无线传感器网络节点数据管理与能耗研究
|
摘要
无线传感器网络是涉及多学科知识的新型网络,综合了传感器技术、网络技术与无线通信技术等,应用前景十分广泛,是21世纪前沿新技术之一。
无线传感器网络节点能量有限,能量高效的数据管理和延长网络寿命是该领域很多学者十分关注的问题。传感器节点是一个独立的计算和控制单元,能够实现自身的数据管理,即数据感知、分析、转发以及自身状态的控制,或者是与其它节点协同完成数据管理。节点数据管理是无线传感器网络数据管理的组成部分,它与网络拓扑结构、节点自身特性以及节点感知数据密切相关。如何有效地管理节点数据对改善网络能量效率和延长网络寿命具有重要意义,目前国内外尚缺乏无线传感器网络节点数据管理技术的具体研究。
通过对节点数据的有效管理,为查询者提供可靠数据,减少通信中不必要的广播能耗和数据转发能耗,从而提高整个网络能量利用率和延长网络寿命。本文围绕无线传感器网络节点数据管理和能耗处理技术,着重从节点数据管理特点、网络拓扑控制、节点分类管理和节点感知数据预测四个方面进行了比较系统的研究,具体研究内容如下:
①结合无线传感器网络及其节点的特性,讨论了无线传感器网络数据管理与节点数据管理的关系。分析得到良好的拓扑管理,合理的节点感知数据处理和节点调度控制对节点数据管理与降低网络能耗有着重要作用。最后讨论了节点数据管理与能耗处理技术的主要研究内容。
②分析了现有分簇算法的优缺点,提出了基于参数优化的分簇算法。以降低簇间通信能耗为目标,给出了簇间最优单跳距离、分簇角与节点物理参数和节点数目的关系;以减少簇头更换频率和降低簇内广播能耗为目标,提出了簇头连续担任本地控制中心直至其工作次数到达最优值才被候选簇头替换的簇内数据收集机制。仿真结果表明所提分簇算法能够有效降低簇内、簇间通信能耗,并能显著延长网络寿命。
③利用节点的计算和分析功能,提出了基于感知数据综合支持度的节点分类算法。簇头利用误差函数和模糊函数分析成员感知数据的关联性,获取节点感知数据综合支持度,由此将成员节点划分为冲突节点、补充节点和可靠节点。针对休眠的节点,通过分析节点感知数据综合支持度及其增量,给出了相应的休眠控制规则。针对具有高综合支持度的冗余节点,给出了相应的调度规则以降低簇头能耗和尽可能实现簇间节点能耗均衡。仿真结果表明算法能够实现簇内节点分类,降低簇内数据收发量并能有效延长网络寿命。
④以减少簇内数据收发量为目标,提出了面向数据收集的节点数据预测算法。簇头采用GM(1, 1)预测模型和动态更新参数阵列的机制实现对部分成员感知数据的预测。针对簇头不同的预测模式,给出了被预测节点的两种调度机制即顺序调度和选择性调度以及簇头数据融合的处理方法。实验和仿真结果表明采用预测算法能够准确预测节点感知数据,并能有效改善网络能耗和延长寿命。
论文最后对节点数据管理及能耗研究所提出的算法及相关的工作进行了总结,并对有待于进一步研究的课题和方向提出自己的思考。
Wireless Sensor Networks (WSN) is a new type network made up of sensor, network and wireless communication technologies and has a wide application future, and it is one of the new and high technologies in the 21st century.
The nodes of WSN are extremely power constrained, so energy-efficient data management and prolonging the networks lifetime are the major concerns to many scholars in this researching area. Each node of WSN is an independent computing and controlling unit which can achieve their own data management such as sensing data, analyzing data, transmitting data and controlling their own state, or collaborate with other nodes for the data management. Node data management is a part of WSN data management, and it is closely related to network topology, node characteristics and node sensing data. How to effectively manage nodes’data is very important to improve the networks energy efficiency and extend the networks lifetime, and there is little research result for WSN node data management.
Through energy-efficient node data management, the reliable data can be provided to the users, and the unnecessary energy consumption for broadcasting message and transmitting data can be reduced, and then the entire network energy efficiency can be improved significantly and the network lifetime can be extended. In this paper, the technologies of node data management and energy consumption are studied mainly from the characteristics of the node data management, network topology control and node classification management and sensing data prediction of nodes. The specific studies are as follows.
①According to the characters of WSN and its nodes, the relationship between the WSN data management of and the node data management are discussed. The good topology management, correct processing of the node data and the rules of scheduling node are very important to the node data management and energy consumption optimization. The main studies of node data management and energy consumption optimization are discussed.
②By analyzing the advantages and disadvantages of the existent clustering algorithms, a new clustering algorithm based on optimum parameters is presented. The relationships of the optimum one-hop distance and clustering angle with the nodes electronic parameters and the number of the total nodes are given for minimizing the energy consumption between inter-cluster communications. Furthermore, the continuous working mechanism of each cluster head which acts as the local control center and will not be replaced by the candidate cluster head until its continuous working times reach the optimum values is given. The simulation results demonstrate that the presented clustering algorithm can effectively reduce the energy consumption used for intra-cluster broadcasting message and gathering data, and prolong the network lifetime.
③With calculation and analysis function of node, a node classification algorithm based on the integrative supportability of sensing data is presented. Each cluster head analyzes its member data correlation using error function and fuzzy function, and gains the integrative supportabilities of its members’sensing data. Based on the integrative supportabilities, the members of the cluster are classified as conflict nodes, complementary nodes and reliable nodes. The sleeping rules are given according to the node’s integrative supportability and its increment, and the controlling rules for the redundant nodes with high integrative supportabilities are given to reduce the energy consumption of the cluster and balance the energy consumption among members. The simulation results demonstrate that the presented algorithm can realize the node classification, reduce the amount of the data transmission and prolong the network lifetime.
④In order to reduce the amount of data transmission, a node data prediction algorithm for the data collection is presented. The cluster head predicts some members sensing data with the basic GM (1, 1) prediction model and the mechanism of dynamically updating array parameters. According to the different predicted modes of the cluster head, the two predicted scheduling mechanisms contained ordinal scheduling and selective scheduling are presented and the data fusion algorithm is given. The test and simulation results demonstrate that the presented algorithm can accurately predict the node sensing data, improve the energy efficiency and prolong the network lifetime.
The last chapter concludes the presented algorithms and the related work for the study of the node data management and energy consumption, and outlines the further research contents and directions.
无线传感器网络节点能量有限,能量高效的数据管理和延长网络寿命是该领域很多学者十分关注的问题。传感器节点是一个独立的计算和控制单元,能够实现自身的数据管理,即数据感知、分析、转发以及自身状态的控制,或者是与其它节点协同完成数据管理。节点数据管理是无线传感器网络数据管理的组成部分,它与网络拓扑结构、节点自身特性以及节点感知数据密切相关。如何有效地管理节点数据对改善网络能量效率和延长网络寿命具有重要意义,目前国内外尚缺乏无线传感器网络节点数据管理技术的具体研究。
通过对节点数据的有效管理,为查询者提供可靠数据,减少通信中不必要的广播能耗和数据转发能耗,从而提高整个网络能量利用率和延长网络寿命。本文围绕无线传感器网络节点数据管理和能耗处理技术,着重从节点数据管理特点、网络拓扑控制、节点分类管理和节点感知数据预测四个方面进行了比较系统的研究,具体研究内容如下:
①结合无线传感器网络及其节点的特性,讨论了无线传感器网络数据管理与节点数据管理的关系。分析得到良好的拓扑管理,合理的节点感知数据处理和节点调度控制对节点数据管理与降低网络能耗有着重要作用。最后讨论了节点数据管理与能耗处理技术的主要研究内容。
②分析了现有分簇算法的优缺点,提出了基于参数优化的分簇算法。以降低簇间通信能耗为目标,给出了簇间最优单跳距离、分簇角与节点物理参数和节点数目的关系;以减少簇头更换频率和降低簇内广播能耗为目标,提出了簇头连续担任本地控制中心直至其工作次数到达最优值才被候选簇头替换的簇内数据收集机制。仿真结果表明所提分簇算法能够有效降低簇内、簇间通信能耗,并能显著延长网络寿命。
③利用节点的计算和分析功能,提出了基于感知数据综合支持度的节点分类算法。簇头利用误差函数和模糊函数分析成员感知数据的关联性,获取节点感知数据综合支持度,由此将成员节点划分为冲突节点、补充节点和可靠节点。针对休眠的节点,通过分析节点感知数据综合支持度及其增量,给出了相应的休眠控制规则。针对具有高综合支持度的冗余节点,给出了相应的调度规则以降低簇头能耗和尽可能实现簇间节点能耗均衡。仿真结果表明算法能够实现簇内节点分类,降低簇内数据收发量并能有效延长网络寿命。
④以减少簇内数据收发量为目标,提出了面向数据收集的节点数据预测算法。簇头采用GM(1, 1)预测模型和动态更新参数阵列的机制实现对部分成员感知数据的预测。针对簇头不同的预测模式,给出了被预测节点的两种调度机制即顺序调度和选择性调度以及簇头数据融合的处理方法。实验和仿真结果表明采用预测算法能够准确预测节点感知数据,并能有效改善网络能耗和延长寿命。
论文最后对节点数据管理及能耗研究所提出的算法及相关的工作进行了总结,并对有待于进一步研究的课题和方向提出自己的思考。
Wireless Sensor Networks (WSN) is a new type network made up of sensor, network and wireless communication technologies and has a wide application future, and it is one of the new and high technologies in the 21st century.
The nodes of WSN are extremely power constrained, so energy-efficient data management and prolonging the networks lifetime are the major concerns to many scholars in this researching area. Each node of WSN is an independent computing and controlling unit which can achieve their own data management such as sensing data, analyzing data, transmitting data and controlling their own state, or collaborate with other nodes for the data management. Node data management is a part of WSN data management, and it is closely related to network topology, node characteristics and node sensing data. How to effectively manage nodes’data is very important to improve the networks energy efficiency and extend the networks lifetime, and there is little research result for WSN node data management.
Through energy-efficient node data management, the reliable data can be provided to the users, and the unnecessary energy consumption for broadcasting message and transmitting data can be reduced, and then the entire network energy efficiency can be improved significantly and the network lifetime can be extended. In this paper, the technologies of node data management and energy consumption are studied mainly from the characteristics of the node data management, network topology control and node classification management and sensing data prediction of nodes. The specific studies are as follows.
①According to the characters of WSN and its nodes, the relationship between the WSN data management of and the node data management are discussed. The good topology management, correct processing of the node data and the rules of scheduling node are very important to the node data management and energy consumption optimization. The main studies of node data management and energy consumption optimization are discussed.
②By analyzing the advantages and disadvantages of the existent clustering algorithms, a new clustering algorithm based on optimum parameters is presented. The relationships of the optimum one-hop distance and clustering angle with the nodes electronic parameters and the number of the total nodes are given for minimizing the energy consumption between inter-cluster communications. Furthermore, the continuous working mechanism of each cluster head which acts as the local control center and will not be replaced by the candidate cluster head until its continuous working times reach the optimum values is given. The simulation results demonstrate that the presented clustering algorithm can effectively reduce the energy consumption used for intra-cluster broadcasting message and gathering data, and prolong the network lifetime.
③With calculation and analysis function of node, a node classification algorithm based on the integrative supportability of sensing data is presented. Each cluster head analyzes its member data correlation using error function and fuzzy function, and gains the integrative supportabilities of its members’sensing data. Based on the integrative supportabilities, the members of the cluster are classified as conflict nodes, complementary nodes and reliable nodes. The sleeping rules are given according to the node’s integrative supportability and its increment, and the controlling rules for the redundant nodes with high integrative supportabilities are given to reduce the energy consumption of the cluster and balance the energy consumption among members. The simulation results demonstrate that the presented algorithm can realize the node classification, reduce the amount of the data transmission and prolong the network lifetime.
④In order to reduce the amount of data transmission, a node data prediction algorithm for the data collection is presented. The cluster head predicts some members sensing data with the basic GM (1, 1) prediction model and the mechanism of dynamically updating array parameters. According to the different predicted modes of the cluster head, the two predicted scheduling mechanisms contained ordinal scheduling and selective scheduling are presented and the data fusion algorithm is given. The test and simulation results demonstrate that the presented algorithm can accurately predict the node sensing data, improve the energy efficiency and prolong the network lifetime.
The last chapter concludes the presented algorithms and the related work for the study of the node data management and energy consumption, and outlines the further research contents and directions.
引文
[1] F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci. A survey on sensor networks [J]. IEEE Communications Magazine, August 2002, 40 (8): 102-114.
[2] A. Alfieri, A. Bianco, P. Brandimarte, C. F. Chiasserini. Exploiting sensor spatial redundancy to improve network lifetime (wireless sensor networks) [C], Global Telecommunications Conference, 2004, (5): 3170-3176.
[3] D. Marco, E. J. Duarte-Melo, M. Liu, D. Neuho. On the many-to-one transport capacity of a dense wireless sensor network and the compressibility of its data [C], International Workshop on Information Processing in Sensor Networks, 2003: 1-16.
[4] Zixiang Xiong, Angelos D. Liveris, Samuel Cheng. Distributed Source Coding for Sensor Networks [J]. IEEE Signal Processing Magazine, 2004, (19): 80-94.
[5] D. Slepian, J.K. Wolf. Noiseless coding of correlated information sources [J]. IEEE Trans. Inform. Theory, 1973, 19 (4): 471-480.
[6] Romfin M, Hess C, Cerqueira R, et a1. Gaia: A middleware infrastructure to enable active spaces [J]. IEEE Pervasive Computing, 2002, 1 (4): 74-83.
[7] Romn M, Hess C, Cerqueira R, et a1. Gaia:A middleware platform for active spaces [J]. ACM SIGMOBILE Mobile Computing and Communications Review, 2002, 6(4): 65-67.
[8]徐光佑,史元春,谢伟凯.普适计算[J],计算机学报, 2003, 26 (9): 1042-1052.
[9] Kidawara, Y., Zettsu, K. Operating mechanism for device cooperative content on ubiquitous networks [C]. The Second International Conference on Creating, Connecting and Collaborating through Computing, 2004: 54-61.
[10] Kikuchi, H.; Iseki, F; Moo Wan Kim. Development of University Network based on Wireless Ubiquitous Network [C]. The 9th International Conference on Advanced Communication Technology, 2007, 1: 189-194
[11] Estrin D, Govindan R, Heidemann J S, et.al. Next Century challenges: Scalable coordinate in sensor network [C]. In: Proc 5th ACM/IEEE Int’l1 Conf on Mobile Computing and Networking, 1999: 263-270.
[12]王雪.无线传感网络测量系统[M].北京:机械工业出版社,2007.9.
[13]刘雨.无线传感器网络中的信息处理[D].北京邮电大学,2006.5.
[14]崔莉,鞠海玲,苗勇,李天璞,刘巍,赵泽.无线传感器网络研究进展[J].计算机研究与发展, 2005,42(1): 163-174.
[15]马祖长,孙怡宁,梅涛.无线传感器网络综述[J].通信学报, 2004, 25(4):114- 124.
[16] Lin Ruizhong, Wang Zhi, Sun Youxian. Wireless Sensor Networks Solutions for Real TimeMonitoring of Nuclear Power Plant [C]. Proceedings of the 5th IEEE World Congress on Intelligent Control and Automation. 2004:3663-3667.
[17]孙利民,李建中,陈渝等.无线传感器网络[M].北京:清华大学出版社, 2005.2.
[18] Wouter Horr′e, Sam Michiels, Nelson Matthys, et. al. On the integration of sensor networks and general purpose IT infrastructure [C]. In Proceedings of the 2nd International Workshop on Middleware for Sensor Networks, 2007, 11: 7-12.
[19]汪学清.无线传感器网络中连通与覆盖问题研究[D].哈尔滨工程大学, 2006.10.
[20]任彪.无线传感器网络节能机制与移动性的研究[D].北京邮电大学, 2006.9.
[21] Ganesan D, Estrin D, Heidemann J. DIMENSIONS: Why do we need a new data handling architecture for sensor networks? [J]. SIGCOMM Computer communication Review, 2003, 33(1): 143-148.
[22] Li X, Kim Y J, Govindan R. Multi-dimensional Range Queries in Sensor Networks [C]. Proceedings of the ACM Conference on Embedded Networked Sensor Systems. Los Angeles, California, USA, ACM Press, 2003: 63-75.
[23] Madden SR, Szewczyk R, Franklin MJ, Culler D. Supporting aggregate queries over ad-hoc wireless sensor networks [C]. In Proceedings of the Workshop on Mobile Computing and Systems Applications. IEEE Computer Press, 2002: 49-58.
[24] Madden SR, Franklin MJ. Fjording the stream: An architecture for queries over streaming sensor data [C]. In Proceedings of the ICDE Conference. IEEE Computer Press, 2002: 555-566.
[25] Madden SR, Shah MA, Hellerstein JM, Raman V. Continuously adaptive continuous queries over streams [C]. In Proceedings of the SIGMOD Conference. ACM Press, 2002: 49-60.
[26] Madden SR, Franklin MJ, Hellerstein JM, Hong W. The design of an acquisitional query processor for sensor networks [C]. In Proceedings of the SIGMOD Conference. ACM Press, 2003: 491-502.
[27] University of California at Berkeley. TinyDB [EB/OL]. http: //telegraph. cs. berkeley. edu/ tinydb/.
[28] Gerhke J. COUGAR design and implementation [EB/OL]. http: // www. cs. cornell. edu/ database/cougar/.
[29] Yao Y, Gehrke J. The cougar approach to in-network query processing in sensor networks [J]. SIGMOD Record, 2002, 31(3): 9-18.
[30] Bonnet P, Gehrke JE, Seshadri P. Towards sensor database systems [C]. In Proceedings of the 2nd International Conference on Mobile Data Management. Springer-Verlag, 2001:3-14.
[31] Krishnamachari B. Impact of data aggregation in wireless sensor networks [C]. In Proceedings of the International Workshop of Distributed Event Based Systems. IEEE Computer Press, 2002: 1-11.
[32]李建中,李金宝,石胜飞.传感器网络及其数据管理的概念、问题与进展[J].软件学报, 2003, 14(10): 717-1727.
[33] Honggang Wang, Dongming Peng, Wei Wang, et.al. Cross-Layer Routing Optimization in Multirate Wireless Sensor Networks for Distributed Source Coding Based Applications [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2008, 7(10): 3999-4009.
[34] Hui Wang, Yuhang Yang, Maode Ma, et.al. Network Lifetime Maximization with Cross-Layer Design in Wireless Sensor Networks [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2008, 7(10): 3759-3768.
[35] Bart Elen, Sam Michiels, Wouter Joosen, et. al. A Middleware Pattern to Support Complex Sensor Network Applications [C]. ACM SIGPLAN, OOPSLA '06 Workshop on Building Software for Sensor Networks, 2006: 22-26.
[36]蒋杰.无线传感器网络覆盖控制研究[D],国防科学技术大学, 2005, 9.
[37] Khaled Arisha, Moustafa Youssef, Mohamed Younis. Energy-aware TDMA-based MAC for sensor networks [C]. IEEE Workshop on Integrated M an agement of Power Aware Co mmunications, Computing and Networking, New York, USA, 2002: 69-74
[38]李建中,高宏.无线传感器网络的研究进展[J].计算机研究与发展, 2008, 45(1): 1-15.
[39] E Shih, S Cho, N Ickes, et a1. Physical layer driven protocol an d algorithm design for energy-efficient wireless sensor networks [C]. ACM Annual Int’l Co nf on Mobile Co mputing and Networking, Rome, Italy, 2001: 272- 287.
[40] A Woo, D Culler. A transmission control scheme for media access in sensor networks [C]. ACM An nual Int’l Co nf onMobile Computing and Networking, Rome, Italy, 2001, 221- 235.
[41] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-efficient routing protocols for wireless microsensor networks [C]. In Proc. 33rd Hawaii Int. Conf. System Sciences, Jan, 2000: 1-10.
[42] Ossama Younis, Sonia Fahmy. HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks [J]. IEEE TRANSACTIONS ON MOBILE COMPUTING, 2004, 3(4): 366-379.
[43] Amol Deshpande, Carlos Guestrin, Samuel R. Madden. Using Probabilistic Models for Data Management in Acquisitional Environments [C]. In Proceedings of the CIDR, 2005:317-328.
[44] Chu D, Deshpande A, Hellerstein JM, Hong W. Approximate data collection in sensor networks using probabilistic models [C]. In Proceedings of the Data Engineering. IEEE Computer Society, 2006: 48-48.
[45]赵志滨,于戈,李斌阳,姚兰,杨晓春.一种无线传感器网络中的多维K-NN查询优化算法[J].软件学报, 2007, 18(5): 1186-1197.
[46] Abdelmounaam Rezgui, Mohamed Eltoweissy. Service-Driven Query Routing in Sensor Networks [C]. In Proceedings of Local Computer Networks, 2006: 649-655.
[47] Jun-Zhao Sun. Coarse-Grain Data Gathering in Continuous Query for Periodical Phenomena in Wireless Sensor Networks [C]. The 2nd International Conference on Sensor Technologies and Applications, 2008: 525-530.
[48]李建中,石胜飞,王朝坤.基于感知数据概率模型的无线传感器网络采样和通信调度算法[J].计算机应用, 2005, 25(9): 1982-1985.
[49] Liqiang Pan, Jizhou Luo, Jianzhong Li. Probing Queries in Wireless Sensor Networks [C]. The 28th International Conference on Distributed Computing Systems. 2008: 546-553.
[50]刘琳,于海斌,曾鹏.无线传感器网络数据管理技术[J].计算机工程, 2008, 34(2): 62-65.
[51] C, Imatagonwiwat, R. Govindan and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks [C]. In Proceedings of the Sixth Annual ACM/lEEE International Conference on Mobile Computing and Networking, 2000: 56-67.
[52] Boon Thau Loo, Joseph M. Hellerstein, Ion Stoica, Raghu Ramakrishnan. Declarative Routing: Extensible Routing with Declarative Queries [C]. In Proceedings of the ACM workshop on Programmable routers for extensible services of tomorrow, 2005: 63-68.
[53] Scott S, Sylvia R, Brad K, Ramesh G, Deborah E. Data-Centric storage in sensor nets [J]. ACM SIGCOMM Computer Communication Review, 2003, 33(1): 137-142.
[54] Sylvia R, Brad K, Scott S, Deborah E, et.al. Data-Centric storage in sensor nets with GHT, a geographic hash table [J]. Mobile Networks and Applications, 2003, 8(4): 427-442.
[55] Ramakrishna Gummadi, Xin Li, Ramesh Govindan, Cyrus Shahabi, Wei Hong, Energy-Efficient Data Organization and Query Processing in Sensor Networks [J], SIGBED Review, 2005, 2(1): 7-12.
[56] D. Ganesan, B. Greenstein. D. Perelyubskiy, D. Estrin and J. Heidemann. An Evaluation of Multi-resolution Search and Storage in Resource-constrained Sensor Networks [C]. In Proc. of the First ACM Conference on Embedded Networked Sensor Systems, 2003: 89-102.
[57] Yanlei Diao, Deepak Ganesan, Gaurav Mathur, et.al. Rethinking Data Management for Storage-centric Sensor Networks [C]. In Conference on Innovative Data Systems Research,2007: 22-31.
[58] Li X, Bian E Govindan R. Rebalancing Distributed Data Storage in Sensor Networks [EB/OL]. http://www.cs.usc.edu/Research/TechReports/05-852.pdf.
[59]蔚赵春,周水庚,肖斌.无线传感器网络中自适应数据存取[J].软件学报, 2008, 19(1): 103-115.
[60]李贵林,高宏.传感器网络中基于环的负载平衡数据存储方法[J].软件学报.2007, 18(5): 1173-1185.
[61] R. Ramanathan and R. Rosales-Hain. Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment [C]. In the 19th International Annual Joint Conference of the IEEE Computer and Communications Societies, 2000: 404–413.
[62] C. Schurgers, V. Tsiatsis, S. Ganeriwal, and M. Srivastava. Optimizing Sensor Networks in the Energy-Latency-Density Design Space [J]. IEEE Transactions on Mobile Computing, 2002, 1(1): 70-80.
[63] Y. Xu, J. Heidemann, and D. Estrin. Geography-informed Energy Conservation for Ad Hoc Routing [C]. In the ACM/IEEE International Conference on Mobile Computing and Networking. 2001: 70-84.
[64] B. Chen, K. Jamieson, H. Balakrishnan, R. Morris. Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks [C]. In the 7th ACM International Conference on Mobile Computing and Networking, 2001: 85-96.
[65] A. Cerpa and D. Estrin. ASCENT: Adaptive Self-Configuring Sensor Network Topologies [J]. IEEE Transactions on Mobile Computing, 2004, 3(3): 272-285.
[66] Abhishek G, Jens G, John C. Resilient data-centric storage in wireless ad-hoc sensor networks [C]. In the 4th Conf. on Mobile Data Management, 2003: 45-62.
[67] Yantao Pan, Xicheng Lu. Energy-efficient lifetime maximization and sleeping scheduling supporting data fusion and QoS in Multi-Sensor Net [J]. Signal Processing 2007, 87(12), 2949-2964.
[68] A. Demers, J. Gehrke, R. Rajaraman, N. Trigoni, and Y. Yao. Energy-Efficient Data Management for Sensor Networks: A Work-In-Progress Report [EB/OL]. http: //www. comlab. ox. ac.uk/sensors/publications/Demers_UpstateNYWorkshop2003.pdf
[69] Israfil Bahceci, Amir K. Khandani. Linear Estimation of Correlated Data in Wireless Sensor Networks with Optimum Power Allocation and Analog Modulation [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2008, 56(7): 1146- 1156.
[70] Wook C, Das S K. A novel frame work for energy conserving data gathering in wirelesssensor networks[C]. Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communication Societies, 2005: 1985-1996.
[71] Lindsey S, Raghavendra C, Sivalingam K M. Data gathering algorithms in sensor networks using energy metrics [J]. IEEE Transaction on Parallel and Distributed Systems, 2002, 13(9): 924-935.
[72]周四望,林亚平,聂雅琳,等.无线传感器网络中基于数据融合的移动代理曲线动态路由算法研究[J].计算机学报, 2007, 30(6): 894- 904.
[73] Hairong Qi, S. Sitharama Iyengar, Krishnendu Chakrabarty. Multi-Resolution Data Integration Using Mobile Agents in Distributed Sensor Networks [J]. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS, 2001, 31(3): 383-391.
[74] Min Chen, Taekyoung Kwon, Yanghee Choi. Data Dissemination based on Mobile Agent in Wireless Sensor Networks [C]. Proceedings of the IEEE Conference on Local Computer Networks 30th Anniversary, 2005: 1-2.
[75] Samuel Madden, Michael J. Franklin, Joseph Hellerstein et. al. TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks [C]. Proceedings of the Fifth Symposium on Operating Systems Design and implementation.2002: 131-146.
[76] Manjeshwar, Dharma P. Agrawal. TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks [C]. 15th International Parallel and Distributed Processing Symposium, Apr, 2001: 2009-2015.
[77] Estrin D. Wireless Sensor Networks Tutorial part IV: Sensor Network Protocols. In Proceedings of the ACM Mobile Computing and Networking, 2002: 23-28.
[78] W. Ding, S. Iyengar and R. Kannan. Energy equivalence routing in wireless sensor networks [J]. Microprocessors and Microsystems, 2004, 28(8): 467-475.
[79] N. Hu, Y. Zhang. Energy Balance Routing in Wireless Sensor Networks [J]. Journal of Xi’an Jiaotong University, 2006, 40(6): 675-680.
[80]杨军,张德运,张云翼等.基于分簇的无线传感器网络数据汇聚传送协议[J],软件学报,2009, doi: 10.3724/SP.J.1001.2009.03534.
[81]张学,陆桑璐,陈贵海,陈道蓄,谢立.无线传感器网络的拓扑控制[J].软件学报, 18(4), 943-954.
[82] Poduri S, Pattem S, Krishnamachari B, Sukhatme G. A unifying framework for tunable topology control in sensor networks [R]. Technical Report, CRES-05-004, University of Southern California, 2005: 1-15.
[83] Oussalah M, De Schutter J. Hybrid Fuzzy Probabilistic Data Association Filter and JointProbabilistic Data Association Filter [J]. Information Sciences, 2002, 142(1- 4): 195-226.
[84] GREGORY. P, WILLIAM J. K. Embedding the Internet: Wireless integrated network sensors [J]. Communications of the ACM. 2000, 43(5): 1-58.
[85]隆克平,邓银波,陈前斌,等.移动IP和MPLS结合的网格体系结构及关键技术[J].重庆邮电学院学报(自然科学版), 2004, 16(6): 1-6.
[86] Bonfils B, Bonnet P. Adaptive and decentralized operator placement for in-network query processing. Telecommunication Systems, 2004, 26(2-4): 389-409.
[87]陈颖文,徐明,吴一.无线传感器网络网内数据处理节点的优化选取[J].软件学报, 2007, 18(12): 3104-3114.
[88] Q. Xue, A. Ganz. Maximizing Sensor Network Lifetime: Analysis and Design Guides [C]. In Proceedings of the 2004 Military Communications Conference, 2004, 2: 1144-1150.
[89] Santi P. Silence is golden with high probability: Maintaining a connected backbone in wireless sensor networks [C]. Proc. of the 1st European Workshop on Wireless Sensor Networks, 2004, 1: 106-121.
[90] Lindsey S, Raghavendra CS. PEGASIS: Power Efficient Gathering in Sensor Information Systems [C]. Proceedings of IEEE Aerospace Conference. Montana, USA: IEEE Computer Society, 2002: 23-29.
[91]李成法,陈贵海,叶懋等.一种基于非均匀分簇的无线传感器网络路由协议[J].计算机学报,2007, 30(8): 27-30.
[92] B. O. Priscilla Chen and E. Callaway. Energy efficient system design with optimum transmission range for wireless ad-hoc networks [C]. In Proceedings of the 2002 IEEE International Conference on Communications, 2002, 2: 945-952.
[93] Z. Shelby, C. Pomalaza-Raez, H. Karvonen. Energy optimization in multihop wireless embedded and sensor networks [J]. International Journal of Wireless Information Networks, 2005, 12(1): 11-20.
[94]刘敏华,萧德云.基于信息熵的多传感器数据分类方法[J].控制与决策, 2006, 21(4): 410-414.
[95] HallD L, L linas J. An Introduction to Multi-sensor Data Fusion [C]. Proc of the IEEE, 1997, 85 (1): 6-23.
[96]文成林.多传感器单模型动态系统多尺度数据融合[J].电子学报, 2001, 29 (3): 341-345.
[97] REN C. LUO, MICHAEL G. KEY. Multisensor Integration and Fusion in Intelligent Systems [J]. IEEE Trans on Systems, Man, and Cybernetics, 1989, 19 (5): 901-931.
[98] E. Shih, S. Cho, N. Ickes, et al. Physical layer driven protocol and algorithm design forenergy-efficient wireless sensor networks [C]. Proc. of the 7th Annual Int’l Conf. on Mobile Computing and Networking (MobiCom 2001), Rome: ACM Press, 2001: 272-287.
[99] D. Tian, N. Georganas. A coverage-preserving node scheduling scheme for large wireless sensor networks [C]. Proc. of the 1st International Workshop on Wireless Sensor Networks and Applications (WSNA 2002). Atlanta: ACM Press, 2002: 32-41.
[100] J. Lu, J. Wang, T. Suda. Scalable Coverage Maintenance for Dense Wireless Sensor Networks [J]. Journal on Wireless Communications and Networking, Hindawi Publishing Corporation, 2007: 1-12.
[101] M. Liu, J. Cao, Y. Zheng, et al. Analysis for Multi-Coverage Problem in Wireless Sensor Networks [J]. Journal of Software, 2007, 18(1): 127-136.
[102] Gao Y, Wu K, Li F. Analysis on the redundancy of wireless sensor networks [C]. Proc. of the 2nd ACM Int’l Conf. on Wireless Sensor Networks and Applications (WSNA 2003). San Diego: ACM Press, 2003. 108-114.
[103]李国华,刘宝玲,沈树群.用于区域监测的无线传感器网络数据去冗余研究[J].微电子学与计算机, 2005, 22(9): 134-136.
[104]禹春来,许化龙,黄世奇.基于关系矩阵的多传感器数据融合方法[J].航空计算技术, 2005, 35(1): 23-26.
[105]高方伟,刘贵喜,王蕾,张靖.基于支持度矩阵的一种多传感器融合方法[J].弹箭与制导学报, 2007, 27(4): 284-287.
[106]王威,周军红,王润生.多传感器数据融合的一种方法[J].传感器技术, 2003, 22(9): 39-41.
[107]刘建书,李人厚,刘云龙等.基于相关性函数和模糊综合函数的多传感器数据融合[J].系统工程与电子技术, 2006, 28(7): 1006-1009.
[108]刘明,曹建农,郑源.无线传感器网络多重覆盖问题分析[J].软件学报, 2007, 18(1): 127-136.
[109] H. Koskinen. On the coverage of a random sensor network in a bounded domain [C]. In Proc. ITC Specialist Seminar on Performance Evaluation of Wireless and Mobile Systems, 2004.
[110]任彦,张思东,张宏科.无线传感器网络中覆盖控制理论与算法[J].软件学报, 2006, 17(3): 422-433.
[111] Kar K, Banerjee S. Node placement for connected coverage in sensor networks [C]. In: Crowcroft J, ed. Proc. of the Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks. Sophia-Antipolis: IEEE Press, 2003: 50-52.
[112] Gupta H, Das SR, Gu Q. Connected sensor cover: Self-Organization of sensor networks for efficient query execution [C]. Proc. of the ACM Int’l Symp. on Mobile Ad HocNetworking and Computing. New York: ACM Press, 2003: 189-200.
[113] Huang CF, Tseng YC. A survey of solutions to the coverage problems in wireless sensor networks [J]. Journal of Internet Technology, 2005, 6(1):1-8.
[114] Xue Wang, Sheng Wang, Jun-Jie Ma, et. al. Energy-efficient Organization of Wireless Sensor Networks with Adaptive Forecasting [J], Sensors, 2008, 8: 2604-2616.
[115] J. Considine, F. Li, G. Kollios, J. W. Byers. Approximate aggregation techniques for sensor databases [C]. In Proc of the 20th International Conference on Data Engineering, 2004: 449-460.
[116]郭水霞,王一夫,陈安.基于支持向量机回归模型的海量数据预测[J],计算机工程与应用, 2007, 43(5): 12-15.
[117]张志明,程惠涛,徐鸿,等.神经网络组合预报模型及其在汽轮发电机组状态检修中的应用[J],中国电机工程学报,2003, 23(9): 204-206.
[118] Deng Julong. Three stages for grey modeling and grey model: GM(1, 1|τ, r), GM (1 ,1|tg(k-τ) p, sin(k-τ) p) [J]. The Journal of Grey System.2002, 3:213-216.
[119] Guo Yifan, Deng Julong. The Influence of variation of modeling data on parameters of GM(1,1) model [J]. The Journal of Grey System. 2004, 1: 29-34.
[120]吉培荣,黄巍松,胡翔勇.无偏灰色预测模型[J],系统工程与电子技术, 2000, 22(6): 6-8.
[121]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社, 2002.
[122] Jer Min Jou, Pei Yinchen, Jian Mingsun. The Gray Prediction Search Algorithm for Block Motion Estimation [J]. IEEE Transactions on Circuits and Systems for Video Technology .1999, 9(6): 843-847.
[123]邓聚龙.灰预测与灰决策(修订版) [M].武汉:华中科技大学出版社, 2002.
[124] Chang Shih-chi, LA1 Hsien-che, YU Hsiao-cheng. A Variable P Value Rolling Grey Forecasting Model for Taiwan Semiconductor Industry Production [J]. Technological Forecasting & Social Change, 2005, 72(5): 623-640.
[125] Wang Qijie, Liao Xinhao, Zhou Yonghong, et al. Hybrid Grey Model to Forecast Monitoring Series with Seasonality [J]. Journal of Central South University of Technology, 2005, 12(5): 623-627.
[126]谢乃明,刘思峰.离散GM(1,1)模型与灰色预测模型建模机理[J],系统工程理论与实践. 2005, 25(1): 93-98.
[127] Baek W, Bommareddy S. Optimal m-ary data fusion with distributed sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(3): 1150-1152.
[128] Yifeng Z, Leung H, Yip P C. An exact maximum likelihood registration algorithm for data fusion [J]. IEEE Transactions on Signal Processing, 1997, 45(1): 1560-1573.
[129]唐琎,张闻捷,高琰,等.不同精度冗余数据的融合[J].自动化学报,2005, 31(6): 934-942.
[130]刘建书,李人厚,常宏.基于相关性函数和最小二乘的多传感器数据融合[J].控制与决策, 2006, 21(6): 714-717.
[2] A. Alfieri, A. Bianco, P. Brandimarte, C. F. Chiasserini. Exploiting sensor spatial redundancy to improve network lifetime (wireless sensor networks) [C], Global Telecommunications Conference, 2004, (5): 3170-3176.
[3] D. Marco, E. J. Duarte-Melo, M. Liu, D. Neuho. On the many-to-one transport capacity of a dense wireless sensor network and the compressibility of its data [C], International Workshop on Information Processing in Sensor Networks, 2003: 1-16.
[4] Zixiang Xiong, Angelos D. Liveris, Samuel Cheng. Distributed Source Coding for Sensor Networks [J]. IEEE Signal Processing Magazine, 2004, (19): 80-94.
[5] D. Slepian, J.K. Wolf. Noiseless coding of correlated information sources [J]. IEEE Trans. Inform. Theory, 1973, 19 (4): 471-480.
[6] Romfin M, Hess C, Cerqueira R, et a1. Gaia: A middleware infrastructure to enable active spaces [J]. IEEE Pervasive Computing, 2002, 1 (4): 74-83.
[7] Romn M, Hess C, Cerqueira R, et a1. Gaia:A middleware platform for active spaces [J]. ACM SIGMOBILE Mobile Computing and Communications Review, 2002, 6(4): 65-67.
[8]徐光佑,史元春,谢伟凯.普适计算[J],计算机学报, 2003, 26 (9): 1042-1052.
[9] Kidawara, Y., Zettsu, K. Operating mechanism for device cooperative content on ubiquitous networks [C]. The Second International Conference on Creating, Connecting and Collaborating through Computing, 2004: 54-61.
[10] Kikuchi, H.; Iseki, F; Moo Wan Kim. Development of University Network based on Wireless Ubiquitous Network [C]. The 9th International Conference on Advanced Communication Technology, 2007, 1: 189-194
[11] Estrin D, Govindan R, Heidemann J S, et.al. Next Century challenges: Scalable coordinate in sensor network [C]. In: Proc 5th ACM/IEEE Int’l1 Conf on Mobile Computing and Networking, 1999: 263-270.
[12]王雪.无线传感网络测量系统[M].北京:机械工业出版社,2007.9.
[13]刘雨.无线传感器网络中的信息处理[D].北京邮电大学,2006.5.
[14]崔莉,鞠海玲,苗勇,李天璞,刘巍,赵泽.无线传感器网络研究进展[J].计算机研究与发展, 2005,42(1): 163-174.
[15]马祖长,孙怡宁,梅涛.无线传感器网络综述[J].通信学报, 2004, 25(4):114- 124.
[16] Lin Ruizhong, Wang Zhi, Sun Youxian. Wireless Sensor Networks Solutions for Real TimeMonitoring of Nuclear Power Plant [C]. Proceedings of the 5th IEEE World Congress on Intelligent Control and Automation. 2004:3663-3667.
[17]孙利民,李建中,陈渝等.无线传感器网络[M].北京:清华大学出版社, 2005.2.
[18] Wouter Horr′e, Sam Michiels, Nelson Matthys, et. al. On the integration of sensor networks and general purpose IT infrastructure [C]. In Proceedings of the 2nd International Workshop on Middleware for Sensor Networks, 2007, 11: 7-12.
[19]汪学清.无线传感器网络中连通与覆盖问题研究[D].哈尔滨工程大学, 2006.10.
[20]任彪.无线传感器网络节能机制与移动性的研究[D].北京邮电大学, 2006.9.
[21] Ganesan D, Estrin D, Heidemann J. DIMENSIONS: Why do we need a new data handling architecture for sensor networks? [J]. SIGCOMM Computer communication Review, 2003, 33(1): 143-148.
[22] Li X, Kim Y J, Govindan R. Multi-dimensional Range Queries in Sensor Networks [C]. Proceedings of the ACM Conference on Embedded Networked Sensor Systems. Los Angeles, California, USA, ACM Press, 2003: 63-75.
[23] Madden SR, Szewczyk R, Franklin MJ, Culler D. Supporting aggregate queries over ad-hoc wireless sensor networks [C]. In Proceedings of the Workshop on Mobile Computing and Systems Applications. IEEE Computer Press, 2002: 49-58.
[24] Madden SR, Franklin MJ. Fjording the stream: An architecture for queries over streaming sensor data [C]. In Proceedings of the ICDE Conference. IEEE Computer Press, 2002: 555-566.
[25] Madden SR, Shah MA, Hellerstein JM, Raman V. Continuously adaptive continuous queries over streams [C]. In Proceedings of the SIGMOD Conference. ACM Press, 2002: 49-60.
[26] Madden SR, Franklin MJ, Hellerstein JM, Hong W. The design of an acquisitional query processor for sensor networks [C]. In Proceedings of the SIGMOD Conference. ACM Press, 2003: 491-502.
[27] University of California at Berkeley. TinyDB [EB/OL]. http: //telegraph. cs. berkeley. edu/ tinydb/.
[28] Gerhke J. COUGAR design and implementation [EB/OL]. http: // www. cs. cornell. edu/ database/cougar/.
[29] Yao Y, Gehrke J. The cougar approach to in-network query processing in sensor networks [J]. SIGMOD Record, 2002, 31(3): 9-18.
[30] Bonnet P, Gehrke JE, Seshadri P. Towards sensor database systems [C]. In Proceedings of the 2nd International Conference on Mobile Data Management. Springer-Verlag, 2001:3-14.
[31] Krishnamachari B. Impact of data aggregation in wireless sensor networks [C]. In Proceedings of the International Workshop of Distributed Event Based Systems. IEEE Computer Press, 2002: 1-11.
[32]李建中,李金宝,石胜飞.传感器网络及其数据管理的概念、问题与进展[J].软件学报, 2003, 14(10): 717-1727.
[33] Honggang Wang, Dongming Peng, Wei Wang, et.al. Cross-Layer Routing Optimization in Multirate Wireless Sensor Networks for Distributed Source Coding Based Applications [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2008, 7(10): 3999-4009.
[34] Hui Wang, Yuhang Yang, Maode Ma, et.al. Network Lifetime Maximization with Cross-Layer Design in Wireless Sensor Networks [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2008, 7(10): 3759-3768.
[35] Bart Elen, Sam Michiels, Wouter Joosen, et. al. A Middleware Pattern to Support Complex Sensor Network Applications [C]. ACM SIGPLAN, OOPSLA '06 Workshop on Building Software for Sensor Networks, 2006: 22-26.
[36]蒋杰.无线传感器网络覆盖控制研究[D],国防科学技术大学, 2005, 9.
[37] Khaled Arisha, Moustafa Youssef, Mohamed Younis. Energy-aware TDMA-based MAC for sensor networks [C]. IEEE Workshop on Integrated M an agement of Power Aware Co mmunications, Computing and Networking, New York, USA, 2002: 69-74
[38]李建中,高宏.无线传感器网络的研究进展[J].计算机研究与发展, 2008, 45(1): 1-15.
[39] E Shih, S Cho, N Ickes, et a1. Physical layer driven protocol an d algorithm design for energy-efficient wireless sensor networks [C]. ACM Annual Int’l Co nf on Mobile Co mputing and Networking, Rome, Italy, 2001: 272- 287.
[40] A Woo, D Culler. A transmission control scheme for media access in sensor networks [C]. ACM An nual Int’l Co nf onMobile Computing and Networking, Rome, Italy, 2001, 221- 235.
[41] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-efficient routing protocols for wireless microsensor networks [C]. In Proc. 33rd Hawaii Int. Conf. System Sciences, Jan, 2000: 1-10.
[42] Ossama Younis, Sonia Fahmy. HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks [J]. IEEE TRANSACTIONS ON MOBILE COMPUTING, 2004, 3(4): 366-379.
[43] Amol Deshpande, Carlos Guestrin, Samuel R. Madden. Using Probabilistic Models for Data Management in Acquisitional Environments [C]. In Proceedings of the CIDR, 2005:317-328.
[44] Chu D, Deshpande A, Hellerstein JM, Hong W. Approximate data collection in sensor networks using probabilistic models [C]. In Proceedings of the Data Engineering. IEEE Computer Society, 2006: 48-48.
[45]赵志滨,于戈,李斌阳,姚兰,杨晓春.一种无线传感器网络中的多维K-NN查询优化算法[J].软件学报, 2007, 18(5): 1186-1197.
[46] Abdelmounaam Rezgui, Mohamed Eltoweissy. Service-Driven Query Routing in Sensor Networks [C]. In Proceedings of Local Computer Networks, 2006: 649-655.
[47] Jun-Zhao Sun. Coarse-Grain Data Gathering in Continuous Query for Periodical Phenomena in Wireless Sensor Networks [C]. The 2nd International Conference on Sensor Technologies and Applications, 2008: 525-530.
[48]李建中,石胜飞,王朝坤.基于感知数据概率模型的无线传感器网络采样和通信调度算法[J].计算机应用, 2005, 25(9): 1982-1985.
[49] Liqiang Pan, Jizhou Luo, Jianzhong Li. Probing Queries in Wireless Sensor Networks [C]. The 28th International Conference on Distributed Computing Systems. 2008: 546-553.
[50]刘琳,于海斌,曾鹏.无线传感器网络数据管理技术[J].计算机工程, 2008, 34(2): 62-65.
[51] C, Imatagonwiwat, R. Govindan and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks [C]. In Proceedings of the Sixth Annual ACM/lEEE International Conference on Mobile Computing and Networking, 2000: 56-67.
[52] Boon Thau Loo, Joseph M. Hellerstein, Ion Stoica, Raghu Ramakrishnan. Declarative Routing: Extensible Routing with Declarative Queries [C]. In Proceedings of the ACM workshop on Programmable routers for extensible services of tomorrow, 2005: 63-68.
[53] Scott S, Sylvia R, Brad K, Ramesh G, Deborah E. Data-Centric storage in sensor nets [J]. ACM SIGCOMM Computer Communication Review, 2003, 33(1): 137-142.
[54] Sylvia R, Brad K, Scott S, Deborah E, et.al. Data-Centric storage in sensor nets with GHT, a geographic hash table [J]. Mobile Networks and Applications, 2003, 8(4): 427-442.
[55] Ramakrishna Gummadi, Xin Li, Ramesh Govindan, Cyrus Shahabi, Wei Hong, Energy-Efficient Data Organization and Query Processing in Sensor Networks [J], SIGBED Review, 2005, 2(1): 7-12.
[56] D. Ganesan, B. Greenstein. D. Perelyubskiy, D. Estrin and J. Heidemann. An Evaluation of Multi-resolution Search and Storage in Resource-constrained Sensor Networks [C]. In Proc. of the First ACM Conference on Embedded Networked Sensor Systems, 2003: 89-102.
[57] Yanlei Diao, Deepak Ganesan, Gaurav Mathur, et.al. Rethinking Data Management for Storage-centric Sensor Networks [C]. In Conference on Innovative Data Systems Research,2007: 22-31.
[58] Li X, Bian E Govindan R. Rebalancing Distributed Data Storage in Sensor Networks [EB/OL]. http://www.cs.usc.edu/Research/TechReports/05-852.pdf.
[59]蔚赵春,周水庚,肖斌.无线传感器网络中自适应数据存取[J].软件学报, 2008, 19(1): 103-115.
[60]李贵林,高宏.传感器网络中基于环的负载平衡数据存储方法[J].软件学报.2007, 18(5): 1173-1185.
[61] R. Ramanathan and R. Rosales-Hain. Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment [C]. In the 19th International Annual Joint Conference of the IEEE Computer and Communications Societies, 2000: 404–413.
[62] C. Schurgers, V. Tsiatsis, S. Ganeriwal, and M. Srivastava. Optimizing Sensor Networks in the Energy-Latency-Density Design Space [J]. IEEE Transactions on Mobile Computing, 2002, 1(1): 70-80.
[63] Y. Xu, J. Heidemann, and D. Estrin. Geography-informed Energy Conservation for Ad Hoc Routing [C]. In the ACM/IEEE International Conference on Mobile Computing and Networking. 2001: 70-84.
[64] B. Chen, K. Jamieson, H. Balakrishnan, R. Morris. Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks [C]. In the 7th ACM International Conference on Mobile Computing and Networking, 2001: 85-96.
[65] A. Cerpa and D. Estrin. ASCENT: Adaptive Self-Configuring Sensor Network Topologies [J]. IEEE Transactions on Mobile Computing, 2004, 3(3): 272-285.
[66] Abhishek G, Jens G, John C. Resilient data-centric storage in wireless ad-hoc sensor networks [C]. In the 4th Conf. on Mobile Data Management, 2003: 45-62.
[67] Yantao Pan, Xicheng Lu. Energy-efficient lifetime maximization and sleeping scheduling supporting data fusion and QoS in Multi-Sensor Net [J]. Signal Processing 2007, 87(12), 2949-2964.
[68] A. Demers, J. Gehrke, R. Rajaraman, N. Trigoni, and Y. Yao. Energy-Efficient Data Management for Sensor Networks: A Work-In-Progress Report [EB/OL]. http: //www. comlab. ox. ac.uk/sensors/publications/Demers_UpstateNYWorkshop2003.pdf
[69] Israfil Bahceci, Amir K. Khandani. Linear Estimation of Correlated Data in Wireless Sensor Networks with Optimum Power Allocation and Analog Modulation [J]. IEEE TRANSACTIONS ON COMMUNICATIONS, 2008, 56(7): 1146- 1156.
[70] Wook C, Das S K. A novel frame work for energy conserving data gathering in wirelesssensor networks[C]. Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communication Societies, 2005: 1985-1996.
[71] Lindsey S, Raghavendra C, Sivalingam K M. Data gathering algorithms in sensor networks using energy metrics [J]. IEEE Transaction on Parallel and Distributed Systems, 2002, 13(9): 924-935.
[72]周四望,林亚平,聂雅琳,等.无线传感器网络中基于数据融合的移动代理曲线动态路由算法研究[J].计算机学报, 2007, 30(6): 894- 904.
[73] Hairong Qi, S. Sitharama Iyengar, Krishnendu Chakrabarty. Multi-Resolution Data Integration Using Mobile Agents in Distributed Sensor Networks [J]. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS, 2001, 31(3): 383-391.
[74] Min Chen, Taekyoung Kwon, Yanghee Choi. Data Dissemination based on Mobile Agent in Wireless Sensor Networks [C]. Proceedings of the IEEE Conference on Local Computer Networks 30th Anniversary, 2005: 1-2.
[75] Samuel Madden, Michael J. Franklin, Joseph Hellerstein et. al. TAG: a Tiny Aggregation Service for Ad-Hoc Sensor Networks [C]. Proceedings of the Fifth Symposium on Operating Systems Design and implementation.2002: 131-146.
[76] Manjeshwar, Dharma P. Agrawal. TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks [C]. 15th International Parallel and Distributed Processing Symposium, Apr, 2001: 2009-2015.
[77] Estrin D. Wireless Sensor Networks Tutorial part IV: Sensor Network Protocols. In Proceedings of the ACM Mobile Computing and Networking, 2002: 23-28.
[78] W. Ding, S. Iyengar and R. Kannan. Energy equivalence routing in wireless sensor networks [J]. Microprocessors and Microsystems, 2004, 28(8): 467-475.
[79] N. Hu, Y. Zhang. Energy Balance Routing in Wireless Sensor Networks [J]. Journal of Xi’an Jiaotong University, 2006, 40(6): 675-680.
[80]杨军,张德运,张云翼等.基于分簇的无线传感器网络数据汇聚传送协议[J],软件学报,2009, doi: 10.3724/SP.J.1001.2009.03534.
[81]张学,陆桑璐,陈贵海,陈道蓄,谢立.无线传感器网络的拓扑控制[J].软件学报, 18(4), 943-954.
[82] Poduri S, Pattem S, Krishnamachari B, Sukhatme G. A unifying framework for tunable topology control in sensor networks [R]. Technical Report, CRES-05-004, University of Southern California, 2005: 1-15.
[83] Oussalah M, De Schutter J. Hybrid Fuzzy Probabilistic Data Association Filter and JointProbabilistic Data Association Filter [J]. Information Sciences, 2002, 142(1- 4): 195-226.
[84] GREGORY. P, WILLIAM J. K. Embedding the Internet: Wireless integrated network sensors [J]. Communications of the ACM. 2000, 43(5): 1-58.
[85]隆克平,邓银波,陈前斌,等.移动IP和MPLS结合的网格体系结构及关键技术[J].重庆邮电学院学报(自然科学版), 2004, 16(6): 1-6.
[86] Bonfils B, Bonnet P. Adaptive and decentralized operator placement for in-network query processing. Telecommunication Systems, 2004, 26(2-4): 389-409.
[87]陈颖文,徐明,吴一.无线传感器网络网内数据处理节点的优化选取[J].软件学报, 2007, 18(12): 3104-3114.
[88] Q. Xue, A. Ganz. Maximizing Sensor Network Lifetime: Analysis and Design Guides [C]. In Proceedings of the 2004 Military Communications Conference, 2004, 2: 1144-1150.
[89] Santi P. Silence is golden with high probability: Maintaining a connected backbone in wireless sensor networks [C]. Proc. of the 1st European Workshop on Wireless Sensor Networks, 2004, 1: 106-121.
[90] Lindsey S, Raghavendra CS. PEGASIS: Power Efficient Gathering in Sensor Information Systems [C]. Proceedings of IEEE Aerospace Conference. Montana, USA: IEEE Computer Society, 2002: 23-29.
[91]李成法,陈贵海,叶懋等.一种基于非均匀分簇的无线传感器网络路由协议[J].计算机学报,2007, 30(8): 27-30.
[92] B. O. Priscilla Chen and E. Callaway. Energy efficient system design with optimum transmission range for wireless ad-hoc networks [C]. In Proceedings of the 2002 IEEE International Conference on Communications, 2002, 2: 945-952.
[93] Z. Shelby, C. Pomalaza-Raez, H. Karvonen. Energy optimization in multihop wireless embedded and sensor networks [J]. International Journal of Wireless Information Networks, 2005, 12(1): 11-20.
[94]刘敏华,萧德云.基于信息熵的多传感器数据分类方法[J].控制与决策, 2006, 21(4): 410-414.
[95] HallD L, L linas J. An Introduction to Multi-sensor Data Fusion [C]. Proc of the IEEE, 1997, 85 (1): 6-23.
[96]文成林.多传感器单模型动态系统多尺度数据融合[J].电子学报, 2001, 29 (3): 341-345.
[97] REN C. LUO, MICHAEL G. KEY. Multisensor Integration and Fusion in Intelligent Systems [J]. IEEE Trans on Systems, Man, and Cybernetics, 1989, 19 (5): 901-931.
[98] E. Shih, S. Cho, N. Ickes, et al. Physical layer driven protocol and algorithm design forenergy-efficient wireless sensor networks [C]. Proc. of the 7th Annual Int’l Conf. on Mobile Computing and Networking (MobiCom 2001), Rome: ACM Press, 2001: 272-287.
[99] D. Tian, N. Georganas. A coverage-preserving node scheduling scheme for large wireless sensor networks [C]. Proc. of the 1st International Workshop on Wireless Sensor Networks and Applications (WSNA 2002). Atlanta: ACM Press, 2002: 32-41.
[100] J. Lu, J. Wang, T. Suda. Scalable Coverage Maintenance for Dense Wireless Sensor Networks [J]. Journal on Wireless Communications and Networking, Hindawi Publishing Corporation, 2007: 1-12.
[101] M. Liu, J. Cao, Y. Zheng, et al. Analysis for Multi-Coverage Problem in Wireless Sensor Networks [J]. Journal of Software, 2007, 18(1): 127-136.
[102] Gao Y, Wu K, Li F. Analysis on the redundancy of wireless sensor networks [C]. Proc. of the 2nd ACM Int’l Conf. on Wireless Sensor Networks and Applications (WSNA 2003). San Diego: ACM Press, 2003. 108-114.
[103]李国华,刘宝玲,沈树群.用于区域监测的无线传感器网络数据去冗余研究[J].微电子学与计算机, 2005, 22(9): 134-136.
[104]禹春来,许化龙,黄世奇.基于关系矩阵的多传感器数据融合方法[J].航空计算技术, 2005, 35(1): 23-26.
[105]高方伟,刘贵喜,王蕾,张靖.基于支持度矩阵的一种多传感器融合方法[J].弹箭与制导学报, 2007, 27(4): 284-287.
[106]王威,周军红,王润生.多传感器数据融合的一种方法[J].传感器技术, 2003, 22(9): 39-41.
[107]刘建书,李人厚,刘云龙等.基于相关性函数和模糊综合函数的多传感器数据融合[J].系统工程与电子技术, 2006, 28(7): 1006-1009.
[108]刘明,曹建农,郑源.无线传感器网络多重覆盖问题分析[J].软件学报, 2007, 18(1): 127-136.
[109] H. Koskinen. On the coverage of a random sensor network in a bounded domain [C]. In Proc. ITC Specialist Seminar on Performance Evaluation of Wireless and Mobile Systems, 2004.
[110]任彦,张思东,张宏科.无线传感器网络中覆盖控制理论与算法[J].软件学报, 2006, 17(3): 422-433.
[111] Kar K, Banerjee S. Node placement for connected coverage in sensor networks [C]. In: Crowcroft J, ed. Proc. of the Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks. Sophia-Antipolis: IEEE Press, 2003: 50-52.
[112] Gupta H, Das SR, Gu Q. Connected sensor cover: Self-Organization of sensor networks for efficient query execution [C]. Proc. of the ACM Int’l Symp. on Mobile Ad HocNetworking and Computing. New York: ACM Press, 2003: 189-200.
[113] Huang CF, Tseng YC. A survey of solutions to the coverage problems in wireless sensor networks [J]. Journal of Internet Technology, 2005, 6(1):1-8.
[114] Xue Wang, Sheng Wang, Jun-Jie Ma, et. al. Energy-efficient Organization of Wireless Sensor Networks with Adaptive Forecasting [J], Sensors, 2008, 8: 2604-2616.
[115] J. Considine, F. Li, G. Kollios, J. W. Byers. Approximate aggregation techniques for sensor databases [C]. In Proc of the 20th International Conference on Data Engineering, 2004: 449-460.
[116]郭水霞,王一夫,陈安.基于支持向量机回归模型的海量数据预测[J],计算机工程与应用, 2007, 43(5): 12-15.
[117]张志明,程惠涛,徐鸿,等.神经网络组合预报模型及其在汽轮发电机组状态检修中的应用[J],中国电机工程学报,2003, 23(9): 204-206.
[118] Deng Julong. Three stages for grey modeling and grey model: GM(1, 1|τ, r), GM (1 ,1|tg(k-τ) p, sin(k-τ) p) [J]. The Journal of Grey System.2002, 3:213-216.
[119] Guo Yifan, Deng Julong. The Influence of variation of modeling data on parameters of GM(1,1) model [J]. The Journal of Grey System. 2004, 1: 29-34.
[120]吉培荣,黄巍松,胡翔勇.无偏灰色预测模型[J],系统工程与电子技术, 2000, 22(6): 6-8.
[121]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社, 2002.
[122] Jer Min Jou, Pei Yinchen, Jian Mingsun. The Gray Prediction Search Algorithm for Block Motion Estimation [J]. IEEE Transactions on Circuits and Systems for Video Technology .1999, 9(6): 843-847.
[123]邓聚龙.灰预测与灰决策(修订版) [M].武汉:华中科技大学出版社, 2002.
[124] Chang Shih-chi, LA1 Hsien-che, YU Hsiao-cheng. A Variable P Value Rolling Grey Forecasting Model for Taiwan Semiconductor Industry Production [J]. Technological Forecasting & Social Change, 2005, 72(5): 623-640.
[125] Wang Qijie, Liao Xinhao, Zhou Yonghong, et al. Hybrid Grey Model to Forecast Monitoring Series with Seasonality [J]. Journal of Central South University of Technology, 2005, 12(5): 623-627.
[126]谢乃明,刘思峰.离散GM(1,1)模型与灰色预测模型建模机理[J],系统工程理论与实践. 2005, 25(1): 93-98.
[127] Baek W, Bommareddy S. Optimal m-ary data fusion with distributed sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(3): 1150-1152.
[128] Yifeng Z, Leung H, Yip P C. An exact maximum likelihood registration algorithm for data fusion [J]. IEEE Transactions on Signal Processing, 1997, 45(1): 1560-1573.
[129]唐琎,张闻捷,高琰,等.不同精度冗余数据的融合[J].自动化学报,2005, 31(6): 934-942.
[130]刘建书,李人厚,常宏.基于相关性函数和最小二乘的多传感器数据融合[J].控制与决策, 2006, 21(6): 714-717.