto S D, Barrenechea G. Constrained random walks on random graphs: Rout-ing algorithms for large scale wireless sensor networks [C]. In Proceedings of theFirst ACMWorkshop on Wireless Sensor Networks and Applications (WSNA’02).2002: 12–21.
[156] Avin C, Ercal G. On the Cover Time of Random Geometric Graphs [C]. In Pro-ceedings of the 32nd International Colloquium of Automata, Languages and Pro-gramming (ICALP). 2005: 677–689.
[157] Berge C. The Theory of Graphs and Its Applications [M]. John Wiley & Sons,1962.
[158]路纲,周明天,唐勇,等.任意图支配集精确算法回顾[J].计算机学报.2010,33:1073–1087.
[159] Du D-Z, Pardalos P. Handbook of Combinatorial Optimization [M]. Kluwer Aca-demic Publishers, 2004.
[160] Kahng A B, Reda S. Match Twice and Stitch: A New TSP Tour ConstructionHeuristic [J]. Operations Research Letters. 2004, 32: 499–509.
[161] Wan P, Alzoubi K, Frieder O. Distributed Construction of Connected Dominat-ing Set in Wireless Ad Hoc Networks [C]. In Proceedings of 21th Annual JointConference of the IEEE Computer and Communications Societies (INFOCOM).2002: 1597–1604.
[162]彭绍亮.无线传感器网络中高效传输技术研究[D].长沙:国防科技大学,2008.
[163]陈颖文.无线传感器网络低能耗数据查询[D].长沙:国防科技大学,2007.
[2] Akyildiz I, Su W, Sankarasubramaniam Y, et al. Wireless Sensor Networks: ASurvey [J]. Computer Networks. 2002, 38: 393–422.
[3] Chong C, Kumar S. Sensor Networks: Evolution, Opportunisties, and Chal-lenges [J]. Proceedings of the IEEE. 2003, 91 (8): 1247–1256.
[4] http://robotics.eecs.berkeley.edu/~pister/SmartDust/.
[5] 21 Ideas for the 21st Century [J]. Business Week. 1999(Aug. 30): 78–167.
[6] Phoha S, LaPorta T, Griffin C. Sensor Network Operations [M]. IEEE Press, 2006.
[7] Hara T, Zadorozhny V I, Buchmann E. Wireless Sensor Network Technologies forthe Information Explosion Era [M]. Springer, 2010.
[8] Ten Emerging Technologies That Will Change The World. Technology Re-view [EB/OL]. 2003. http://www.techreview.com/articles/emerging0203.asp.
[9] Ricadela A. Sensors Everywhere. Information Week [EB/OL]. 2005. http://www.informationweek.com/news/global-cio/showArticle.jhtml?articleID=57702816&pgno=2&queryText=&isPrev=.
[10]刘云浩.物联网导论[M].科学出版社,2010.
[11] http://www.gov.cn/ldhd/2009-11/23/content_1471208.htm.
[12] ProceedingsoftheDistributedSensorNetsWorkshop,Pittsburgh,PA:Departmentof Computer Science, Carnegie Mellon University, 1978 [C]. 1978.
[13] http://www.xbow.com/Products/Wireless_Sensor_Networks.htm.
[14] http://bwrc.eecs.berkeley.edu/Research/Pico_Radio/Default.htm.
[15] http://nesl.ee.ucla.edu/projects/ahlos/.
[16] http://www.intel.com/research/exploratory/motes.htm.
[17] http://www.tinyos.net/.
[18] http://nesl.ee.ucla.edu/projects/sos/.
[19]任丰原,黄海宁,林闯.无线传感器网络[J].软件学报.2003,14 (7):1282–1291.
[20]余旭涛,张在琛.无线传感器网络[J].数字通信世界.2006 (5):66–68.
[21]孙利民.无线传感器网络[M].清华大学出版社,2005.
[22] Yick J, Mukherjee B, Ghosal D. Wireless Sensor Network Survey [J]. ComputerNetworks. 2008, 52 (12): 2292–2330.
[23] Juang P, Oki H, Wang Y, et al. Energy-Efficient Computing for Wildlife Tracking:Design Tradeoffs and Early Experiences with ZebraNet [C]. In Proceedings of the10th annual conference on Architectural Support for Programming Languages andOperating Systems. 2002: 96–107.
[24] SzewczykR,MainwaringA,PolastreJ,etal.AnAnalysisofALargeScaleHabitatMonitoring Application [C]. In Proceedings of the 2nd International Conferenceon Embedded Networked Sensor Systems (SenSys). 2004: 214–226.
[25] Markham A, Trigoni N, Ellwood S, et al. Revealing the Hidden Lives of Under-ground Animals Using Magneto-inductive Tracking [C]. In Proceedings of the8th ACM Conference on Embedded Networked Sensor Systems (SenSys). 2010:281–294.
[26] Tolle G, Polastre J, Szewczyk R, et al. A Macroscope in The Redwoods [C]. InProceedings of the 3rd International Conference on Embedded Networked SensorSystems (SenSys). 2005: 51–63.
[27] Mo L, He Y, Liu Y, et al. Canopy Closure Estimates with GreenOrbs: Sustain-able Sensing in The Forest [C]. In Proceedings of the 7th ACM Conference onEmbedded Networked Sensor Systems (SenSys). 2009: 99–112.
[28] Langendoen K, Baggio A, Visser O. Murphy Loves Potatoes: Experiences from aPilot Sensor Network Deployment in Precision Agriculture [C]. In Proceedings ofthe 20th International Parallel and Distributed Processing Symposium. 2006: 8.
[29] Wark T, Corke P, Sikka P, et al. Transforming Agriculture Through PervasiveWireless Sensor Networks [J]. IEEE Pervasive Computing. 2007, 6 (2): 50–57.
[30] Cardell-Oliver R, Smettem K, Kranz M, et al. A Reactive Soil Moisture SensorNetwork: Design and Field Evaluation [J]. International Journal of DistributedSensor Networks. 2005, 1 (2): 149–162.
[31] Bonnet P, Gehrke J, Seshadri P. Querying the Physical World [J]. IEEE PersonalCommunications. 2000: 10–15.
[32] http://www.alertsystems.org.
[33] Barrenetxea G, Ingelrest F, Schaefer G, et al. The Hitchhiker’s Guide to Success-ful Wireless Sensor Network Deployments [C]. In Proceedings of the 6th ACMConference on Embedded Network Sensor Systems. 2008: 43–56.
[34] Werner-Allen G, Lorincz K, Johnson J, et al. Fidelity and Yield in a Volcano Mon-itoring Sensor Network [C]. In Proceedings of the 7th Symposium on OperatingSystems Design and Implementation. 2006.
[35] Shnayder V, Chen B, Lorincz K, et al. Sensor Networks for Medical Care [R].2005.
[36] Milenkovic A, Otto C, Jovanov E. Wireless Sensor Networks for Personal HealthMonitoring: Issues and an Implementation [J]. Computer Communications. 2006,29: 2521–2533.
[37] Chipara O, Lu C, Bailey T C, et al. Reliable Clinical Monitoring using WirelessSensorNetworks:ExperiencesinaStep-downHospitalUnit[C].InProceedingsofthe 8th International Conference on Embedded Networked Sensor Systems (Sen-Sys). 2010: 155–168.
[38] Essa I. Ubiquitous Sensing for Smart and Aware Environments [J]. IEEE PersonalCommunications. 2000 (10): 47–49.
[39] Liang C-J M, Liu J, Luo L, et al. RACNet: A High-Fidelity Data Center SensingNetwork [C]. In Proceedings of 7th ACM Conference on Embedded NetworkedSensor Systems (SenSys). 2009: 15–28.
[40] Lu J, Sookoor T, Srinivasan V, et al. The Smart Thermostat: Using OccupancySensors to Save Energy in Homes [C]. In Proceedings of the 8th InternationalConference on Embedded Networked Sensor Systems (SenSys). 2010: 211–224.
[41]李栋,回春立,黄希,等.无线传感器网络应用集锦(2):在故宫环境监测中的应用[J].中国计算机学会通讯.2006,2 (5):70–73.
[42] XuN,RangwalaS,ChintalapudiK,etal.AWirelessSensorNetworkforStructuralMonitoring [C]. In Proceedings of the 2nd International Conference on EmbeddedNetworked Sensor Systems (SenSys). 2004: 13–24.
[43] Chintalapudi K, Fu T, Paek J, et al. Monitoring Civil Structures With A WirelessSensor Network [J]. IEEE Internet Computing. 2006: 26–34.
[44] Kim S, Pakzad S, Culler D, et al. Health Monitoring of Civil Infrastructures UsingWireless Sensor Networks [C]. In Poceedings of the 6th International Conferenceon Information Processing in Sensor Networks. 2007: 254–263.
[45] CeriottiM,MottolaL,PiccoG,etal.MonitoringHeritageBuildingswithWirelessSensor Networks: The Torre Aquila Deployment [C]. In Proceedings of the 2009International Conference on Information Processing in Sensor Networks. 2009:277–288.
[46] Li M, Liu Y. Underground Structure Monitoring with Wireless Sensor Network-s [C]. In Proceedings of the 6th Information Processing in Sensor Networks (IP-SN). 2007: 69–78.
[47] Li M, Liu Y, Wang J, et al. Sensor Network Navigation without Locations [C]. InProceedings of 28th Annual Joint Conference of the IEEE Computer and Commu-nications Societies (INFOCOM). 2009: 2419–2427.
[48] YangZ,LiM,LiuY.SeaDepthMeasurementwithRestrictedFloatingSensors[C].In Proceedings of IEEE RTSS. 2007: 469–478.
[49] Pottie G, Kaiser W. Wireless Integrated Network Sensors [J]. Communications ofthe ACM. 2000, 43 (5): 551–558.
[50] Connolly M, O’Reilly F. Sensor Networks and the Food Industry [C]. In Proceed-ings of Workshop on Real-World Wireless Sensor Networks. 2005.
[51] Lai T, Chen Y, Huang P, et al. PipeProbe: A Mobile Sensor Droplet for MappingHidden Pipeline [C]. In Proceedings of the 8th International Conference on Em-bedded Networked Sensor Systems (SenSys). 2010: 113–126.
[52] http://sensorwebs.jpl.nasa.gov/.
[53] United States Department of Defense. Joint Vision 2020 [EB/OL]. 2000. http://www.dtic.mil/doctrine/jel/jfq_pubs/1225.pdf.
[54] Alberts D S, Garstka J J, Stein F P. Network Centric Warfare: Developing andLeveraging Information Superiority [EB/OL]. 1999. http://www.dodccrp.org/files/Alberts_NCW.pdf.
[55] The Cooperative Engagement Capability [EB/OL]. http://techdigest.jhuapl.edu/td1604/APLteam.pdf.
[56] Carts-Powell Y. Unattended Ground Sensors Stop and Analyze the Ros-es [EB/OL]. 2000. http://www.spie.org/web/oer/april/apr00/cover2.html.
[57] Kumar S, Shepherd D. SensIT: Sensor Information Technology for the Warfight-er [C]. In Proceedings of the 4th International Conference on Information Fusion.2001.
[58] Paul J. Smart Sensor Web: Web-based Exploitation of Sensor Fusion for Visual-ization of the Tactical Battlefield [C]. In Proceedings of the Third InternationalConference on Information Fusion. 2000: 16–23.
[59] http://nest.netlab.ohio-state.edu/.
[60] Simon G, Maróti M, Lédecziá, et al. Sensor Network-Based Countersniper Sys-tem [C]. In Proceedings of the 2nd international conference on Embedded net-worked sensor systems (SenSys). 2004: 1–12.
[61] https://www.fbo.gov/?tab=documents&tabmode=form&subtab=core&tabid=b4bebef33b4ac559de101e1831f19292.
[62] He T, Huang C, Blum B, et al. Range-free Localization Schemes for Large ScaleSensor Networks [C]. In Proceedings of the 9th annual International Conferenceon Mobile Computing and Networking (MobiCom). 2003: 81–95.
[63] Niculescu D, Nath B. DV Based Positioning in Ad Hoc Networks [J]. Telecom-munication Systems. 2003, 22 (1): 267–280.
[64] Bahl P, Padmanabhan V N. RADAR: An In-building RF-based User Location andTracking system [C]. In Proceedings of 19th Annual Joint Conference of the IEEEComputer and Communications Societies (INFOCOM). 2000: 775–784.
[65] PriyanthaNB,ChakrabortyA,BalakrishnanH.TheCricketLocation-supportSys-tem[C].InProceedingsofthe6thannualinternationalconferenceonMobilecom-puting and networking (MobiCom). 2000: 32–43.
[66] Seidel S Y, Rappaport T S. 914 MHz Path Loss Prediction Models for IndoorWireless Communications in Multifloored Buildings [J]. IEEE Transactions onAntennas and Propagation. 1992, 40: 207–217.
[67] Goldenberg D K, Bihler P, Cao M, et al. Localization in Sparse Networks UsingSweeps [C]. In Proceedings of the 12th Annual International Conference on Mo-bile Computing and Networking (MobiCom). 2006: 110–121.
[68] Savvides A, Han C-C, Strivastava M B. Dynamic Fine-grained Localization inAd-Hoc Networks of Sensors [C]. In Proceedings of the 7th annual internationalconference on Mobile computing and networking (MobiCom). 2001: 166–179.
[69] Wang X, Luo J, Li S, et al. Component Based Localization in Sparse WirelessAd Hoc and Sensor Networks [C]. In Proceedings of the 16th IEEE InternationalConference on Network Protocols (ICNP). 2008: 288–297.
[70] Whitehouse K, Culler D. Calibration as parameter estimation in sensor network-s [C]. In Proceedings of the 1st ACM international workshop on Wireless sensornetworks and applications (WSNA). 2002: 59–67.
[71] Peng C, Shen G, Zhang Y, et al. BeepBeep: A High Accuracy Acoustic RangingSystem Using COTS Mobile Devices [C]. In Proceedings of the 5th internationalconference on Embedded networked sensor systems (SenSys). 2007: 1–14.
[72] Goldenberg D, Krishnamurthy A, Maness W, et al. Network Localization in Par-tially Localizable Networks [C]. In Proceedings of 24th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2005:313–326.
[73] Eren T, Goldenberg D K, Whiteley W, et al. Rigidity, Computation, and Random-ization in Network Localization [C]. In Proceedings of 23th Annual IEEE Confer-ence on Computer Communications (INFOCOM). April 2004: 2673–2684.
[74] Yang Z, Liu Y. Understanding Node Localizability of Wireless Ad-Hoc Network-s [C]. In Proceedings of 29th Annual Joint Conference of the IEEE Computer andCommunications Societies (INFOCOM). San Diego, CA, USA, 2010: 1–9.
[75] Laman G. On Graphs and Rigidity of Plane Skeletal Structures [J]. Journal of En-gineering Mathematics. 1970, 4: 231–340.
[76] Jacobs D J, Hendrickson B. An Algorithm for Two-dimensional Rigidity Percola-tion:ThePebbleGame[J].JournalofComputationalPhysics.1997,137:346–365.
[77] Jackson B, Jordán T. Connected Rigidity Matroids and Unique Realizations ofGraphs [J]. J. Comb. Theory Ser. B. 2005, 94 (1): 1–29.
[78] Eren T, Whiteley W, Belhumeur P N. Further Results on Sensor Network Local-ization Using Rigidity [C]. In Proceedings of the Second European Workshop onWireless Sensor Networks. 2005: 405–409.
[79] Pathirana P, Bulusu N, Savkin A, et al. Node Localization Using Mobile Robotsin Delay-tolerant Sensor Networks [J]. IEEE Transactions on Mobile Computing.2005: 285–296.
[80] SichitiuM,RamaduraiV.LocalizationofWirelessSensorNetworkswithaMobileBeacon[C].InProceedingsof2004IEEEInternationalConferenceonMobileAd-hoc and Sensor Systems. 2004: 174–183.
[81] Priyantha N B, Balakrishnan H, Demaine E D, et al. Mobile-assisted Localizationin Wireless Sensor Networks [C]. In Proceedings of 24th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2005:172–183.
[82] Wu C, Zhang Y, Sheng W, et al. Rigidity Guided Localization for Mobile RoboticSensor Networks [J]. International Journal of Ad Hoc and Ubiquitous Computing.2010, 6 (2): 114–128.
[83] Anderson B D, Belhumeur P N, Eren T, et al. Graphical Properties of Easily Lo-calizable Sensor Networks [J]. Wirel. Netw. 2009, 15 (2): 177–191.
[84] Naik V, Arora A, Sinha P, et al. Sprinkler: A Reliable and Energy Efficient DataDissemination Service for Wireless Embedded Devices [C]. In Proceedings of the26th IEEE International Real-Time Systems Symposium (RTSS),. 2005: 277–286.
[85] Huang L, Setia S. CORD: Energy-efficient Reliable Bulk Data Dissemination inSensorNetworks[C].InProceedingsof27thAnnualJointConferenceoftheIEEEComputer and Communications Societies (INFOCOM). 2008: 574–582.
[86] Tolle G, Culler D. Design of An Application-cooperative Management System forWireless Sensor Networks [C]. In Proceedings of the Second European Workshopof Wireless Sensor Networks (EWSN). 2005: 121–132.
[87] Lin K, Levis P. Data Discovery and Dissemination with DIP [C]. In Proceedingsof the 7th Information Processing in Sensor Networks (IPSN). 2008: 433–444.
[88] Levis P, Patel N, Culler D, et al. Trickle: A Self-regulating Algorithm for CodeMaintenance and Propagation in Wireless Sensor Networks [C]. In Proceedings ofthe First USENIX/ACM Symposium on Network Systems Design and Implemen-tation (NSDI). 2004: 2.
[89] Stann F, Heidemann J, Shroff R, et al. RBP: Robust Broadcast Propagation inWirelessNetworks [C].InProceedingsofthe4ndinternational conferenceonEm-bedded networked sensor systems (SenSys). 2006: 85–98.
[90] Wang F, Liu J. Duty-cycle-aware Broadcast in Wireless Sensor Networks [C]. InProceedings of 28th Annual Joint Conference of the IEEE Computer and Commu-nications Societies (INFOCOM). 2009: 468–476.
[91] Sun Y, Gurewitz O, Du S, et al. ADB: an efficient multihop broadcast protocolbased on asynchronous duty-cycling in wireless sensor networks [C]. In Proceed-ings of the 7th ACM Conference on Embedded Networked Sensor Systems (Sen-Sys). 2009: 43–56.
[92] Guo S, Gu Y, Jiang B, et al. Opportunistic Flooding in Low-duty-cycle WirelessSensor Networks with Unreliable Links [C]. In Proceedings of the 15th Annual In-ternational Conference on Mobile Computing and Networking(MobiCom). 2009:133–144.
[93] Bonomi F, Mitzenmacher M, Panigrahy R. Beyond Bloom Filters: From Approx-imate Membership Checks to Approximate State Machines [C]. In Proceedings ofACM SIGCOMM. 2006: 315–326.
[94]李志刚.无线传感器网络分布数据存储策略研究[D].长沙:国防科技大学,2010.
[95] Ratnasamy S, Karp B, Estrin D, et al. GHT: A Geographic Hash Table for Data-Centric Storage [C]. In Proceedings of 1st ACM Workshop on Wireless SensorNetworks and Applications. 2002: 78–87.
[96] Sarkar R, Zhu X, Gao J. Double Rulings for Information Brokerage in Sensor Net-works [C]. In Proceedings of the 12th Annual International Conference on MobileComputing and Networking (MobiCom). 2006: 286–297.
[97] Liu X, Huang Q, Zhang Y. Combs, Needles, Haystacks: Balancing Push and Pul-l for Discovery in Large-Scale Sensor Networks [C]. In Proceedings of the 2ndACM Conference on Embedded Network Sensor Systems. 2004: 122–133.
[98] Braginsky D, Estrin D. Rumor Routing Algorithm for Sensor Networks [C]. InProceedings of the 8th annual international conference on Mobile computing andnetworking (MobiCom). 2002: 22–31.
[99]彭绍亮,李姗姗,彭宇行,等.无线传感器网络中一种实时高效的数据存储和查询方法[J].通信学报.2008,29 (11):128–138.
[100] Govindan R, Hellerstein J M, Hong W, et al. The Sensor Network as A Database,USC Technical Report No. 02-771 [R]. 2002.
[101] Madden S, Franklin M J, Hong W, et al. TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks [C]. In Proceedings of the 5th Operating Systems Designand Implementation (OSDI). 2002: 171–182.
[102] Dimakis A, Sarwate A, Wainwright M. Geographic Gossip: Efficient Aggregationfor Sensor Networks [C]. In Proceedings of the 5th International Symposium onInformation Processing in Sensor Networks (IPSN). 2006: 69–76.
[103] Kamra A, Misra V, Feldman J, et al. Growth Codes: Maximizing Sensor Net-work Data Persistence [C]. In Proceedings of the 2006 Conference on Applica-tions, Technologies, Architectures, and Protocols for Computer Communications(SIGCOMM). 2006: 255–266.
[104] Lin Y, Liang B, Li B. Data Persistence in Large-scale Sensor Networks withDecentralized Fountain Codes [C]. In Proceedings of 26th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2007:1658–1666.
[105] DimakisA,PrabhakaranV,RamchandranK.DecentralizedErasureCodesforDis-tributed Networked Storage [J]. IEEE/ACM Transactions on Networking (TON).2006, 52: 2809–2816.
[106] Aly S, Kong Z, Soljanin E. Fountain Codes Based Distributed Storage Algo-rithms for Large-scale Wireless Sensor Networks [C]. In Proceedings of Interna-tional Conference on Information Processing in Sensor Networks (IPSN). 2008:171–182.
[107] Gandhi S, Suri S, Welzl E. Catching eElephants with Mice: Sparse Sampling forMonitoring Sensor Networks [C]. In Proceedings of the ACM Conference on Em-bedded Networked Sensor Systems (Sensys). 2007: 261–274.
[108] Shah R, Roy S, Jain S, et al. Data Mules: Modeling and Analysis of A Three-tierArchitecture for Sparse Sensor Networks [J]. Ad Hoc Networks. 2003, 1 (2-3):215–233.
[109] Kinalis A, Nikoletseas S. Scalable Data Collection Protocols for Wireless Sen-sor Networks with Multiple Mobile Sinks [C]. In Proceedings of the 40th AnnualSimulation Symposium. 2007: 60–72.
[110] ChakrabartiA,SabharwalA,AazhangB.UsingPredictableObserverMobilityforPowerEfficientDesignofSensorNetworks[C].InProceedingsofthe2ndinterna-tional conference on Information processing in sensor networks. 2003: 129–145.
[111] Luo J, Hubaux J. Joint Mobility and Routing for Lifetime Elongation in WirelessSensorNetworks[C].InProceedingsof24thAnnualJointConferenceoftheIEEEComputer and Communications Societies (INFOCOM). 2005: 1735–1746.
[112] Xing G, Wang T, Jia W, et al. Rendezvous Design Algorithms for Wireless SensorNetworks with A Mobile Base Station [C]. In Proceedings of the 9th ACM Inter-national Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc).2008: 231–240.
[113] Lee J, Kusy B, Azim T, et al. Whirlpool Routing for Mobility [C]. In Proceedingsof the 11th ACM International Symposium on Mobile Ad Hoc Networking andComputing (MobiHoc). 2010: 131–140.
[114] Li Z, Li M, Wang J, et al. Ubiquitous Data Collection for Mobile Users in WirelessSensorNetworks[C].InProceedingsof30thAnnualJointConferenceoftheIEEEComputer and Communications Societies (INFOCOM). 2011: 2246–2254.
[115] Couto D, Aguayo D, Bicket J, et al. A highthroughput path metric for multi-hopwireless routing [C]. In Proceedings of MobiCom. 2003: 134–146.
[116] Sang L, Arora A, Zhang H. On Exploiting Asymmetric Wireless Links via One-way Estimation [C]. In Proceedings of MobiHoc. 2007: 11–21.
[117] Wang J, Liu Y, Li M, et al. QoF: Towards Comprehensive Path Quality Measure-ment in Wireless Sensor Networks [C]. In Proceedings of 30th Annual Joint Con-ferenceoftheIEEEComputerandCommunicationsSocieties(INFOCOM).2011:775–783.
[118] Gnawali O, Fonseca R, Jamieson K, et al. Collection Tree Protocol [C]. In Pro-ceedings of the 7th ACM Conference on Embedded Networked Sensor Systems(SenSys). 2009: 1–14.
[119] GreenOrbs Project. http://www.greenorbs.org.
[120] Ye W, Heidemann J S, Estrin D. An Energy-Efficient MAC Protocol for Wire-less Sensor Network [C]. In Proceedings of the 21st Annual IEEE Conference onComputer Communications(INFOCOM). 2002: 1567–1576.
[121] vanDamT,LangendoenK.AnAdaptiveEnergy-efficientMACProtocolforWire-less Sensor Networks [C]. In Proceedings of the 1st International Conference OnEmbedded Networked Sensor System(SenSys). 2003: 171–180.
[122] Du S, Saha A K, Johnson D B. RMAC: A Routing-enhanced Duty-cycle MACProtocol for Wireless Sensor Networks [C]. In Proceedings of the 26th AnnualIEEEConference onComputerCommunications (INFOCOM).2007:1478–1486.
[123] Cao Q, Abdelzaher T, He T, et al. Towards Optimal Sleep Scheduling in SensorNetworks for Rare-event Detection [C]. In Proceedings of the 4th InternationalSymposium on Information Processing in Sensor Networks(IPSN). 2005: 22–31.
[124] Keshavarzian A, Lee H, Venkatraman L. Wakeup Scheduling in Wireless SensorNetworks[C].InProceedingsofthe7thACMInternationalSymposiumonMobileAd hoc Networking and Computing(MobiHoc). 2006: 322–333.
[125] Ma J, Lou W, Wu Y, et al. Energy Efficient TDMA Sleep Scheduling in WirelessSensor Networks [C]. In Proceedings of the 28th Annual IEEE Conference onComputer Communications (INFOCOM). 2009: 630–638.
[126] Gu Y, He T. Data Forwarding in Extremely Low Duty-cycle Sensor Networkswith Unreliable Communication Links [C]. In Proceedings of the 5th internationalConference on Embedded Networked Sensor Systems(SenSys). 2007: 321–334.
[127] PolastreJ,HillJ,CullerD.VersatileLowPowerMediaAccessforWirelessSensorNetworks [C]. In Proceedings of the 2nd International Conference On EmbeddedNetworked Sensor Systems (SenSys). 2004: 95–107.
[128] Sun Y, Gurewitz O, Johnson D B. RI-MAC: A Receiver-Initiated AsynchronousDuty Cycle MAC Protocol for Dynamic Traffic Loads in Wireless Sensor Net-works [C]. In Proceedings of the 6th ACM Conference on Embedded NetworkedSensor Systems(SenSys). 2008: 1–14.
[129] Brown P, Bovey J, Chen X. Context-aware applications: from the laboratory to themarketplace [J]. Personal Communications, IEEE. 1997, 4 (5): 58–64.
[130] Lam S S, Qian C. Geographic Routing in d-dimensional Spaces with GuaranteedDeliveryandLowStretch[C].InProceedingsofInternationalConferenceonMea-surement and Modeling of Computer Systems. 2011: 257–268.
[131] Ding M, Chen D, Xing K, et al. Localized fault-tolerant event boundary detectionin sensor networks [C]. In Proceedings of 24th Annual Joint Conference of theIEEE Computer and Communications Societies (INFOCOM). 2005: 902–913.
[132] Meguerdichian S, Koushanfar F, Potkonjak M, et al. Coverage problems in wire-less ad-hoc sensor networks [C]. In Proceedings of 20th Annual Joint Confer-ence of the IEEE Computer and Communications Societies (INFOCOM). 2001:1380–1387.
[133] OceanSense Project. http://www.cse.ust.hk/ liu/Ocean/index.html.
[134] Ssu K, Ou C, Jiau H. Localization with Mobile Anchor Points in Wireless Sen-sor Networks [J]. IEEE Transactions on Vehicular Technology. 2005, 54 (3):1187–1197.
[135] Moore D, Leonard J, Rus D, et al. Robust Distributed Network Localization withNoisy Range Measurements [C]. In Proceedings of the 2nd international confer-ence on Embedded networked sensor systems (SenSys). 2004: 50–61.
[136] AspnesJ,ErenT,GoldenbergD,etal.ATheoryofNetworkLocalization[J].IEEETransactions on Mobile Computing. 2006: 1663–1678.
[137] Hendrickson B. Conditions for Unique Graph Realizations [J]. SIAM Journal ofComputing. 1992, 21 (1): 65–84.
[138] Hopcroft J, Tarjan R. Algorithm 447: Efficient Algorithms for Graph Manipula-tion [J]. Commun. ACM. 1973, 16 (6): 372–378.
[139] LiuY,LiuK,LiM.Passivediagnosisforwirelesssensornetworks[J].IEEE/ACMTransactions on Networking. 2010, 18: 1132–1144.
[140] Pham T L, Lavallee I, Bui M, et al. A distributed algorithm for the maximum flowproblem [C]. In Proceedings of the 4th International Symposium on Parallel andDistributed Computing. 2005: 131–138.
[141] Kirousis L, Kranakis E, Krizanc D, et al. Power consumption in packet radio net-works [J]. Theoretical Computer Science. 2000, 243 (1-2): 289–305.
[142] Crossbow. Mote in Nnetwork Programming User Reference [EB/OL]. http://www.tinyos.net/tinyos-1.x/doc/xnp.pdf.
[143] Stathopoulos T, Heidemann J, Estrin D. A Remote Code Update Mechanism forWireless Sensor Networks [R]. 2003.
[144] Hui J, Culler D. The Dynamic Behavior of A Data Dissemination Protocol forNetwork Programming at Scale [C]. In Proceedings of the 2nd international con-ference on Embedded networked sensor systems (SenSys). 2004: 81–94.
[145] Wang L. MNP: Multihop Network Reprogramming Service for Sensor Network-s [C]. In Proceedings of the 2nd international conference on Embedded networkedsensor systems (SenSys). 2004: 285–286.
[146] Liang C, Musaloiu-Elefteri R, Terzis A. Typhoon: A Reliable Data DisseminationProtocol for Wireless Sensor Networks [C]. In Proceedings of the 5th EuropeanConference on Wireless Sensor Networks (EWSN). 2008: 268–285.
[147] Levis P, Gay D, Culler D. Active Sensor Networks [C]. In Proceedings of the Sec-ond USENIX/ACM Symposium on Network Systems Design and Implementation(NSDI). 2005: 343–356.
[148] GnawaliO,GreensteinB,JangK,etal.TheTENETArchitectureforTieredSensorNetworks [C]. In Proceedings of the 4nd international conference on Embeddednetworked sensor systems (SenSys). 2006: 153–166.
[149] BroderA,MitzenmacherM.NetworkApplicationsofBloomFilters:ASurvey[J].Internet Mathematics. 2004, 1: 485–509.
[150] CC2420 Packet Formats [EB/OL]. http://docs.tinyos.net/index.php/CC2420_Packet_Formats.
[151] Levis P, Lee N, Welsh M, et al. TOSSIM: Simulating Large Wireless Sensor Net-works of TinyOS Motes [C]. In Proceedings of the 1st ACM Conference on Em-bedded Networked Sensor Systems (SenSys). 2003: 381–396.
[152]俞建新,杨小虎.网络存储新技术评析[J].计算机工程.2006,32(20):120–122.
[153] Byers J, Luby M, Mitzenmacher M, et al. A Digital Fountain Approach to ReliableDistribution of Bulk Data [C]. In Proceedings of the Conference on Applications,Technologies, Architectures, and Protocols for Computer Communications (SIG-COMM). 1998: 56–67.
[154] Luby M. LT Codes [C]. In Proceedings of the 43rd Symposium on Foundations ofComputer Science (FOCS). 2002: 271–280.
[155] Servetto S D, Barrenechea G. Constrained random walks on random graphs: Rout-ing algorithms for large scale wireless sensor networks [C]. In Proceedings of theFirst ACMWorkshop on Wireless Sensor Networks and Applications (WSNA’02).2002: 12–21.
[156] Avin C, Ercal G. On the Cover Time of Random Geometric Graphs [C]. In Pro-ceedings of the 32nd International Colloquium of Automata, Languages and Pro-gramming (ICALP). 2005: 677–689.
[157] Berge C. The Theory of Graphs and Its Applications [M]. John Wiley & Sons,1962.
[158]路纲,周明天,唐勇,等.任意图支配集精确算法回顾[J].计算机学报.2010,33:1073–1087.
[159] Du D-Z, Pardalos P. Handbook of Combinatorial Optimization [M]. Kluwer Aca-demic Publishers, 2004.
[160] Kahng A B, Reda S. Match Twice and Stitch: A New TSP Tour ConstructionHeuristic [J]. Operations Research Letters. 2004, 32: 499–509.
[161] Wan P, Alzoubi K, Frieder O. Distributed Construction of Connected Dominat-ing Set in Wireless Ad Hoc Networks [C]. In Proceedings of 21th Annual JointConference of the IEEE Computer and Communications Societies (INFOCOM).2002: 1597–1604.
[162]彭绍亮.无线传感器网络中高效传输技术研究[D].长沙:国防科技大学,2008.
[163]陈颖文.无线传感器网络低能耗数据查询[D].长沙:国防科技大学,2007.
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