无线传感器网络查询处理关键技术研究
|
摘要
传感器网络在国防军事、医疗服务和交通控制等领域有着广泛的应用前景，近年来成为研究热点。与传统的网络不同，它具有许多显著的特点：传感器节点计算能力、存储能力和通信能力十分有限；节点感知的数据通常含有噪声，具有不确定性；受到节点移动、节点休眠、通信链路失效和周围环境等因素影响，传感器网络的拓扑结构频繁变化。传感器网络具备的上述特点使得开发其应用十分困难。考虑到传感器网络是以数据为中心的网络，用户使用它的主要目的是查询其感知的数据或监控的事件。传感器网络数据管理融合了流数据库、嵌入式数据和分布式数据库等技术，以有效管理传感器数据。它为用户提供简单的查询接口，屏蔽了查询处理的复杂性，能够极大地简化传感器网络应用的开发，已成为重要的传感器网络应用开发支撑软件。
传感器节点的能量由电池供应且通常无法更换，能量十分有限。因此能量高效的查询处理技术是传感器网络数据管理系统的核心。本文根据无线传感器网络的特点，研究了无线传感器网络的查询处理技术。主要研究成果包括如下几个方面：
(1)空间范围查询用于获得用户感兴趣区域内所有节点的感知数据。空间范围查询处理算法的能耗取决于三个参数：查询消息的转发次数、返回至Sink节点的感知数据数目、平均每个感知数据发送至Sink节点的转发次数。现有算法仅优化了其中的一个参数，而忽略了另外两个参数，导致能耗较大。提出了一种对以上三个参数进行全盘优化的空间范围查询处理算法，通过调度查询区域内的部分节点发送查询消息，在保证查询区域内所有节点接收到查询消息的同时，减少了查询消息的转发次数；利用相邻传感器节点的感知数据具有空间相关性，选择查询区域内的部分代表节点返回感知数据，Sink节点利用这些返回的数据对查询区域内其他节点的感知数据值进行估计，在保证查询结果精度的前提下，减少了返回至Sink节点的感知数据数目。另外，查询区域内节点的感知数据直接利用位置路由协议返回至Sink节点，使得平均每个感知数据发送至Sink节点的转发次数较少。理论分析和仿真实验结果表明，本文提出的算法在能耗方面优于现有的算法。
(2)为了减少不规则区域时空查询处理的能量消耗和提高查询结果准确性，提出了一种基于树的不规则区域时空数据收集查询算法，该算法将查询区域内的节点组织成一颗树，树中的节点依次将其感知数据发送至其父节点直至汇聚到树的根节点。针对不规则区域时空聚集查询，提出了一种基于路线的算法，该算法沿一条路线收集查询区域内节点的感知数据并对其进行聚集以生成最终的查询结果。这两种算法均通过将复杂的不规则查询区域划分为简单的凸多边形，降低了判断节点是否在查询区域内的计算复杂度，且保证仅查询区域内的节点发送感知数据，减少了能量消耗。仿真实验结果表明，提出的算法优于现有的针对规则区域的查询算法。
(3)针对包含不等值连接条件的流数据复杂聚集查询，草图技术能够计算流数据上等值连接大小的高精度近似值，直方图技术能够统计流数据的分布，本文结合了这两种技术的优势，提出了一种能够高效处理流数据上复杂聚集查询的算法。理论分析和实验结果表明,该算法具有较高的精度和较小的空间复杂度。
(4)传感器网络主要用于监测被监控区域的状态或发生的事件。当监控区域有事件发生时，用户通过获得以事件发生地为查询点的K近邻查询结果可以对事件发生的原因进行分析并预测其发展趋势。针对现有K近邻查询处理算法能量消耗大且查询成功率低的问题，提出了一种鲁棒的数据收集协议ROC，它将需要进行数据收集的区域划分为若干个环扇区，每个环扇区中有个一个簇头节点负责收集所在环扇区其他节点的感知数据，计算出部分查询结果并将将其发送至下一个环扇区的簇头节点。ROC利用位置路由协议绕过不存在节点的“空洞”区域，保证查询处理过程不被中断。基于ROC提出了一类传感器网络K近邻查询处理算法ROC-KNN。仅访问可能包含查询结果的节点，降低了算法的能量消耗。当簇头节点失效时，该节点所在环扇区内任意一个节点可代替它继续查询处理过程，提高了查询成功率。实验结果表明，ROC-KNN在能量消耗和查询成功率方面均优于现有基于路线的算法。
WSNs (Wireless Sensor networks) have a wide range of applications in military defense,medical services, traffic control and etc. WSN is a kind of distributed embedded system and thesensor nodes have very limited computing power, storage capacity, communication and energy.Therefore, the development of sensor network applications is extremely difficult. Unlike theother networks, wireless sensor networks are data-centric. The main purpose of using WSNs bythe users is to collect the events or data generated by the network. WSN database managementsystem provides users with a simple query interface which shields the complexity of queryprocessing. It greatly simplifies the development of sensor network applications and has becomean important middleware for WSN. Query processing is the core technology of WSN databasemanagement system.
(1) Spatial window query is used to obtain the sensory data of all the nodes in the user's areaof interest. The energy consumption of spatial window query processing algorithms depends onthree parameters: number of query message forwarding, number of sensory data returned to Sink,the average number of sensory data packets sent to the Sink by a node. The existing algorithmsonly optimize one of the parameters, while ignoring the other two parameters, resulting in a largeamout of energy. We propose a spatial window query processing algorithm which makes anholistic optimization of the above three parameters. It schedules some but not all nodes in thequery region to distribute query messages to ensure that all nodes within the query region receivea query message, which reduces the number of query message forwarding. It selects somerepresentative nodes in the query region to return their sensory data back to sink. The sensorydata of the other nodes in the query region is estimated by siink under the premise of ensuringthe accuracy of the query results. In addition, the sensory data in the query region is returned toSink directly by geographic routing protocol, so that the number of forwarding while sending asensory data o the Sink is reduced. The theory and simulation results show that our algorithm issuperior to existing algorithms in terms of energy consumption.
(2) In order to reduce the energy consumption of irregular spatio-temporal query processingand improve the query success rate, a tree-based algorithm is proposed to processspatio-temporal data collection queries with irregular query regions. It organizes sensor nodes inquery regions as a tree. The nodes in the tree send local data to their parents until reaching theroot of the tree. An itinerary-based algorithm to process spatio-temporal data aggregation querieswith irregular query regions is also proposed here. It collects the data of nodes in the queryregion and aggregates them along an itinerary to generate the final query result. Both of themdivide the complex and irregular query region into some simple convex polygons in order to reduce the computational complexity of determining whether the nodes are in the query regionand ensure that only the nodes in query regions send the sensed data, thus reducing the energyconsumption. The experimental results show that the proposed algorithms outperform theexistion spatio-temporal query processing algorithms for irregular region query.
(3) Sketch can estimate the equal join size of data stream with high precision and histogramcan calculate the distribution of data stream accurately. An efficient complex data aggregationquery processing algorithm for data stream is proposed based on sketch and histogramtechniques. The algorithm can provide approximate answers to a certain kind of complexaggregate queries. The theory and experimental results show that the algorithm has highprecision and small space complexity.
(4) Wireless sensor networks are mainly used for monitoring the status of an area or theevents happened. When an event occurs in the monitored area, users can obtain the results of knearest neighbors query to analyze the causes and forecast the development trend of the event.The current state-of-the-art k nearest neighbor query processing algorithms have fairy highenergy consumption and low query success rate. A robust data collection protocol called ROC isproposed in this paper. It divides the query region into several ring sectors. Each ring sector has acluster head node which collects the sensory data in it, calculates the partial query result andsends it to the cluster head node in the next ring sector. It takes advantage of geographic routingprotocol to bypass the region which has no nodes, which ensures that query processing is notinterrupted. We also propose a class of k nearest neighbor query processing algorithms calledROC-KNN based on ROC protocol. They only access the nodes which may contain query resultsin order to reduce the energy consumption. When a cluster head node fails during queryprocessing, any node within the sector which the cluster head node locates in can replace it tocontinue query processing, which improves the query success rate. The experimental resultsshow that ROC-KNN outperforms the existing itinerary-based algorithms in terms of energyconsumption and query success rate.
传感器节点的能量由电池供应且通常无法更换，能量十分有限。因此能量高效的查询处理技术是传感器网络数据管理系统的核心。本文根据无线传感器网络的特点，研究了无线传感器网络的查询处理技术。主要研究成果包括如下几个方面：
(1)空间范围查询用于获得用户感兴趣区域内所有节点的感知数据。空间范围查询处理算法的能耗取决于三个参数：查询消息的转发次数、返回至Sink节点的感知数据数目、平均每个感知数据发送至Sink节点的转发次数。现有算法仅优化了其中的一个参数，而忽略了另外两个参数，导致能耗较大。提出了一种对以上三个参数进行全盘优化的空间范围查询处理算法，通过调度查询区域内的部分节点发送查询消息，在保证查询区域内所有节点接收到查询消息的同时，减少了查询消息的转发次数；利用相邻传感器节点的感知数据具有空间相关性，选择查询区域内的部分代表节点返回感知数据，Sink节点利用这些返回的数据对查询区域内其他节点的感知数据值进行估计，在保证查询结果精度的前提下，减少了返回至Sink节点的感知数据数目。另外，查询区域内节点的感知数据直接利用位置路由协议返回至Sink节点，使得平均每个感知数据发送至Sink节点的转发次数较少。理论分析和仿真实验结果表明，本文提出的算法在能耗方面优于现有的算法。
(2)为了减少不规则区域时空查询处理的能量消耗和提高查询结果准确性，提出了一种基于树的不规则区域时空数据收集查询算法，该算法将查询区域内的节点组织成一颗树，树中的节点依次将其感知数据发送至其父节点直至汇聚到树的根节点。针对不规则区域时空聚集查询，提出了一种基于路线的算法，该算法沿一条路线收集查询区域内节点的感知数据并对其进行聚集以生成最终的查询结果。这两种算法均通过将复杂的不规则查询区域划分为简单的凸多边形，降低了判断节点是否在查询区域内的计算复杂度，且保证仅查询区域内的节点发送感知数据，减少了能量消耗。仿真实验结果表明，提出的算法优于现有的针对规则区域的查询算法。
(3)针对包含不等值连接条件的流数据复杂聚集查询，草图技术能够计算流数据上等值连接大小的高精度近似值，直方图技术能够统计流数据的分布，本文结合了这两种技术的优势，提出了一种能够高效处理流数据上复杂聚集查询的算法。理论分析和实验结果表明,该算法具有较高的精度和较小的空间复杂度。
(4)传感器网络主要用于监测被监控区域的状态或发生的事件。当监控区域有事件发生时，用户通过获得以事件发生地为查询点的K近邻查询结果可以对事件发生的原因进行分析并预测其发展趋势。针对现有K近邻查询处理算法能量消耗大且查询成功率低的问题，提出了一种鲁棒的数据收集协议ROC，它将需要进行数据收集的区域划分为若干个环扇区，每个环扇区中有个一个簇头节点负责收集所在环扇区其他节点的感知数据，计算出部分查询结果并将将其发送至下一个环扇区的簇头节点。ROC利用位置路由协议绕过不存在节点的“空洞”区域，保证查询处理过程不被中断。基于ROC提出了一类传感器网络K近邻查询处理算法ROC-KNN。仅访问可能包含查询结果的节点，降低了算法的能量消耗。当簇头节点失效时，该节点所在环扇区内任意一个节点可代替它继续查询处理过程，提高了查询成功率。实验结果表明，ROC-KNN在能量消耗和查询成功率方面均优于现有基于路线的算法。
WSNs (Wireless Sensor networks) have a wide range of applications in military defense,medical services, traffic control and etc. WSN is a kind of distributed embedded system and thesensor nodes have very limited computing power, storage capacity, communication and energy.Therefore, the development of sensor network applications is extremely difficult. Unlike theother networks, wireless sensor networks are data-centric. The main purpose of using WSNs bythe users is to collect the events or data generated by the network. WSN database managementsystem provides users with a simple query interface which shields the complexity of queryprocessing. It greatly simplifies the development of sensor network applications and has becomean important middleware for WSN. Query processing is the core technology of WSN databasemanagement system.
(1) Spatial window query is used to obtain the sensory data of all the nodes in the user's areaof interest. The energy consumption of spatial window query processing algorithms depends onthree parameters: number of query message forwarding, number of sensory data returned to Sink,the average number of sensory data packets sent to the Sink by a node. The existing algorithmsonly optimize one of the parameters, while ignoring the other two parameters, resulting in a largeamout of energy. We propose a spatial window query processing algorithm which makes anholistic optimization of the above three parameters. It schedules some but not all nodes in thequery region to distribute query messages to ensure that all nodes within the query region receivea query message, which reduces the number of query message forwarding. It selects somerepresentative nodes in the query region to return their sensory data back to sink. The sensorydata of the other nodes in the query region is estimated by siink under the premise of ensuringthe accuracy of the query results. In addition, the sensory data in the query region is returned toSink directly by geographic routing protocol, so that the number of forwarding while sending asensory data o the Sink is reduced. The theory and simulation results show that our algorithm issuperior to existing algorithms in terms of energy consumption.
(2) In order to reduce the energy consumption of irregular spatio-temporal query processingand improve the query success rate, a tree-based algorithm is proposed to processspatio-temporal data collection queries with irregular query regions. It organizes sensor nodes inquery regions as a tree. The nodes in the tree send local data to their parents until reaching theroot of the tree. An itinerary-based algorithm to process spatio-temporal data aggregation querieswith irregular query regions is also proposed here. It collects the data of nodes in the queryregion and aggregates them along an itinerary to generate the final query result. Both of themdivide the complex and irregular query region into some simple convex polygons in order to reduce the computational complexity of determining whether the nodes are in the query regionand ensure that only the nodes in query regions send the sensed data, thus reducing the energyconsumption. The experimental results show that the proposed algorithms outperform theexistion spatio-temporal query processing algorithms for irregular region query.
(3) Sketch can estimate the equal join size of data stream with high precision and histogramcan calculate the distribution of data stream accurately. An efficient complex data aggregationquery processing algorithm for data stream is proposed based on sketch and histogramtechniques. The algorithm can provide approximate answers to a certain kind of complexaggregate queries. The theory and experimental results show that the algorithm has highprecision and small space complexity.
(4) Wireless sensor networks are mainly used for monitoring the status of an area or theevents happened. When an event occurs in the monitored area, users can obtain the results of knearest neighbors query to analyze the causes and forecast the development trend of the event.The current state-of-the-art k nearest neighbor query processing algorithms have fairy highenergy consumption and low query success rate. A robust data collection protocol called ROC isproposed in this paper. It divides the query region into several ring sectors. Each ring sector has acluster head node which collects the sensory data in it, calculates the partial query result andsends it to the cluster head node in the next ring sector. It takes advantage of geographic routingprotocol to bypass the region which has no nodes, which ensures that query processing is notinterrupted. We also propose a class of k nearest neighbor query processing algorithms calledROC-KNN based on ROC protocol. They only access the nodes which may contain query resultsin order to reduce the energy consumption. When a cluster head node fails during queryprocessing, any node within the sector which the cluster head node locates in can replace it tocontinue query processing, which improves the query success rate. The experimental resultsshow that ROC-KNN outperforms the existing itinerary-based algorithms in terms of energyconsumption and query success rate.
引文
[1] Philip Levis, Sam Madden, David Gay, Joseph Polastre, Robert Szewczyk, Alec Woo, Eric Brewer, andDavid Culler. The emergence of networking abstractions and techniques in tinyos. In Proceedings of the1stconference on Symposium on Networked Systems Design and Implementation-Volume1, NSDI’04, pages1–1, Berkeley, CA, USA,2004. USENIX Association.
[2] Adam Dunkels, Bjorn Gronvall, and Thiemo Voigt. Contiki-a lightweight and flexible operating system fortiny networked sensors. In Proceedings of the29th Annual IEEE International Conference on LocalComputer Networks, LCN’04, pages455–462, Washington, DC, USA,2004. IEEE Computer Society.
[3] David Gay, Philip Levis, Robert von Behren, Matt Welsh, Eric Brewer, and David Culler. The nesc language:A holistic approach to networked embedded systems. In Proceedings of the ACM SIGPLAN2003conference on Programming language design and implementation, PLDI’03, pages1–11, New York, NY,USA,2003. ACM.
[4] Philippe Bonnet, Johannes Gehrke, and Praveen Seshadri. Towards sensor database systems. In Proceedingsof the Second International Conference on Mobile Data Management, MDM’01, pages3–14, London, UK,UK,2001. Springer-Verlag.
[5] Ramesh Govindan, Joseph M. Hellerstein, Wei Hong, Samuel Madden, Michael Franklin, and Scott Shenker.The sensor network as a database. Technical report,2002.
[6] Samuel R. Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. Tinydb: an acquisitionalquery processing system for sensor networks. ACM Trans. Database Syst.,30(1):122–173, March2005.
[7] Wei Ye, J. Heidemann, and D. Estrin. An energy-efficient mac protocol for wireless sensor networks. InINFOCOM2002. Twenty-First Annual Joint Conference of the IEEE Computer and CommunicationsSocieties. Proceedings. IEEE, volume3, pages1567–1576vol.3,2002.
[8] Tijs van Dam and Koen Langendoen. An adaptive energy-efficient mac protocol for wireless sensornetworks. In Proceedings of the1st international conference on Embedded networked sensor systems,SenSys’03, pages171–180, New York, NY, USA,2003. ACM.
[9] Kyle Jamieson, Hari Balakrishnan, and Y.C. Tay. Sift: A MAC Protocol for Event-Driven Wireless SensorNetworks, volume3868of Lecture Notes in Computer Science, pages260–275. Springer Berlin Heidelberg,2006.
[10] V. Rajendran, J.J. Garcia-Luna-Aceves, and K. Obraczka. Energy-efficient channel access scheduling forpower-constrained networks. In Wireless Personal Multimedia Communications,2002. The5th InternationalSymposium on, volume2, pages509–513vol.2, oct.2002.
[11] Venkatesh Rajendran, Katia Obraczka, and J. J. Garcia-Luna-Aceves. Energy-efficient collision-freemedium access control for wireless sensor networks. In Proceedings of the1st international conference onEmbedded networked sensor systems, SenSys’03, pages181–192, New York, NY, USA,2003. ACM.
[12] Gang Lu, Bhaskar Krishnamachari, and Cauligi S. Raghavendra. An adaptive energy-efficient andlow-latency mac for tree-based data gathering in sensor networks: Research articles. Wirel. Commun. Mob.Comput.,7(7):863–875, September2007.
[13] Injong Rhee, Ajit Warrier, Mahesh Aia, and Jeongki Min. Z-mac: a hybrid mac for wireless sensor networks.In Proceedings of the3rd international conference on Embedded networked sensor systems, SenSys’05,pages90–101, New York, NY, USA,2005. ACM.
[14] K. Sohrabi, J. Gao, V. Ailawadhi, and G.J. Pottie. Protocols for self-organization of a wireless sensornetwork. Personal Communications, IEEE,7(5):16–27, oct2000.
[15] Swetha Narayanaswamy, Vikas Kawadia, R. S. Sreenivas, and P. R. Kumar. Power control in ad-hocnetworks: Theory, architecture, algorithm and implementation of the compow protocol. In EuropeanWireless Conference, pages156–162,2002.
[16] M. Kubisch, H. Karl, A. Wolisz, L.C. Zhong, and J. Rabaey. Distributed algorithms for transmission powercontrol in wireless sensor networks. In Wireless Communications and Networking,2003. WCNC2003.2003IEEE, volume1, pages558–563vol.1, march2003.
[17] Li Li, J.Y. Halpern, P. Bahl, Yi-Min Wang, and R. Wattenhofer. A cone-based distributed topology-controlalgorithm for wireless multi-hop networks. Networking, IEEE/ACM Transactions on,13(1):147–159, feb.2005.
[18] N. Li and J.C. Hou. Topology control in heterogeneous wireless networks: problems and solutions. InINFOCOM2004. Twenty-third AnnualJoint Conference of the IEEE Computer and CommunicationsSocieties, volume1, pages4vol.(xxxv+2866), march2004.
[19] Santosh Kumar, Ten H. Lai, and József Balogh. On k-coverage in a mostly sleeping sensor network. InProceedings of the10th annual international conference on Mobile computing and networking,MobiCom’04, pages144–158, New York, NY, USA,2004. ACM.
[20] Kui Wu, Yong Gao, Fulu Li, and Yang Xiao. Lightweight deployment-aware scheduling for wireless sensornetworks. Mob. Netw. Appl.,10(6):837–852, December2005.
[21] Alberto Cerpa and Deborah Estrin. Ascent: Adaptive self-configuring sensor networks topologies. IEEETransactions on Mobile Computing,3(3):272–285, July2004.
[22] Fan Ye, Gary Zhong, Jesse Cheng, Songwu Lu, and Lixia Zhang. Peas: A robust energy conserving protocolfor long-lived sensor networks. In Proceedings of the23rd International Conference on DistributedComputing Systems, ICDCS’03, pages28–, Washington, DC, USA,2003. IEEE Computer Society.
[23] Chao Gui and Prasant Mohapatra. Power conservation and quality of surveillance in target tracking sensornetworks. In Proceedings of the10th annual international conference on Mobile computing and networking,MobiCom’04, pages129–143, New York, NY, USA,2004. ACM.
[24] Saurabh Ganeriwal, Ram Kumar, and Mani B. Srivastava. Timing-sync protocol for sensor networks. InProceedings of the1st international conference on Embedded networked sensor systems, SenSys’03, pages138–149, New York, NY, USA,2003. ACM.
[25] M.L. Sichitiu and C. Veerarittiphan. Simple, accurate time synchronization for wireless sensor networks. InWireless Communications and Networking,2003. WCNC2003.2003IEEE, volume2, pages1266–1273vol.2, march2003.
[26] Hui Dai and Richard Han. Tsync: a lightweight bidirectional time synchronization service for wirelesssensor networks. SIGMOBILE Mob. Comput. Commun. Rev.,8(1):125–139, January2004.
[27] Jana van Greunen and Jan Rabaey. Lightweight time synchronization for sensor networks. In Proceedings ofthe2nd ACM international conference on Wireless sensor networks and applications, WSNA’03, pages11–19, New York, NY, USA,2003. ACM.
[28] Qun Li and Daniela Rus. Global clock synchronization in sensor networks. IEEE Transactions onComputers,55:214–226,2006.
[29] Kay R mer. Time synchronization in ad hoc networks. In Proceedings of the2nd ACM internationalsymposium on Mobile ad hoc networking&computing, MobiHoc’01, pages173–182, New York, NY,USA,2001. ACM.
[30] Dragos Niculescu and Badri Nath. Error characteristics of ad hoc positioning systems (aps). In Proceedingsof the5th ACM international symposium on Mobile ad hoc networking and computing, MobiHoc’04, pages20–30, New York, NY, USA,2004. ACM.
[31] Yi Shang, Wheeler Ruml, Ying Zhang, and Markus Fromherz. Localization from connectivity in sensornetworks. IEEE Trans. Parallel Distrib. Syst.,15(11):961–974, November2004.
[32] Suman Nath, Amol Deshpande, Yan Ke, Phillip B. Gibbons, Brad Karp, and Srinivasan Seshan. Irisnet: anarchitecture for internet-scale sensing services. In Proceedings of the29th international conference on Verylarge data bases-Volume29, VLDB’03, pages1137–1140. VLDB Endowment,2003.
[33] Bo Xu, Fatemeh Vafaee, and Ouri Wolfson. In-network query processing in mobile p2p databases. InProceedings of the17th ACM SIGSPATIAL International Conference on Advances in GeographicInformation Systems, GIS’09, pages207–216, New York, NY, USA,2009. ACM.
[34] Michele Albano and Stefano Chessa. Publish/subscribe in wireless sensor networks based on data centricstorage. In Proceedings of the1st International Workshop on Context-Aware Middleware and Services:affiliated with the4th International Conference on Communication System Software and Middleware(COMSWARE2009), CAMS’09, pages37–42, New York, NY, USA,2009. ACM.
[35] Sylvia Ratnasamy, Brad Karp, Li Yin, Fang Yu, Deborah Estrin, Ramesh Govindan, and Scott Shenker. Ght:a geographic hash table for data-centric storage. In Proceedings of the1st ACM international workshop onWireless sensor networks and applications, WSNA’02, pages78–87, New York, NY, USA,2002. ACM.
[36] Scott Shenker, Sylvia Ratnasamy, Brad Karp, Ramesh Govindan, and Deborah Estrin. Data-centric storagein sensornets. SIGCOMM Comput. Commun. Rev.,33(1):137–142, January2003.
[37] Mohamed Aly, Kirk Pruhs, and Panos K. Chrysanthis. Kddcs: a load-balanced in-network data-centricstorage scheme for sensor networks. In Proceedings of the15th ACM international conference onInformation and knowledge management, CIKM’06, pages317–326, New York, NY, USA,2006. ACM.
[38] Abhishek Ghose, Jens Grossklags, and John Chuang. Resilient data-centric storage in wireless ad-hoc sensornetworks. In Proceedings of the4th International Conference on Mobile Data Management, MDM’03,pages45–62, London, UK, UK,2003. Springer-Verlag.
[39] Wen-Hwa Liao, Kuei-PIng Shih, and Wan-Chi Wu. A grid-based dynamic load balancing approach fordata-centric storage in wireless sensor networks. Comput. Electr. Eng.,36(1):19–30, January2010.
[40] Deepak Ganesan, Deborah Estrin, and John Heidemann. Dimensions: why do we need a new data handlingarchitecture for sensor networks? SIGCOMM Comput. Commun. Rev.,33(1):143–148, January2003.
[41] B. Greenstein, D. Estrin, R. Govindan, S. Ratnasamy, and S. Shenker. Difs: a distributed index for featuresin sensor networks. In Sensor Network Protocols and Applications,2003. Proceedings of the First IEEE.2003IEEE International Workshop on, pages163–173, may2003.
[42] Xin Li, Young Jin Kim, Ramesh Govindan, and Wei Hong. Multi-dimensional range queries in sensornetworks. In Proceedings of the1st international conference on Embedded networked sensor systems,SenSys’03, pages63–75, New York, NY, USA,2003. ACM.
[43] Murat Demirbas and Hakan Ferhatosmanoglu. Peer-to-peer spatial queries in sensor networks. InProceedings of the3rd International Conference on Peer-to-Peer Computing, P2P’03, pages32–39,Washington, DC, USA,2003. IEEE Computer Society.
[44] T.M. Gil and S. Madden. Scoop: An adaptive indexing scheme for stored data in sensor networks. In DataEngineering,2007. ICDE2007. IEEE23rd International Conference on, pages1345–1349, april2007.
[45] Yong Yao and Johannes Gehrke. The cougar approach to in-network query processing in sensor networks.SIGMOD Rec.,31(3):9–18, September2002.
[46] C. Castelluccia, E. Mykletun, and G. Tsudik. Efficient aggregation of encrypted data in wireless sensornetworks. In Mobile and Ubiquitous Systems: Networking and Services,2005. MobiQuitous2005. TheSecond Annual International Conference on, pages109–117, july2005.
[47] Claude Castelluccia, Aldar C-F. Chan, Einar Mykletun, and Gene Tsudik. Efficient and provably secureaggregation of encrypted data in wireless sensor networks. ACM Trans. Sen. Netw.,5(3):20:1–20:36, June2009.
[48] M.M. Groat, Wenbo He, and S. Forrest. Kipda: k-indistinguishable privacy-preserving data aggregation inwireless sensor networks. In INFOCOM,2011Proceedings IEEE, pages2024–2032, april2011.
[49] Wenbo He, Xue Liu, Hoang Nguyen, K. Nahrstedt, and T.T. Abdelzaher. Pda: Privacy-preserving dataaggregation in wireless sensor networks. In INFOCOM2007.26th IEEE International Conference onComputer Communications. IEEE, pages2045–2053, may2007.
[50] Bo Sheng and Qun Li. Verifiable privacy-preserving sensor network storage for range query. IEEETransactions on Mobile Computing,10(9):1312–1326, September2011.
[51] Dirk Westhoff, Joao Girao, and Mithun Acharya. Concealed data aggregation for reverse multicast traffic insensor networks: Encryption, key distribution, and routing adaptation. IEEE Transactions on MobileComputing,5(10):1417–1431, October2006.
[52] Yonglei Yao, Naixue Xiong, Jong Hyuk Park, Li Ma, and Jingfa Liu. Privacy-preserving max/min query intwo-tiered wireless sensor networks. Computers& Mathematics with Applications,(0):–,2012.
[53] Bogdan Carbunar, Yang Yu, Weidong Shi, Michael Pearce, and Venu Vasudevan. Query privacy in wirelesssensor networks. ACM Trans. Sen. Netw.,6(2):14:1–14:34, March2010.
[54] Na Li, Nan Zhang, Sajal K. Das, and Bhavani Thuraisingham. Privacy preservation in wireless sensornetworks: A state-of-the-art survey. Ad Hoc Netw.,7(8):1501–1514, November2009.
[55] Yun Li and Jian Ren. Source-location privacy through dynamic routing in wireless sensor networks. InProceedings of the29th conference on Information communications, INFOCOM’10, pages2660–2668,Piscataway, NJ, USA,2010. IEEE Press.
[56] Celal Ozturk, Yanyong Zhang, and Wade Trappe. Source-location privacy in energy-constrained sensornetwork routing. In Proceedings of the2nd ACM workshop on Security of ad hoc and sensor networks,SASN’04, pages88–93, New York, NY, USA,2004. ACM.
[57] Pandurang Kamat, Wenyuan Xu, Wade Trappe, and Yanyong Zhang. Temporal privacy in wireless sensornetworks: Theory and practice. ACM Trans. Sen. Netw.,5(4):28:1–28:24, November2009.
[58] JieYuan Li and Guohua Liu. On the representation and querying of sets of possible worlds in thek-anonymity privacy protecting model. In Proceedings of the2011Seventh International Conference onComputational Intelligence and Security, CIS’11, pages662–664, Washington, DC, USA,2011. IEEEComputer Society.
[59] Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. Tag: a tiny aggregation servicefor ad-hoc sensor networks. SIGOPS Oper. Syst. Rev.,36(SI):131–146, December2002.
[60] M. Lee and V.W.S. Wong. An energy-aware spanning tree algorithm for data aggregation in wireless sensornetworks. In Communications, Computers and signal Processing,2005. PACRIM.2005IEEE Pacific RimConference on, pages300–303, aug.2005.
[61] Yingqi Xu, Wang-Chien Lee, Jianliang Xu, and G. Mitchell. Processing window queries in wireless sensornetworks. In Data Engineering,2006. ICDE’06. Proceedings of the22nd International Conference on,pages70–80, april2006.
[62] A. Sharaf, Jonathan Beaver, Alexandros Labrinidis, and K. Chrysanthis. Balancing energy efficiency andquality of aggregate data in sensor networks. The VLDB Journal,13(4):384–403, December2004.
[63] Suman Nath, Phillip B. Gibbons, Srinivasan Seshan, and Zachary R. Anderson. Synopsis diffusion forrobust aggregation in sensor networks. In Proceedings of the2nd international conference on Embeddednetworked sensor systems, SenSys’04, pages250–262, New York, NY, USA,2004. ACM.
[64] Amit Manjhi, Suman Nath, and Phillip B. Gibbons. Tributaries and deltas: efficient and robust aggregationin sensor network streams. In Proceedings of the2005ACM SIGMOD international conference onManagement of data, SIGMOD’05, pages287–298, New York, NY, USA,2005. ACM.
[65] Kai-Wei Fan and P. Sinha. Distributed online data aggregation for large scale sensor networks. In Mobile AdHoc and Sensor Systems,2008. MASS2008.5th IEEE International Conference on, pages153–162,292008-oct.22008.
[66] Jie Gao, Leonidas Guibas, Nikola Milosavljevic, and John Hershberger. Sparse data aggregation in sensornetworks. In Proceedings of the6th international conference on Information processing in sensor networks,IPSN’07, pages430–439, New York, NY, USA,2007. ACM.
[67]程思瑶，李建中.无线传感器网络中(ε,δ)-近似聚集算法.软件学报,(08):1936–1953,2010.
[68] Xueyan Tang and Jianliang Xu. Adaptive data collection strategies for lifetime-constrained wireless sensornetworks. IEEE Trans. Parallel Distrib. Syst.,19(6):721–734, June2008.
[69] Xueyan Tang and Jianliang Xu. Optimizing lifetime for continuous data aggregation with precisionguarantees in wireless sensor networks. IEEE/ACM Trans. Netw.,16(4):904–917, August2008.
[70] Jeffrey Considine, Marios Hadjieleftheriou, Feifei Li, John Byers, and George Kollios. Robust approximateaggregation in sensor data management systems. ACM Trans. Database Syst.,34(1):6:1–6:35, April2009.
[71] Adam Silberstein, Kamesh Munagala, and Jun Yang. Energy-efficient monitoring of extreme values insensor networks. In Proceedings of the2006ACM SIGMOD international conference on Management ofdata, SIGMOD’06, pages169–180, New York, NY, USA,2006. ACM.
[72] Amol Deshpande, Carlos Guestrin, Samuel R. Madden, Joseph M. Hellerstein, and Wei Hong. Model-drivendata acquisition in sensor networks. In Proceedings of the Thirtieth international conference on Very largedata bases-Volume30, VLDB’04, pages588–599. VLDB Endowment,2004.
[73] Carlos Guestrin, Peter Bodik, Romain Thibaux, Mark Paskin, and Samuel Madden. Distributed regression:an efficient framework for modeling sensor network data. In Proceedings of the3rd internationalsymposium on Information processing in sensor networks, IPSN’04, pages1–10, New York, NY, USA,2004. ACM.
[74] Yao-Chung Fan and Arbee L.P. Chen. Efficient and robust schemes for sensor data aggregation based onlinear counting. IEEE Transactions on Parallel and Distributed Systems,21:1675–1691,2010.
[75] Boris Jan Bonfils and Philippe Bonnet. Adaptive and decentralized operator placement for in-network queryprocessing. In Proceedings of the2nd international conference on Information processing in sensornetworks, IPSN’03, pages47–62, Berlin, Heidelberg,2003. Springer-Verlag.
[76] J. Gehrke and S. Madden. Query processing in sensor networks. Pervasive Computing, IEEE,3(1):46–55,jan.-march2004.
[77] Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. The design of an acquisitionalquery processor for sensor networks. In Proceedings of the2003ACM SIGMOD international conferenceon Management of data, SIGMOD’03, pages491–502, New York, NY, USA,2003. ACM.
[78] Daniel J. Abadi, Samuel Madden, and Wolfgang Lindner. Reed: robust, efficient filtering and eventdetection in sensor networks. In Proceedings of the31st international conference on Very large data bases,VLDB’05, pages769–780. VLDB Endowment,2005.
[79] Himanshu Gupta and Vishal Chowdhary. Communication-efficient implementation of join in sensornetworks. Ad Hoc Netw.,5(6):929–942, August2007.
[80] Aditi Pandit and Himanshu Gupta. Communication-efficient implementation of range-joins in sensornetworks. In Proceedings of the11th international conference on Database Systems for AdvancedApplications, DASFAA’06, pages859–869, Berlin, Heidelberg,2006. Springer-Verlag.
[81]刘亮,秦小麟,戴华,杨丰.基于环扇区的无线传感器网络k近邻查询处理算法.小型微型计算机系统,(08):1487–1492,2011.
[82] Yuxia Yao, Xueyan Tang, and Ee-Peng Lim. Localized monitoring of knn queries in wireless sensornetworks. The VLDB Journal,18(1):99–117, January2009.
[83]郭龙江,李建中,李贵林.无线传感器网络环境下时-空查询处理方法.软件学报,(04):794–805,2006.
[84] Alexandru Coman, Mario A. Nascimento, and J rg Sander. A framework for spatio-temporal queryprocessing over wireless sensor networks. In Proceeedings of the1st international workshop on Datamanagement for sensor networks: in conjunction with VLDB2004, DMSN’04, pages104–110, New York,NY, USA,2004. IEEE.
[85] A. Coman, J. Sander, and M.A. Nascimento. An analysis of spatio-temporal query processing in sensornetworks. In21st International Conference on Data Engineering Workshops, page1190, april2005.
[86] Alexandru Coman, Mario A. Nascimento, and J rg Sander. Exploiting redundancy in sensor networks forenergy efficient processing of spatiotemporal region queries. In Proceedings of the14th ACM internationalconference on Information and knowledge management, CIKM’05, pages187–194, New York, NY, USA,2005. ACM.
[87] Dina Goldin, Mingjun Song, Ayferi Kutlu, Huayan Gao, and Hardik Dave. Georouting and delta-gathering:Efficient data propagation techniques for geosensor networks. In First Workshop on Geo Sensor Networks,pages9–11,2003.
[88] Julian Winter and Wang-Chien Lee. Kpt: a dynamic knn query processing algorithm for location-awaresensor networks. In Proceeedings of the1st international workshop on Data management for sensornetworks: in conjunction with VLDB2004, DMSN’04, pages119–124, New York, NY, USA,2004. ACM.
[89] Yingqi Xu, Tao-Yang Fu, Wang-Chien Lee, and Julian Winter. Processing k nearest neighbor queries inlocation-aware sensor networks. Signal Process.,87(12):2861–2881, December2007.
[90] Tao-Young Fu, Wen-Chih Peng, and Wang-Chien Lee. Optimizing parallel itineraries for knn queryprocessing in wireless sensor networks. In Proceedings of the sixteenth ACM conference on Conference oninformation and knowledge management, CIKM’07, pages391–400, New York, NY, USA,2007. ACM.
[91] Shan-Hung Wu, Kun-Ta Chuang, Chung-Min Chen, and Ming-Syan Chen. Diknn: An itinerary-based knnquery processing algorithm for mobile sensor networks. In Proceedings of the2007IEEE23rd InternationalConference on Data Engineering, volume0, pages456–465, Los Alamitos, CA, USA,2007. IEEEComputer Society.
[92] Shan-Hung Wu, Kun-Ta Chuang, Chung-Min Chen, and Ming-Syan Chen. Toward the optimalitinerary-based knn query processing in mobile sensor networks. IEEE Trans. on Knowl. and Data Eng.,20(12):1655–1668, December2008.
[93] Hakan Hacigümüs, Bala Iyer, Chen Li, and Sharad Mehrotra. Executing sql over encrypted data in thedatabase-service-provider model. In Proceedings of the2002ACM SIGMOD international conference onManagement of data, SIGMOD’02, pages216–227, New York, NY, USA,2002. ACM.
[94] Jing Shi, Rui Zhang, and Yanchao Zhang. Secure range queries in tiered sensor networks. In INFOCOM2009, IEEE, pages945–953, april2009.
[95] Jing Shi, Rui Zhang, and Yanchao Zhang. A spatiotemporal approach for secure range queries in tieredsensor networks. Wireless Communications, IEEE Transactions on,10(1):264–273, january2011.
[96] Fei Chen and A.X. Liu. Safeq: Secure and efficient query processing in sensor networks. In INFOCOM,2010Proceedings IEEE, pages1–9, march2010.
[97] C. Castelluccia, E. Mykletun, and G. Tsudik. Efficient aggregation of encrypted data in wireless sensornetworks. In Mobile and Ubiquitous Systems: Networking and Services,2005. MobiQuitous2005. TheSecond Annual International Conference on, pages109–117, july2005.
[98] Aldar C-F. Chan and Claude Castelluccia. A security framework for privacy-preserving data aggregation inwireless sensor networks. ACM Trans. Sen. Netw.,7(4):29:1–29:45, February2011.
[99] Taiming Feng, Chuang Wang, Wensheng Zhang, and Lu Ruan. Confidentiality protection for distributedsensor data aggregation. In INFOCOM2008. The27th Conference on Computer Communications. IEEE,pages56–60, april2008.
[100] Hongjuan Li, Kai Lin, and Keqiu Li. Energy-efficient and high-accuracy secure data aggregation inwireless sensor networks. Computer Communications,34(4):591–597,2011.Special issue:Building Secure Parallel and Distributed Networks and Systems .
[101] Chien-Liang Fok, Gruia-Catalin Roman, and Chenyang Lu. Agilla: A mobile agent middleware forself-adaptive wireless sensor networks. ACM Trans. Auton. Adapt. Syst.,4(3):16:1–16:26, July2009.
[102] Xue Wang, Aiguo Jiang, and Sheng Wang. Mobile agent based wireless sensor network for intelligentmaintenance. In De-Shuang Huang, Xiao-Ping Zhang, and Guang-Bin Huang, editors, Advances inIntelligent Computing, volume3645of Lecture Notes in Computer Science, pages316–325. Springer Berlin/Heidelberg,2005.10.1007/11538356_33.
[103] Alexandru Coman, Joerg Sander, and Mario A. Nascimento. Adaptive processing of historical spatial rangequeries in peer-to-peer sensor networks. Distrib. Parallel Databases,22(2-3):133–163, December2007.
[104] David Chu, Amol Deshpande, Joseph M. Hellerstein, and Wei Hong. Approximate data collection in sensornetworks using probabilistic models. In Proceedings of the22nd International Conference on DataEngineering, ICDE’06, pages48–, Washington, DC, USA,2006. IEEE Computer Society.
[105] Antonios Deligiannakis, Yannis Kotidis, and Nick Roussopoulos. Compressing historical information insensor networks. In Proceedings of the2004ACM SIGMOD international conference on Management ofdata, SIGMOD’04, pages527–538, New York, NY, USA,2004. ACM.
[106] Sorabh Gandhi, Suman Nath, Subhash Suri, and Jie Liu. Gamps: compressing multi sensor data bygrouping and amplitude scaling. In Proceedings of the2009ACM SIGMOD International Conference onManagement of data, SIGMOD’09, pages771–784, New York, NY, USA,2009. ACM.
[107] Pratik Biswas, Tzu-Chen Lian, Ta-Chung Wang, and Yinyu Ye. Semidefinite programming basedalgorithms for sensor network localization. ACM Trans. Sen. Netw.,2(2):188–220, May2006.
[108] Amin Karbasi and Sewoong Oh. Distributed sensor network localization from local connectivity:performance analysis for the hop-terrain algorithm. In Proceedings of the ACM SIGMETRICS internationalconference on Measurement and modeling of computer systems, SIGMETRICS’10, pages61–70, NewYork, NY, USA,2010. ACM.
[109] Mo Li and Yunhao Liu. Rendered path: range-free localization in anisotropic sensor networks with holes. InProceedings of the13th annual ACM international conference on Mobile computing and networking,MobiCom’07, pages51–62, New York, NY, USA,2007. ACM.
[110] Guoqiang Mao, Bars Fidan, and Brian D. O. Anderson. Wireless sensor network localization techniques.omput. Netw.,51(10):2529–2553, July2007.
[111] Brad Karp and H. T. Kung. Gpsr: greedy perimeter stateless routing for wireless networks. In Proceedingsof the6th annual international conference on Mobile computing and networking, MobiCom’00, pages243–254, New York, NY, USA,2000. ACM.
[112] Amol Deshpande, Carlos Guestrin, Wei Hong, and Samuel Madden. Exploiting correlated attributes inacquisitional query processing. In Proceedings of the21st International Conference on Data Engineering,ICDE’05, pages143–154, Washington, DC, USA,2005. IEEE Computer Society.
[113] Yannis Kotidis. Snapshot queries: Towards data-centric sensor networks. In Proceedings of the21stInternational Conference on Data Engineering, ICDE’05, pages131–142, Washington, DC, USA,2005.IEEE Computer Society.
[114] Siyao Cheng and Jianzhong Li. Sampling based (epsilon, delta)-approximate aggregation algorithm insensor networks. In Proceedings of the200929th IEEE International Conference on Distributed ComputingSystems, ICDCS’09, pages273–280, Washington, DC, USA,2009. IEEE Computer Society.
[115] Siyao Cheng, Jianzhong Li, Qianqian Ren, and Lei Yu. Bernoulli sampling based (ε,δ)-approximate aggregation in large-scale sensor networks. In Proceedings of the29th conference onInformation communications, INFOCOM’10, pages1181–1189, Piscataway, NJ, USA,2010. IEEE Press.
[116] Liu Yu, Jianzhong Li, Hong Gao, and Xiaolin Fang. Enabling?-approximate querying in sensor networks.Proc. VLDB Endow.,2(1):169–180, August2009.
[117]孙利民,李建中,陈渝.无线传感器网络.清华大学出版社,2005.
[118]刘亮,秦小麟,戴华,严伟中,潘锦基.能量高效的无线传感器网络时空查询处理算法.电子学报,38(01):54–59,2010.
[119] Antonin Guttman. R-trees: a dynamic index structure for spatial searching. SIGMOD Rec.,14(2):47–57,June1984.
[120] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: a survey.Computer Networks,38(4):393–422,2002.
[121]清华大学出版社.计算几何———算法分析与设计.清华大学出版社,2005.
[122] Theodore S. Rappaport. Wireless Communications: Principles and Practice. IEEE Press, Piscataway, NJ,USA,1st edition,1996.
[123] Gregory Piatetsky-Shapiro and Charles Connell. Accurate estimation of the number of tuples satisfying acondition. SIGMOD Rec.,14(2):256–276, June1984.
[124] Viswanath Poosala, Peter J. Haas, Yannis E. Ioannidis, and Eugene J. Shekita. Improved histograms forselectivity estimation of range predicates. SIGMOD Rec.,25(2):294–305, June1996.
[125] Michael Greenwald and Sanjeev Khanna. Space-efficient online computation of quantile summaries.SIGMOD Rec.,30(2):58–66, May2001.
[126] Yannis E. Ioannidis and Viswanath Poosala. Balancing histogram optimality and practicality for queryresult size estimation. SIGMOD Rec.,24(2):233–244, May1995.
[127] H. V. Jagadish, Nick Koudas, S. Muthukrishnan, Viswanath Poosala, Kenneth C. Sevcik, and Torsten Suel.Optimal histograms with quality guarantees. In Proceedings of the24rd International Conference on VeryLarge Data Bases, VLDB’98, pages275–286, San Francisco, CA, USA,1998. Morgan KaufmannPublishers Inc.
[128] Anna C. Gilbert, Yannis Kotidis, S. Muthukrishnan, and Martin Strauss. Surfing wavelets on streams:One-pass summaries for approximate aggregate queries. In Proceedings of the27th International Conferenceon Very Large Data Bases, VLDB’01, pages79–88, San Francisco, CA, USA,2001. Morgan KaufmannPublishers Inc.
[129] Jeffrey S. Vitter. Random sampling with a reservoir. ACM Trans. Math. Softw.,11(1):37–57, March1985.
[130] Phillip B. Gibbons and Yossi Matias. New sampling-based summary statistics for improving approximatequery answers. SIGMOD Rec.,27(2):331–342, June1998.
[131] Brian Babcock, Mayur Datar, and Rajeev Motwani. Sampling from a moving window over streaming data.In Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms, SODA’02, pages633–634, Philadelphia, PA, USA,2002. Society for Industrial and Applied Mathematics.
[132] Alin Dobra, Minos Garofalakis, Johannes Gehrke, and Rajeev Rastogi. Processing complex aggregatequeries over data streams. In Proceedings of the2002ACM SIGMOD international conference onManagement of data, SIGMOD’02, pages61–72, New York, NY, USA,2002. ACM.
[133] Johannes Gehrke, Flip Korn, and Divesh Srivastava. On computing correlated aggregates over continualdata streams. SIGMOD Rec.,30(2):13–24, May2001.
[134] Alin Dobra, Minos Garofalakis, Johannes Gehrke, and Rajeev Rastogi. Processing complex aggregatequeries over data streams. In Proceedings of the2002ACM SIGMOD international conference onManagement of data, SIGMOD’02, pages61–72, New York, NY, USA,2002. ACM.
[135] Johannes Gehrke, Flip Korn, and Divesh Srivastava. On computing correlated aggregates over continualdata streams. SIGMOD Rec.,30(2):13–24, May2001.
[136] Noga Alon, Phillip B. Gibbons, Yossi Matias, and Mario Szegedy. Tracking join and self-join sizes inlimited storage. In Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium onPrinciples of database systems, PODS’99, pages10–20, New York, NY, USA,1999. ACM.
[137] Noga Alon, Yossi Matias, and Mario Szegedy. The space complexity of approximating the frequencymoments. In Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, STOC’96,pages20–29, New York, NY, USA,1996. ACM.
[138] Graham Cormode and Marios Hadjieleftheriou. Methods for finding frequent items in data streams. TheVLDB Journal,19(1):3–20, February2010.
[139] Michael Greenwald and Sanjeev Khanna. Space-efficient online computation of quantile summaries.SIGMOD Rec.,30(2):58–66, May2001.
[140]韩近强,杨冬青,唐世渭.数据流处理中一种自适应的直方图维护算法. In第二十届全国数据库学术会议, pages178–181,中国湖南长沙,2003.
[141] Baichen Chen, Weifa Liang, and Jeffrey Xu Yu. Online time interval top-k queries in wireless sensornetworks. In Proceedings of the2010Eleventh International Conference on Mobile Data Management,MDM’10, pages177–182, Washington, DC, USA,2010. IEEE Computer Society.
[142] Baichen Chen, Weifa Liang, Rui Zhou, and Jeffrey Xu Yu. Energy-efficient top-k query processing inwireless sensor networks. In Proceedings of the19th ACM international conference on Information andknowledge management, CIKM’10, pages329–338, New York, NY, USA,2010. ACM.
[143] Xingjie Liu, Jianliang Xu, and Wang-Chien Lee. A cross pruning framework for top-k data collection inwireless sensor networks. In Proceedings of the2010Eleventh International Conference on Mobile DataManagement, MDM’10, pages157–166, Washington, DC, USA,2010. IEEE Computer Society.
[144] Hai Thanh Mai and Myoung Ho Kim. Processing continuous top-k data collection queries inlifetime-constrained wireless sensor networks. In Proceedings of the5th International Conference onUbiquitous Information Management and Communication, ICUIMC’11, pages14:1–14:10, New York, NY,USA,2011. ACM.
[145]Minji Wu, Jianliang Xu, Xueyan Tang, and Wang-Chien Lee. Top-k monitoring in wireless sensor networks.IEEE Trans. on Knowl. and Data Eng.,19(7):962–976, July2007.
[146] Baichen Chen, Weifa Liang, and Jeffrey Xu Yu. Progressive skyline query evaluation and maintenance inwireless sensor networks. In Proceedings of the18th ACM conference on Information and knowledgemanagement, CIKM’09, pages1445–1448, New York, NY, USA,2009. ACM.
[147] Weifa Liang, Baichen Chen, and Jeffrey Xu Yu. Energy-efficient skyline query processing and maintenancein sensor networks. In Proceedings of the17th ACM conference on Information and knowledgemanagement, CIKM’08, pages1471–1472, New York, NY, USA,2008. ACM.
[148] I-Fang Su, Yu-Chi Chung, Chiang Lee, and Yi-Ying Lin. Efficient skyline query processing in wirelesssensor networks. J. Parallel Distrib. Comput.,70(6):680–698, June2010.
[149] Adam Silberstein, Rebecca Braynard, and Jun Yang. Constraint chaining: on energy-efficient continuousmonitoring in sensor networks. In Proceedings of the2006ACM SIGMOD international conference onManagement of data, SIGMOD’06, pages157–168, New York, NY, USA,2006. ACM.
[150] Daniel J. Abadi, Samuel Madden, and Wolfgang Lindner. Reed: robust, efficient filtering and eventdetection in sensor networks. In Proceedings of the31st international conference on Very large data bases,VLDB’05, pages769–780. VLDB Endowment,2005.
[151] Alexandru Coman, Mario A. Nascimento, and Jorg Sander. On join location in sensor networks. InProceedings of the2007International Conference on Mobile Data Management, MDM’07, pages190–197,Washington, DC, USA,2007. IEEE Computer Society.
[152] Moustafa A. Hammad, Walid G. Aref, and Ahmed K. Elmagarmid. Query processing of multi-way streamwindow joins. The VLDB Journal,17(3):469–488, May2008.
[153] Svilen R. Mihaylov, Marie Jacob, Zachary G. Ives, and Sudipto Guha. Dynamic join optimization inmulti-hop wireless sensor networks. Proc. VLDB Endow.,3(1-2):1279–1290, September2010.
[154] Mirco Stern, Klemens B hm, and Erik Buchmann. Processing continuous join queries in sensor networks: afiltering approach. In Proceedings of the2010ACM SIGMOD International Conference on Management ofdata, SIGMOD’10, pages267–278, New York, NY, USA,2010. ACM.
[155] Mirco Stern, Erik Buchmann, and Klemens B hm. Towards efficient processing of general-purpose joins insensor networks. In Proceedings of the2009IEEE International Conference on Data Engineering, ICDE’09,pages126–137, Washington, DC, USA,2009. IEEE Computer Society.
[156] D. Zeinalipour-Yazti, Z. Vagena, D. Gunopulos, V. Kalogeraki, V. Tsotras, M. Vlachos, N. Koudas, andD. Srivastava. The threshold join algorithm for top-k queries in distributed sensor networks. In Proceedingsof the2nd international workshop on Data management for sensor networks, DMSN’05, pages61–66, NewYork, NY, USA,2005. ACM.
[157] Gsli R. Hjaltason and Hanan Samet. Distance browsing in spatial databases. áCM Trans. Database Syst.,24(2):265–318, June1999.
[158] Nick Roussopoulos, Stephen Kelley, and Frédéric Vincent. Nearest neighbor queries. SIGMOD Rec.,24(2):71–79, May1995.
[159] Yuxia Yao, Xueyan Tang, and Ee-Peng Lim. Localized monitoring of knn queries in wireless sensornetworks. The VLDB Journal,18(1):99–117, January2009.
[2] Adam Dunkels, Bjorn Gronvall, and Thiemo Voigt. Contiki-a lightweight and flexible operating system fortiny networked sensors. In Proceedings of the29th Annual IEEE International Conference on LocalComputer Networks, LCN’04, pages455–462, Washington, DC, USA,2004. IEEE Computer Society.
[3] David Gay, Philip Levis, Robert von Behren, Matt Welsh, Eric Brewer, and David Culler. The nesc language:A holistic approach to networked embedded systems. In Proceedings of the ACM SIGPLAN2003conference on Programming language design and implementation, PLDI’03, pages1–11, New York, NY,USA,2003. ACM.
[4] Philippe Bonnet, Johannes Gehrke, and Praveen Seshadri. Towards sensor database systems. In Proceedingsof the Second International Conference on Mobile Data Management, MDM’01, pages3–14, London, UK,UK,2001. Springer-Verlag.
[5] Ramesh Govindan, Joseph M. Hellerstein, Wei Hong, Samuel Madden, Michael Franklin, and Scott Shenker.The sensor network as a database. Technical report,2002.
[6] Samuel R. Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. Tinydb: an acquisitionalquery processing system for sensor networks. ACM Trans. Database Syst.,30(1):122–173, March2005.
[7] Wei Ye, J. Heidemann, and D. Estrin. An energy-efficient mac protocol for wireless sensor networks. InINFOCOM2002. Twenty-First Annual Joint Conference of the IEEE Computer and CommunicationsSocieties. Proceedings. IEEE, volume3, pages1567–1576vol.3,2002.
[8] Tijs van Dam and Koen Langendoen. An adaptive energy-efficient mac protocol for wireless sensornetworks. In Proceedings of the1st international conference on Embedded networked sensor systems,SenSys’03, pages171–180, New York, NY, USA,2003. ACM.
[9] Kyle Jamieson, Hari Balakrishnan, and Y.C. Tay. Sift: A MAC Protocol for Event-Driven Wireless SensorNetworks, volume3868of Lecture Notes in Computer Science, pages260–275. Springer Berlin Heidelberg,2006.
[10] V. Rajendran, J.J. Garcia-Luna-Aceves, and K. Obraczka. Energy-efficient channel access scheduling forpower-constrained networks. In Wireless Personal Multimedia Communications,2002. The5th InternationalSymposium on, volume2, pages509–513vol.2, oct.2002.
[11] Venkatesh Rajendran, Katia Obraczka, and J. J. Garcia-Luna-Aceves. Energy-efficient collision-freemedium access control for wireless sensor networks. In Proceedings of the1st international conference onEmbedded networked sensor systems, SenSys’03, pages181–192, New York, NY, USA,2003. ACM.
[12] Gang Lu, Bhaskar Krishnamachari, and Cauligi S. Raghavendra. An adaptive energy-efficient andlow-latency mac for tree-based data gathering in sensor networks: Research articles. Wirel. Commun. Mob.Comput.,7(7):863–875, September2007.
[13] Injong Rhee, Ajit Warrier, Mahesh Aia, and Jeongki Min. Z-mac: a hybrid mac for wireless sensor networks.In Proceedings of the3rd international conference on Embedded networked sensor systems, SenSys’05,pages90–101, New York, NY, USA,2005. ACM.
[14] K. Sohrabi, J. Gao, V. Ailawadhi, and G.J. Pottie. Protocols for self-organization of a wireless sensornetwork. Personal Communications, IEEE,7(5):16–27, oct2000.
[15] Swetha Narayanaswamy, Vikas Kawadia, R. S. Sreenivas, and P. R. Kumar. Power control in ad-hocnetworks: Theory, architecture, algorithm and implementation of the compow protocol. In EuropeanWireless Conference, pages156–162,2002.
[16] M. Kubisch, H. Karl, A. Wolisz, L.C. Zhong, and J. Rabaey. Distributed algorithms for transmission powercontrol in wireless sensor networks. In Wireless Communications and Networking,2003. WCNC2003.2003IEEE, volume1, pages558–563vol.1, march2003.
[17] Li Li, J.Y. Halpern, P. Bahl, Yi-Min Wang, and R. Wattenhofer. A cone-based distributed topology-controlalgorithm for wireless multi-hop networks. Networking, IEEE/ACM Transactions on,13(1):147–159, feb.2005.
[18] N. Li and J.C. Hou. Topology control in heterogeneous wireless networks: problems and solutions. InINFOCOM2004. Twenty-third AnnualJoint Conference of the IEEE Computer and CommunicationsSocieties, volume1, pages4vol.(xxxv+2866), march2004.
[19] Santosh Kumar, Ten H. Lai, and József Balogh. On k-coverage in a mostly sleeping sensor network. InProceedings of the10th annual international conference on Mobile computing and networking,MobiCom’04, pages144–158, New York, NY, USA,2004. ACM.
[20] Kui Wu, Yong Gao, Fulu Li, and Yang Xiao. Lightweight deployment-aware scheduling for wireless sensornetworks. Mob. Netw. Appl.,10(6):837–852, December2005.
[21] Alberto Cerpa and Deborah Estrin. Ascent: Adaptive self-configuring sensor networks topologies. IEEETransactions on Mobile Computing,3(3):272–285, July2004.
[22] Fan Ye, Gary Zhong, Jesse Cheng, Songwu Lu, and Lixia Zhang. Peas: A robust energy conserving protocolfor long-lived sensor networks. In Proceedings of the23rd International Conference on DistributedComputing Systems, ICDCS’03, pages28–, Washington, DC, USA,2003. IEEE Computer Society.
[23] Chao Gui and Prasant Mohapatra. Power conservation and quality of surveillance in target tracking sensornetworks. In Proceedings of the10th annual international conference on Mobile computing and networking,MobiCom’04, pages129–143, New York, NY, USA,2004. ACM.
[24] Saurabh Ganeriwal, Ram Kumar, and Mani B. Srivastava. Timing-sync protocol for sensor networks. InProceedings of the1st international conference on Embedded networked sensor systems, SenSys’03, pages138–149, New York, NY, USA,2003. ACM.
[25] M.L. Sichitiu and C. Veerarittiphan. Simple, accurate time synchronization for wireless sensor networks. InWireless Communications and Networking,2003. WCNC2003.2003IEEE, volume2, pages1266–1273vol.2, march2003.
[26] Hui Dai and Richard Han. Tsync: a lightweight bidirectional time synchronization service for wirelesssensor networks. SIGMOBILE Mob. Comput. Commun. Rev.,8(1):125–139, January2004.
[27] Jana van Greunen and Jan Rabaey. Lightweight time synchronization for sensor networks. In Proceedings ofthe2nd ACM international conference on Wireless sensor networks and applications, WSNA’03, pages11–19, New York, NY, USA,2003. ACM.
[28] Qun Li and Daniela Rus. Global clock synchronization in sensor networks. IEEE Transactions onComputers,55:214–226,2006.
[29] Kay R mer. Time synchronization in ad hoc networks. In Proceedings of the2nd ACM internationalsymposium on Mobile ad hoc networking&computing, MobiHoc’01, pages173–182, New York, NY,USA,2001. ACM.
[30] Dragos Niculescu and Badri Nath. Error characteristics of ad hoc positioning systems (aps). In Proceedingsof the5th ACM international symposium on Mobile ad hoc networking and computing, MobiHoc’04, pages20–30, New York, NY, USA,2004. ACM.
[31] Yi Shang, Wheeler Ruml, Ying Zhang, and Markus Fromherz. Localization from connectivity in sensornetworks. IEEE Trans. Parallel Distrib. Syst.,15(11):961–974, November2004.
[32] Suman Nath, Amol Deshpande, Yan Ke, Phillip B. Gibbons, Brad Karp, and Srinivasan Seshan. Irisnet: anarchitecture for internet-scale sensing services. In Proceedings of the29th international conference on Verylarge data bases-Volume29, VLDB’03, pages1137–1140. VLDB Endowment,2003.
[33] Bo Xu, Fatemeh Vafaee, and Ouri Wolfson. In-network query processing in mobile p2p databases. InProceedings of the17th ACM SIGSPATIAL International Conference on Advances in GeographicInformation Systems, GIS’09, pages207–216, New York, NY, USA,2009. ACM.
[34] Michele Albano and Stefano Chessa. Publish/subscribe in wireless sensor networks based on data centricstorage. In Proceedings of the1st International Workshop on Context-Aware Middleware and Services:affiliated with the4th International Conference on Communication System Software and Middleware(COMSWARE2009), CAMS’09, pages37–42, New York, NY, USA,2009. ACM.
[35] Sylvia Ratnasamy, Brad Karp, Li Yin, Fang Yu, Deborah Estrin, Ramesh Govindan, and Scott Shenker. Ght:a geographic hash table for data-centric storage. In Proceedings of the1st ACM international workshop onWireless sensor networks and applications, WSNA’02, pages78–87, New York, NY, USA,2002. ACM.
[36] Scott Shenker, Sylvia Ratnasamy, Brad Karp, Ramesh Govindan, and Deborah Estrin. Data-centric storagein sensornets. SIGCOMM Comput. Commun. Rev.,33(1):137–142, January2003.
[37] Mohamed Aly, Kirk Pruhs, and Panos K. Chrysanthis. Kddcs: a load-balanced in-network data-centricstorage scheme for sensor networks. In Proceedings of the15th ACM international conference onInformation and knowledge management, CIKM’06, pages317–326, New York, NY, USA,2006. ACM.
[38] Abhishek Ghose, Jens Grossklags, and John Chuang. Resilient data-centric storage in wireless ad-hoc sensornetworks. In Proceedings of the4th International Conference on Mobile Data Management, MDM’03,pages45–62, London, UK, UK,2003. Springer-Verlag.
[39] Wen-Hwa Liao, Kuei-PIng Shih, and Wan-Chi Wu. A grid-based dynamic load balancing approach fordata-centric storage in wireless sensor networks. Comput. Electr. Eng.,36(1):19–30, January2010.
[40] Deepak Ganesan, Deborah Estrin, and John Heidemann. Dimensions: why do we need a new data handlingarchitecture for sensor networks? SIGCOMM Comput. Commun. Rev.,33(1):143–148, January2003.
[41] B. Greenstein, D. Estrin, R. Govindan, S. Ratnasamy, and S. Shenker. Difs: a distributed index for featuresin sensor networks. In Sensor Network Protocols and Applications,2003. Proceedings of the First IEEE.2003IEEE International Workshop on, pages163–173, may2003.
[42] Xin Li, Young Jin Kim, Ramesh Govindan, and Wei Hong. Multi-dimensional range queries in sensornetworks. In Proceedings of the1st international conference on Embedded networked sensor systems,SenSys’03, pages63–75, New York, NY, USA,2003. ACM.
[43] Murat Demirbas and Hakan Ferhatosmanoglu. Peer-to-peer spatial queries in sensor networks. InProceedings of the3rd International Conference on Peer-to-Peer Computing, P2P’03, pages32–39,Washington, DC, USA,2003. IEEE Computer Society.
[44] T.M. Gil and S. Madden. Scoop: An adaptive indexing scheme for stored data in sensor networks. In DataEngineering,2007. ICDE2007. IEEE23rd International Conference on, pages1345–1349, april2007.
[45] Yong Yao and Johannes Gehrke. The cougar approach to in-network query processing in sensor networks.SIGMOD Rec.,31(3):9–18, September2002.
[46] C. Castelluccia, E. Mykletun, and G. Tsudik. Efficient aggregation of encrypted data in wireless sensornetworks. In Mobile and Ubiquitous Systems: Networking and Services,2005. MobiQuitous2005. TheSecond Annual International Conference on, pages109–117, july2005.
[47] Claude Castelluccia, Aldar C-F. Chan, Einar Mykletun, and Gene Tsudik. Efficient and provably secureaggregation of encrypted data in wireless sensor networks. ACM Trans. Sen. Netw.,5(3):20:1–20:36, June2009.
[48] M.M. Groat, Wenbo He, and S. Forrest. Kipda: k-indistinguishable privacy-preserving data aggregation inwireless sensor networks. In INFOCOM,2011Proceedings IEEE, pages2024–2032, april2011.
[49] Wenbo He, Xue Liu, Hoang Nguyen, K. Nahrstedt, and T.T. Abdelzaher. Pda: Privacy-preserving dataaggregation in wireless sensor networks. In INFOCOM2007.26th IEEE International Conference onComputer Communications. IEEE, pages2045–2053, may2007.
[50] Bo Sheng and Qun Li. Verifiable privacy-preserving sensor network storage for range query. IEEETransactions on Mobile Computing,10(9):1312–1326, September2011.
[51] Dirk Westhoff, Joao Girao, and Mithun Acharya. Concealed data aggregation for reverse multicast traffic insensor networks: Encryption, key distribution, and routing adaptation. IEEE Transactions on MobileComputing,5(10):1417–1431, October2006.
[52] Yonglei Yao, Naixue Xiong, Jong Hyuk Park, Li Ma, and Jingfa Liu. Privacy-preserving max/min query intwo-tiered wireless sensor networks. Computers& Mathematics with Applications,(0):–,2012.
[53] Bogdan Carbunar, Yang Yu, Weidong Shi, Michael Pearce, and Venu Vasudevan. Query privacy in wirelesssensor networks. ACM Trans. Sen. Netw.,6(2):14:1–14:34, March2010.
[54] Na Li, Nan Zhang, Sajal K. Das, and Bhavani Thuraisingham. Privacy preservation in wireless sensornetworks: A state-of-the-art survey. Ad Hoc Netw.,7(8):1501–1514, November2009.
[55] Yun Li and Jian Ren. Source-location privacy through dynamic routing in wireless sensor networks. InProceedings of the29th conference on Information communications, INFOCOM’10, pages2660–2668,Piscataway, NJ, USA,2010. IEEE Press.
[56] Celal Ozturk, Yanyong Zhang, and Wade Trappe. Source-location privacy in energy-constrained sensornetwork routing. In Proceedings of the2nd ACM workshop on Security of ad hoc and sensor networks,SASN’04, pages88–93, New York, NY, USA,2004. ACM.
[57] Pandurang Kamat, Wenyuan Xu, Wade Trappe, and Yanyong Zhang. Temporal privacy in wireless sensornetworks: Theory and practice. ACM Trans. Sen. Netw.,5(4):28:1–28:24, November2009.
[58] JieYuan Li and Guohua Liu. On the representation and querying of sets of possible worlds in thek-anonymity privacy protecting model. In Proceedings of the2011Seventh International Conference onComputational Intelligence and Security, CIS’11, pages662–664, Washington, DC, USA,2011. IEEEComputer Society.
[59] Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. Tag: a tiny aggregation servicefor ad-hoc sensor networks. SIGOPS Oper. Syst. Rev.,36(SI):131–146, December2002.
[60] M. Lee and V.W.S. Wong. An energy-aware spanning tree algorithm for data aggregation in wireless sensornetworks. In Communications, Computers and signal Processing,2005. PACRIM.2005IEEE Pacific RimConference on, pages300–303, aug.2005.
[61] Yingqi Xu, Wang-Chien Lee, Jianliang Xu, and G. Mitchell. Processing window queries in wireless sensornetworks. In Data Engineering,2006. ICDE’06. Proceedings of the22nd International Conference on,pages70–80, april2006.
[62] A. Sharaf, Jonathan Beaver, Alexandros Labrinidis, and K. Chrysanthis. Balancing energy efficiency andquality of aggregate data in sensor networks. The VLDB Journal,13(4):384–403, December2004.
[63] Suman Nath, Phillip B. Gibbons, Srinivasan Seshan, and Zachary R. Anderson. Synopsis diffusion forrobust aggregation in sensor networks. In Proceedings of the2nd international conference on Embeddednetworked sensor systems, SenSys’04, pages250–262, New York, NY, USA,2004. ACM.
[64] Amit Manjhi, Suman Nath, and Phillip B. Gibbons. Tributaries and deltas: efficient and robust aggregationin sensor network streams. In Proceedings of the2005ACM SIGMOD international conference onManagement of data, SIGMOD’05, pages287–298, New York, NY, USA,2005. ACM.
[65] Kai-Wei Fan and P. Sinha. Distributed online data aggregation for large scale sensor networks. In Mobile AdHoc and Sensor Systems,2008. MASS2008.5th IEEE International Conference on, pages153–162,292008-oct.22008.
[66] Jie Gao, Leonidas Guibas, Nikola Milosavljevic, and John Hershberger. Sparse data aggregation in sensornetworks. In Proceedings of the6th international conference on Information processing in sensor networks,IPSN’07, pages430–439, New York, NY, USA,2007. ACM.
[67]程思瑶，李建中.无线传感器网络中(ε,δ)-近似聚集算法.软件学报,(08):1936–1953,2010.
[68] Xueyan Tang and Jianliang Xu. Adaptive data collection strategies for lifetime-constrained wireless sensornetworks. IEEE Trans. Parallel Distrib. Syst.,19(6):721–734, June2008.
[69] Xueyan Tang and Jianliang Xu. Optimizing lifetime for continuous data aggregation with precisionguarantees in wireless sensor networks. IEEE/ACM Trans. Netw.,16(4):904–917, August2008.
[70] Jeffrey Considine, Marios Hadjieleftheriou, Feifei Li, John Byers, and George Kollios. Robust approximateaggregation in sensor data management systems. ACM Trans. Database Syst.,34(1):6:1–6:35, April2009.
[71] Adam Silberstein, Kamesh Munagala, and Jun Yang. Energy-efficient monitoring of extreme values insensor networks. In Proceedings of the2006ACM SIGMOD international conference on Management ofdata, SIGMOD’06, pages169–180, New York, NY, USA,2006. ACM.
[72] Amol Deshpande, Carlos Guestrin, Samuel R. Madden, Joseph M. Hellerstein, and Wei Hong. Model-drivendata acquisition in sensor networks. In Proceedings of the Thirtieth international conference on Very largedata bases-Volume30, VLDB’04, pages588–599. VLDB Endowment,2004.
[73] Carlos Guestrin, Peter Bodik, Romain Thibaux, Mark Paskin, and Samuel Madden. Distributed regression:an efficient framework for modeling sensor network data. In Proceedings of the3rd internationalsymposium on Information processing in sensor networks, IPSN’04, pages1–10, New York, NY, USA,2004. ACM.
[74] Yao-Chung Fan and Arbee L.P. Chen. Efficient and robust schemes for sensor data aggregation based onlinear counting. IEEE Transactions on Parallel and Distributed Systems,21:1675–1691,2010.
[75] Boris Jan Bonfils and Philippe Bonnet. Adaptive and decentralized operator placement for in-network queryprocessing. In Proceedings of the2nd international conference on Information processing in sensornetworks, IPSN’03, pages47–62, Berlin, Heidelberg,2003. Springer-Verlag.
[76] J. Gehrke and S. Madden. Query processing in sensor networks. Pervasive Computing, IEEE,3(1):46–55,jan.-march2004.
[77] Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. The design of an acquisitionalquery processor for sensor networks. In Proceedings of the2003ACM SIGMOD international conferenceon Management of data, SIGMOD’03, pages491–502, New York, NY, USA,2003. ACM.
[78] Daniel J. Abadi, Samuel Madden, and Wolfgang Lindner. Reed: robust, efficient filtering and eventdetection in sensor networks. In Proceedings of the31st international conference on Very large data bases,VLDB’05, pages769–780. VLDB Endowment,2005.
[79] Himanshu Gupta and Vishal Chowdhary. Communication-efficient implementation of join in sensornetworks. Ad Hoc Netw.,5(6):929–942, August2007.
[80] Aditi Pandit and Himanshu Gupta. Communication-efficient implementation of range-joins in sensornetworks. In Proceedings of the11th international conference on Database Systems for AdvancedApplications, DASFAA’06, pages859–869, Berlin, Heidelberg,2006. Springer-Verlag.
[81]刘亮,秦小麟,戴华,杨丰.基于环扇区的无线传感器网络k近邻查询处理算法.小型微型计算机系统,(08):1487–1492,2011.
[82] Yuxia Yao, Xueyan Tang, and Ee-Peng Lim. Localized monitoring of knn queries in wireless sensornetworks. The VLDB Journal,18(1):99–117, January2009.
[83]郭龙江,李建中,李贵林.无线传感器网络环境下时-空查询处理方法.软件学报,(04):794–805,2006.
[84] Alexandru Coman, Mario A. Nascimento, and J rg Sander. A framework for spatio-temporal queryprocessing over wireless sensor networks. In Proceeedings of the1st international workshop on Datamanagement for sensor networks: in conjunction with VLDB2004, DMSN’04, pages104–110, New York,NY, USA,2004. IEEE.
[85] A. Coman, J. Sander, and M.A. Nascimento. An analysis of spatio-temporal query processing in sensornetworks. In21st International Conference on Data Engineering Workshops, page1190, april2005.
[86] Alexandru Coman, Mario A. Nascimento, and J rg Sander. Exploiting redundancy in sensor networks forenergy efficient processing of spatiotemporal region queries. In Proceedings of the14th ACM internationalconference on Information and knowledge management, CIKM’05, pages187–194, New York, NY, USA,2005. ACM.
[87] Dina Goldin, Mingjun Song, Ayferi Kutlu, Huayan Gao, and Hardik Dave. Georouting and delta-gathering:Efficient data propagation techniques for geosensor networks. In First Workshop on Geo Sensor Networks,pages9–11,2003.
[88] Julian Winter and Wang-Chien Lee. Kpt: a dynamic knn query processing algorithm for location-awaresensor networks. In Proceeedings of the1st international workshop on Data management for sensornetworks: in conjunction with VLDB2004, DMSN’04, pages119–124, New York, NY, USA,2004. ACM.
[89] Yingqi Xu, Tao-Yang Fu, Wang-Chien Lee, and Julian Winter. Processing k nearest neighbor queries inlocation-aware sensor networks. Signal Process.,87(12):2861–2881, December2007.
[90] Tao-Young Fu, Wen-Chih Peng, and Wang-Chien Lee. Optimizing parallel itineraries for knn queryprocessing in wireless sensor networks. In Proceedings of the sixteenth ACM conference on Conference oninformation and knowledge management, CIKM’07, pages391–400, New York, NY, USA,2007. ACM.
[91] Shan-Hung Wu, Kun-Ta Chuang, Chung-Min Chen, and Ming-Syan Chen. Diknn: An itinerary-based knnquery processing algorithm for mobile sensor networks. In Proceedings of the2007IEEE23rd InternationalConference on Data Engineering, volume0, pages456–465, Los Alamitos, CA, USA,2007. IEEEComputer Society.
[92] Shan-Hung Wu, Kun-Ta Chuang, Chung-Min Chen, and Ming-Syan Chen. Toward the optimalitinerary-based knn query processing in mobile sensor networks. IEEE Trans. on Knowl. and Data Eng.,20(12):1655–1668, December2008.
[93] Hakan Hacigümüs, Bala Iyer, Chen Li, and Sharad Mehrotra. Executing sql over encrypted data in thedatabase-service-provider model. In Proceedings of the2002ACM SIGMOD international conference onManagement of data, SIGMOD’02, pages216–227, New York, NY, USA,2002. ACM.
[94] Jing Shi, Rui Zhang, and Yanchao Zhang. Secure range queries in tiered sensor networks. In INFOCOM2009, IEEE, pages945–953, april2009.
[95] Jing Shi, Rui Zhang, and Yanchao Zhang. A spatiotemporal approach for secure range queries in tieredsensor networks. Wireless Communications, IEEE Transactions on,10(1):264–273, january2011.
[96] Fei Chen and A.X. Liu. Safeq: Secure and efficient query processing in sensor networks. In INFOCOM,2010Proceedings IEEE, pages1–9, march2010.
[97] C. Castelluccia, E. Mykletun, and G. Tsudik. Efficient aggregation of encrypted data in wireless sensornetworks. In Mobile and Ubiquitous Systems: Networking and Services,2005. MobiQuitous2005. TheSecond Annual International Conference on, pages109–117, july2005.
[98] Aldar C-F. Chan and Claude Castelluccia. A security framework for privacy-preserving data aggregation inwireless sensor networks. ACM Trans. Sen. Netw.,7(4):29:1–29:45, February2011.
[99] Taiming Feng, Chuang Wang, Wensheng Zhang, and Lu Ruan. Confidentiality protection for distributedsensor data aggregation. In INFOCOM2008. The27th Conference on Computer Communications. IEEE,pages56–60, april2008.
[100] Hongjuan Li, Kai Lin, and Keqiu Li. Energy-efficient and high-accuracy secure data aggregation inwireless sensor networks. Computer Communications,34(4):591–597,2011.
[101] Chien-Liang Fok, Gruia-Catalin Roman, and Chenyang Lu. Agilla: A mobile agent middleware forself-adaptive wireless sensor networks. ACM Trans. Auton. Adapt. Syst.,4(3):16:1–16:26, July2009.
[102] Xue Wang, Aiguo Jiang, and Sheng Wang. Mobile agent based wireless sensor network for intelligentmaintenance. In De-Shuang Huang, Xiao-Ping Zhang, and Guang-Bin Huang, editors, Advances inIntelligent Computing, volume3645of Lecture Notes in Computer Science, pages316–325. Springer Berlin/Heidelberg,2005.10.1007/11538356_33.
[103] Alexandru Coman, Joerg Sander, and Mario A. Nascimento. Adaptive processing of historical spatial rangequeries in peer-to-peer sensor networks. Distrib. Parallel Databases,22(2-3):133–163, December2007.
[104] David Chu, Amol Deshpande, Joseph M. Hellerstein, and Wei Hong. Approximate data collection in sensornetworks using probabilistic models. In Proceedings of the22nd International Conference on DataEngineering, ICDE’06, pages48–, Washington, DC, USA,2006. IEEE Computer Society.
[105] Antonios Deligiannakis, Yannis Kotidis, and Nick Roussopoulos. Compressing historical information insensor networks. In Proceedings of the2004ACM SIGMOD international conference on Management ofdata, SIGMOD’04, pages527–538, New York, NY, USA,2004. ACM.
[106] Sorabh Gandhi, Suman Nath, Subhash Suri, and Jie Liu. Gamps: compressing multi sensor data bygrouping and amplitude scaling. In Proceedings of the2009ACM SIGMOD International Conference onManagement of data, SIGMOD’09, pages771–784, New York, NY, USA,2009. ACM.
[107] Pratik Biswas, Tzu-Chen Lian, Ta-Chung Wang, and Yinyu Ye. Semidefinite programming basedalgorithms for sensor network localization. ACM Trans. Sen. Netw.,2(2):188–220, May2006.
[108] Amin Karbasi and Sewoong Oh. Distributed sensor network localization from local connectivity:performance analysis for the hop-terrain algorithm. In Proceedings of the ACM SIGMETRICS internationalconference on Measurement and modeling of computer systems, SIGMETRICS’10, pages61–70, NewYork, NY, USA,2010. ACM.
[109] Mo Li and Yunhao Liu. Rendered path: range-free localization in anisotropic sensor networks with holes. InProceedings of the13th annual ACM international conference on Mobile computing and networking,MobiCom’07, pages51–62, New York, NY, USA,2007. ACM.
[110] Guoqiang Mao, Bars Fidan, and Brian D. O. Anderson. Wireless sensor network localization techniques.omput. Netw.,51(10):2529–2553, July2007.
[111] Brad Karp and H. T. Kung. Gpsr: greedy perimeter stateless routing for wireless networks. In Proceedingsof the6th annual international conference on Mobile computing and networking, MobiCom’00, pages243–254, New York, NY, USA,2000. ACM.
[112] Amol Deshpande, Carlos Guestrin, Wei Hong, and Samuel Madden. Exploiting correlated attributes inacquisitional query processing. In Proceedings of the21st International Conference on Data Engineering,ICDE’05, pages143–154, Washington, DC, USA,2005. IEEE Computer Society.
[113] Yannis Kotidis. Snapshot queries: Towards data-centric sensor networks. In Proceedings of the21stInternational Conference on Data Engineering, ICDE’05, pages131–142, Washington, DC, USA,2005.IEEE Computer Society.
[114] Siyao Cheng and Jianzhong Li. Sampling based (epsilon, delta)-approximate aggregation algorithm insensor networks. In Proceedings of the200929th IEEE International Conference on Distributed ComputingSystems, ICDCS’09, pages273–280, Washington, DC, USA,2009. IEEE Computer Society.
[115] Siyao Cheng, Jianzhong Li, Qianqian Ren, and Lei Yu. Bernoulli sampling based (ε,δ)-approximate aggregation in large-scale sensor networks. In Proceedings of the29th conference onInformation communications, INFOCOM’10, pages1181–1189, Piscataway, NJ, USA,2010. IEEE Press.
[116] Liu Yu, Jianzhong Li, Hong Gao, and Xiaolin Fang. Enabling?-approximate querying in sensor networks.Proc. VLDB Endow.,2(1):169–180, August2009.
[117]孙利民,李建中,陈渝.无线传感器网络.清华大学出版社,2005.
[118]刘亮,秦小麟,戴华,严伟中,潘锦基.能量高效的无线传感器网络时空查询处理算法.电子学报,38(01):54–59,2010.
[119] Antonin Guttman. R-trees: a dynamic index structure for spatial searching. SIGMOD Rec.,14(2):47–57,June1984.
[120] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: a survey.Computer Networks,38(4):393–422,2002.
[121]清华大学出版社.计算几何———算法分析与设计.清华大学出版社,2005.
[122] Theodore S. Rappaport. Wireless Communications: Principles and Practice. IEEE Press, Piscataway, NJ,USA,1st edition,1996.
[123] Gregory Piatetsky-Shapiro and Charles Connell. Accurate estimation of the number of tuples satisfying acondition. SIGMOD Rec.,14(2):256–276, June1984.
[124] Viswanath Poosala, Peter J. Haas, Yannis E. Ioannidis, and Eugene J. Shekita. Improved histograms forselectivity estimation of range predicates. SIGMOD Rec.,25(2):294–305, June1996.
[125] Michael Greenwald and Sanjeev Khanna. Space-efficient online computation of quantile summaries.SIGMOD Rec.,30(2):58–66, May2001.
[126] Yannis E. Ioannidis and Viswanath Poosala. Balancing histogram optimality and practicality for queryresult size estimation. SIGMOD Rec.,24(2):233–244, May1995.
[127] H. V. Jagadish, Nick Koudas, S. Muthukrishnan, Viswanath Poosala, Kenneth C. Sevcik, and Torsten Suel.Optimal histograms with quality guarantees. In Proceedings of the24rd International Conference on VeryLarge Data Bases, VLDB’98, pages275–286, San Francisco, CA, USA,1998. Morgan KaufmannPublishers Inc.
[128] Anna C. Gilbert, Yannis Kotidis, S. Muthukrishnan, and Martin Strauss. Surfing wavelets on streams:One-pass summaries for approximate aggregate queries. In Proceedings of the27th International Conferenceon Very Large Data Bases, VLDB’01, pages79–88, San Francisco, CA, USA,2001. Morgan KaufmannPublishers Inc.
[129] Jeffrey S. Vitter. Random sampling with a reservoir. ACM Trans. Math. Softw.,11(1):37–57, March1985.
[130] Phillip B. Gibbons and Yossi Matias. New sampling-based summary statistics for improving approximatequery answers. SIGMOD Rec.,27(2):331–342, June1998.
[131] Brian Babcock, Mayur Datar, and Rajeev Motwani. Sampling from a moving window over streaming data.In Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms, SODA’02, pages633–634, Philadelphia, PA, USA,2002. Society for Industrial and Applied Mathematics.
[132] Alin Dobra, Minos Garofalakis, Johannes Gehrke, and Rajeev Rastogi. Processing complex aggregatequeries over data streams. In Proceedings of the2002ACM SIGMOD international conference onManagement of data, SIGMOD’02, pages61–72, New York, NY, USA,2002. ACM.
[133] Johannes Gehrke, Flip Korn, and Divesh Srivastava. On computing correlated aggregates over continualdata streams. SIGMOD Rec.,30(2):13–24, May2001.
[134] Alin Dobra, Minos Garofalakis, Johannes Gehrke, and Rajeev Rastogi. Processing complex aggregatequeries over data streams. In Proceedings of the2002ACM SIGMOD international conference onManagement of data, SIGMOD’02, pages61–72, New York, NY, USA,2002. ACM.
[135] Johannes Gehrke, Flip Korn, and Divesh Srivastava. On computing correlated aggregates over continualdata streams. SIGMOD Rec.,30(2):13–24, May2001.
[136] Noga Alon, Phillip B. Gibbons, Yossi Matias, and Mario Szegedy. Tracking join and self-join sizes inlimited storage. In Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium onPrinciples of database systems, PODS’99, pages10–20, New York, NY, USA,1999. ACM.
[137] Noga Alon, Yossi Matias, and Mario Szegedy. The space complexity of approximating the frequencymoments. In Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, STOC’96,pages20–29, New York, NY, USA,1996. ACM.
[138] Graham Cormode and Marios Hadjieleftheriou. Methods for finding frequent items in data streams. TheVLDB Journal,19(1):3–20, February2010.
[139] Michael Greenwald and Sanjeev Khanna. Space-efficient online computation of quantile summaries.SIGMOD Rec.,30(2):58–66, May2001.
[140]韩近强,杨冬青,唐世渭.数据流处理中一种自适应的直方图维护算法. In第二十届全国数据库学术会议, pages178–181,中国湖南长沙,2003.
[141] Baichen Chen, Weifa Liang, and Jeffrey Xu Yu. Online time interval top-k queries in wireless sensornetworks. In Proceedings of the2010Eleventh International Conference on Mobile Data Management,MDM’10, pages177–182, Washington, DC, USA,2010. IEEE Computer Society.
[142] Baichen Chen, Weifa Liang, Rui Zhou, and Jeffrey Xu Yu. Energy-efficient top-k query processing inwireless sensor networks. In Proceedings of the19th ACM international conference on Information andknowledge management, CIKM’10, pages329–338, New York, NY, USA,2010. ACM.
[143] Xingjie Liu, Jianliang Xu, and Wang-Chien Lee. A cross pruning framework for top-k data collection inwireless sensor networks. In Proceedings of the2010Eleventh International Conference on Mobile DataManagement, MDM’10, pages157–166, Washington, DC, USA,2010. IEEE Computer Society.
[144] Hai Thanh Mai and Myoung Ho Kim. Processing continuous top-k data collection queries inlifetime-constrained wireless sensor networks. In Proceedings of the5th International Conference onUbiquitous Information Management and Communication, ICUIMC’11, pages14:1–14:10, New York, NY,USA,2011. ACM.
[145]Minji Wu, Jianliang Xu, Xueyan Tang, and Wang-Chien Lee. Top-k monitoring in wireless sensor networks.IEEE Trans. on Knowl. and Data Eng.,19(7):962–976, July2007.
[146] Baichen Chen, Weifa Liang, and Jeffrey Xu Yu. Progressive skyline query evaluation and maintenance inwireless sensor networks. In Proceedings of the18th ACM conference on Information and knowledgemanagement, CIKM’09, pages1445–1448, New York, NY, USA,2009. ACM.
[147] Weifa Liang, Baichen Chen, and Jeffrey Xu Yu. Energy-efficient skyline query processing and maintenancein sensor networks. In Proceedings of the17th ACM conference on Information and knowledgemanagement, CIKM’08, pages1471–1472, New York, NY, USA,2008. ACM.
[148] I-Fang Su, Yu-Chi Chung, Chiang Lee, and Yi-Ying Lin. Efficient skyline query processing in wirelesssensor networks. J. Parallel Distrib. Comput.,70(6):680–698, June2010.
[149] Adam Silberstein, Rebecca Braynard, and Jun Yang. Constraint chaining: on energy-efficient continuousmonitoring in sensor networks. In Proceedings of the2006ACM SIGMOD international conference onManagement of data, SIGMOD’06, pages157–168, New York, NY, USA,2006. ACM.
[150] Daniel J. Abadi, Samuel Madden, and Wolfgang Lindner. Reed: robust, efficient filtering and eventdetection in sensor networks. In Proceedings of the31st international conference on Very large data bases,VLDB’05, pages769–780. VLDB Endowment,2005.
[151] Alexandru Coman, Mario A. Nascimento, and Jorg Sander. On join location in sensor networks. InProceedings of the2007International Conference on Mobile Data Management, MDM’07, pages190–197,Washington, DC, USA,2007. IEEE Computer Society.
[152] Moustafa A. Hammad, Walid G. Aref, and Ahmed K. Elmagarmid. Query processing of multi-way streamwindow joins. The VLDB Journal,17(3):469–488, May2008.
[153] Svilen R. Mihaylov, Marie Jacob, Zachary G. Ives, and Sudipto Guha. Dynamic join optimization inmulti-hop wireless sensor networks. Proc. VLDB Endow.,3(1-2):1279–1290, September2010.
[154] Mirco Stern, Klemens B hm, and Erik Buchmann. Processing continuous join queries in sensor networks: afiltering approach. In Proceedings of the2010ACM SIGMOD International Conference on Management ofdata, SIGMOD’10, pages267–278, New York, NY, USA,2010. ACM.
[155] Mirco Stern, Erik Buchmann, and Klemens B hm. Towards efficient processing of general-purpose joins insensor networks. In Proceedings of the2009IEEE International Conference on Data Engineering, ICDE’09,pages126–137, Washington, DC, USA,2009. IEEE Computer Society.
[156] D. Zeinalipour-Yazti, Z. Vagena, D. Gunopulos, V. Kalogeraki, V. Tsotras, M. Vlachos, N. Koudas, andD. Srivastava. The threshold join algorithm for top-k queries in distributed sensor networks. In Proceedingsof the2nd international workshop on Data management for sensor networks, DMSN’05, pages61–66, NewYork, NY, USA,2005. ACM.
[157] Gsli R. Hjaltason and Hanan Samet. Distance browsing in spatial databases. áCM Trans. Database Syst.,24(2):265–318, June1999.
[158] Nick Roussopoulos, Stephen Kelley, and Frédéric Vincent. Nearest neighbor queries. SIGMOD Rec.,24(2):71–79, May1995.
[159] Yuxia Yao, Xueyan Tang, and Ee-Peng Lim. Localized monitoring of knn queries in wireless sensornetworks. The VLDB Journal,18(1):99–117, January2009.