基于数据融合的无线传感器网络路由技术研究
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摘要
无线传感器网络(Wireless Sensor Networks,WSN)由大量廉价的传感器节点所构成,节点间通过无线通信方式形成一个多跳自组织网络。普通传感器节点可以通过其携带的各种类型的传感器采集现场的数据,并以接力的方式通过其它传感器节点将采集到的数据逐跳地发送到汇聚节点(Sink)。
由于无线传感器网络节点资源受限,如何通过拓扑管理、能量有效的路由协议、功率控制等方面来延长网络的生存时间是无线传感器网络的核心研究课题。无线传感器网络路由协议为节点数据的传输指明路径,它影响和决定了整个无线传感器网络的性能和寿命。传输路径上的中间节点通过数据融合技术对来自不同节点的数据进行处理,从而减少传感器节点间数据通信,是节省传感器节点能量开销,延长网络寿命的有效技术手段之一。论文主要研究利用数据融合技术解决无线传感器网络由于能量限制导致的生存性问题,并探讨基于数据融合的无线传感器网络路由协议在网络生存时间方面的性能。
在无线传感器网络中,影响路由的因素包括业务类型、拓扑结构、节点处理能力等各个方面,针对已有的无线传感器网络路由算法极少考虑多种干扰因素的问题,又考虑到几何路由算法是对数据融合支撑性最好的一类路由算法,论文提出了一种基于势能场的几何路由算法,通过距离势能场把二维的无线传感器网络转化到三维空间讨论,并且通过场的概念,使无线传感器网络路由算法可以通过引入不同的势能场的叠加来解决多个因素对路由的影响。仿真分析表明,此算法可以有效地处理多种因素对路由效果的影响,并且能快速地收敛。
论文在引入能量消耗模型的基础上,研究数据融合与路由对网络生存时间的影响。由推导得出,基于数据融合与路由的联合优化,网络生存时间的最大化问题可转化为一个不可微的最优化问题。引入瓶颈节点的概念,利用光滑函数近似的方法,把该最优化问题转化为二阶函数的极大值问题,并得出了有解的充分条件。利用此充分条件和几何路由协议的思想,论文提出了一种分布式路由算法并进行了仿真分析。结果显示,此算法能显著减少数据流量和增大网络生存时间,同时能有效快速的收敛。
Wireless Sensor Network (WSN) is a multi-hop ad hoc network which consists of a large number of spatially distributed autonomous sensors to monitor physical or environmental conditions. Ordinary sensor nodes can carry many types of sensors for field data collection, and send collected data to the sink node hop by hop.
Since wireless sensor network node is resource-constrained, how we utilize the technology of topology management, energy efficient routing protocols and power control to extend the network lifetime is the core of the wireless sensor network research. Wireless sensor network routing protocol specifies path for the data transmission, which influences and determines the performance and lifetime of wireless sensor network. Intermediate nodes on the transmission path can reduce the data communication among sensor nodes through data aggregation, thereby extending the network lifetime. The thesis makes use of data aggregation technology to solve the survival isssues caused by energy constraints of wireless sensor network, and explores the network’s lifetime performance with the data aggregation-based routing protocol. In wireless networks, the factors which impact routing are service type, topology, node process ability and other aspects. The existing routing algorithms for wireless sensor networks rarely consider a variety of interfering factors, and taking account of that the geometric routing is the best support for the data aggregation in routing protocols, the thesis proposes a potential field-based geometric routing algorithm, transforms the two-dimensional wereless sensor network into three-dimensional network through distance potential field, and addresses the impact of multiple factors on the route by the superposition of different potential fields. Simulation analysis shows that this algorithm can effectively handle a variety of factors which affect the routing path. And this algorithm has a quick convergence.
The thesis introduces a energy consumption model to study the influence of the network lifetime by the data aggregation and routing protocol. Obtained by the derivation, network lifetime maximization problem can be transformed into a non-differential optimization problem based on jointly optimizing data aggregation and routing. The thesis also introduces the concept of bottleneck nodes and uses a smooth function approximation method to transform the optimization problem into the maximum value problem of second-order function, and also obtains sufficient conditions when the function is solvable. The thesis proposes a distributed routing algorithm based on the sufficient conditions and geometric routing protocols. Simulation analysis shows that this algorithm can handle a variety of factors which affect the routing path. And this algorithm has a quick convergence.
由于无线传感器网络节点资源受限,如何通过拓扑管理、能量有效的路由协议、功率控制等方面来延长网络的生存时间是无线传感器网络的核心研究课题。无线传感器网络路由协议为节点数据的传输指明路径,它影响和决定了整个无线传感器网络的性能和寿命。传输路径上的中间节点通过数据融合技术对来自不同节点的数据进行处理,从而减少传感器节点间数据通信,是节省传感器节点能量开销,延长网络寿命的有效技术手段之一。论文主要研究利用数据融合技术解决无线传感器网络由于能量限制导致的生存性问题,并探讨基于数据融合的无线传感器网络路由协议在网络生存时间方面的性能。
在无线传感器网络中,影响路由的因素包括业务类型、拓扑结构、节点处理能力等各个方面,针对已有的无线传感器网络路由算法极少考虑多种干扰因素的问题,又考虑到几何路由算法是对数据融合支撑性最好的一类路由算法,论文提出了一种基于势能场的几何路由算法,通过距离势能场把二维的无线传感器网络转化到三维空间讨论,并且通过场的概念,使无线传感器网络路由算法可以通过引入不同的势能场的叠加来解决多个因素对路由的影响。仿真分析表明,此算法可以有效地处理多种因素对路由效果的影响,并且能快速地收敛。
论文在引入能量消耗模型的基础上,研究数据融合与路由对网络生存时间的影响。由推导得出,基于数据融合与路由的联合优化,网络生存时间的最大化问题可转化为一个不可微的最优化问题。引入瓶颈节点的概念,利用光滑函数近似的方法,把该最优化问题转化为二阶函数的极大值问题,并得出了有解的充分条件。利用此充分条件和几何路由协议的思想,论文提出了一种分布式路由算法并进行了仿真分析。结果显示,此算法能显著减少数据流量和增大网络生存时间,同时能有效快速的收敛。
Wireless Sensor Network (WSN) is a multi-hop ad hoc network which consists of a large number of spatially distributed autonomous sensors to monitor physical or environmental conditions. Ordinary sensor nodes can carry many types of sensors for field data collection, and send collected data to the sink node hop by hop.
Since wireless sensor network node is resource-constrained, how we utilize the technology of topology management, energy efficient routing protocols and power control to extend the network lifetime is the core of the wireless sensor network research. Wireless sensor network routing protocol specifies path for the data transmission, which influences and determines the performance and lifetime of wireless sensor network. Intermediate nodes on the transmission path can reduce the data communication among sensor nodes through data aggregation, thereby extending the network lifetime. The thesis makes use of data aggregation technology to solve the survival isssues caused by energy constraints of wireless sensor network, and explores the network’s lifetime performance with the data aggregation-based routing protocol. In wireless networks, the factors which impact routing are service type, topology, node process ability and other aspects. The existing routing algorithms for wireless sensor networks rarely consider a variety of interfering factors, and taking account of that the geometric routing is the best support for the data aggregation in routing protocols, the thesis proposes a potential field-based geometric routing algorithm, transforms the two-dimensional wereless sensor network into three-dimensional network through distance potential field, and addresses the impact of multiple factors on the route by the superposition of different potential fields. Simulation analysis shows that this algorithm can effectively handle a variety of factors which affect the routing path. And this algorithm has a quick convergence.
The thesis introduces a energy consumption model to study the influence of the network lifetime by the data aggregation and routing protocol. Obtained by the derivation, network lifetime maximization problem can be transformed into a non-differential optimization problem based on jointly optimizing data aggregation and routing. The thesis also introduces the concept of bottleneck nodes and uses a smooth function approximation method to transform the optimization problem into the maximum value problem of second-order function, and also obtains sufficient conditions when the function is solvable. The thesis proposes a distributed routing algorithm based on the sufficient conditions and geometric routing protocols. Simulation analysis shows that this algorithm can handle a variety of factors which affect the routing path. And this algorithm has a quick convergence.
引文
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[2] C.Q Hua,T.S.P. Yum.Optimal routing and data aggregation for maximizing lifetime of wireless sensor networks[J].Ieee-Acm Transactions on Networking,2008,16(4):892-903.
[3]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.
[4]于弘毅,李鸥,张效义等.无线传感器网络理论、技术与实现[M].北京,国防工业出版社,2008.
[5] Bloomberg.21 ideas for the 21st century[M].Business Week,1999:78-167.
[6]孙其博,刘杰,黎羴等.物联网:概念、架构与关键技术研究综述[J].北京邮电大学学报,2010,33(3):1-9.
[7] Deepak Ganesan,Alberto Corpa,Wei Ye,etl.Networking issues in wireless sensor networks[J].Journal of Parallel and Distributed Computing,2004,64(7):799-814.
[8] Al-Karaki,R.Ul-Mustafa,A.E Kamal.Data aggregation and routing in Wireless Sensor Networks Optimal and heuristic algorithms.Computer Networks,2009.,53(7):945-960.
[9] Hua,T.S.P Yum.Maximum lifetime routing and data aggregation for wireless sensor networks[J].Mobile and Wireless Communications Systems,,2006,3976:840-855.
[10] Intanagonwiwat.Directed diffusion for wireless sensor networking[J].Ieee-Acm Transactions on Networking,2003,11(1):2-16.
[11] W.R. Heinzelman,A.Chandrakasan,H.Balakrishnan.Energy-Efficient Communication Protocol for Wireless Micro sensor Networks[J].Proceedings of IEEE HICSS,2000,16(5):97-102.
[12] Soro,W.B Heinzelman.Cluster head election techniques for coverage preservation in wireless sensor networks[J].Ad Hoc Networks,2009,7(5):955-972.
[13] Holger Karl,Andreas Willing.无线传感器网络协议与体系结构[M].北京,电子工业出版社,2007.
[14] J.Y Teo,Y.J Ha, C.K Tham.Interference-Minimized Multipath Routing with congestion control in wireless sensor network for high-rate streaming[J].Ieee Transactions on Mobile Computing,,2008,7(9):1124-1137.
[15] N.Ye,R.C. Wang,L.J. Sun.A routing scheme for data aggregation based on ant-like agent in wireless sensor networks[J].Chinese Journal of Electronics,2007,16(3):449-453.
[16] Bhardwaj,Garnett T,Chandrakasan. Upper bounds on the lifetime of sensor networks. In:IEEE International Conference on Communications (ICC’01),Helsinki,Finland,2001:785-790.
[17] Ekram Hossain,Kin K.Leung.Wireless Mesh Networks Architectures and Protocols[M],机械工业出版社,2009.
[18] Villalba.Routing Protocols in Wireless Sensor Networks[J].Sensors,2009,9(11):8399-8421.
[19] Hur.An Efficient Addressing Scheme and Its Routing Algorithm for a Large-Scale Wireless Sensor Network[J]. Eurasip Journal on Wireless Communications and Networking,2008,8(3),751-755.
[20]张擎.无线传感器网络分簇路由算法研究[D].山东大学,2008.
[21]于继民.无线传感器网络基于分簇的多路径路由算法研究[D].南京理工大学,2008.
[22]克莱因.多传感器数据融合理论及应用[M].北京,北京理工大学出版社,2004.
[23] K. Kalpakis, K. Dasgupta, P. Namjoshi.Maximum lifetime datagathering and aggregation in wireless sensor networks[J].ICN,. 2002, 12(5):685–696.
[24] C.Chen,O.L.Mangasarian.Smoothing methods for convex in-equalities and linear complementarity problems[J].Math.Program.,1995,71(1):1–69.
[25] Y.F Wen,F.Y.S Lin.Cross-layer duty cycle scheduling with data aggregation routing in wireless sensor networks[J].Embedded and Ubiquitous Computing Proceedings,2006,40(96):894-903.
[26] K.Wu.Delay-Constrained Optimal Data Aggregation in Hierarchical Wireless Sensor Networks[J].Mobile Networks & Applications,2009,14(5):571-589.
[27] R.G Bai.Cross-Layer Protocol Combining Tree Routing and TDMA Slotting in Wireless Sensor Networks[J].Ieice Transactions on Information and Systems,2009,92(10):1905-1914.
[28]杨树森.大规模无线传感器网络分簇路由协议设计与仿真[D].电子科技大学,2008.
[29] H.H Chen,Y.Yang.Network coverage and routing schemes for wireless sensor networks[J]. Computer Communications,2007,30(14-15):2697-2698.
[30] Kannan,S.S. Iyengar.Game-theoretic models for reliable path-length and energy-constrained routing with data aggregation in wireless sensor networks[J]. Ieee Journal on Selected Areas in Communications,2004,22(6):1141-1150.
[31] C.B. Abbas.A proposal of a wireless sensor network routing protocol[J].Telecommunication Systems,2008,38(1-2):61-68.
[32] Shokrzadeh,A.T. Haghighat,A. Nayebi.New routing framework base on rumor routing in wireless sensor networks[J].Computer Communications,2009,32(1):86-93.
[33] Figueiredo,E.F. Nakamura,A.A.F. Loureiro.A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks[J].Sensors,2009,9(9):7287-7307.
[34]俞黎阳.无线传感器网络网格状分簇路由协议和数据融合算法的研究[D].华东师范大学,2007.
[35] H.H. Yen.Optimization-Based Channel Constrained Data Aggregation Routing Algorithms in Multi-Radio Wireless Sensor Networks[J].Sensors,2009,9(6):4766-4788.
[36] Y.P. Kim,E. Jung,,Y.J. Park.A Radio-Aware Routing Algorithm for Reliable Directed Diffusion in Lossy Wireless Sensor Networks[J].Sensors,2009,9(10):8047-8072.
[37] Y.C Kuo,J.W. Chen.A Power-Saving Data Aggregation Algorithm for Byzantine Faults in Wireless Sensor Networks[J].Ieice Transactions on Communications,2009,92(6): 2201-2208.
[38] M.Laifenfeld.Joint Monitoring and Routing in Wireless Sensor Networks Using Robust Identifying Codes[J].Mobile Networks & Applications,2009,14(4):415-432.
[39] F.Yu.Hole Modeling and Detour Scheme for Geographic Routing in Wireless Sensor Networks[J].Journal of Communications and Networks,2009,11(4):327-336.
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