无线传感器网络拥塞控制研究
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摘要
无线传感器网络(WSN, wireless sensor network)节点的计算能力、能量、通信能力都十分有限,而且WSN固有的多到一、多跳通信的模式使得WSN经常发生网络拥塞。对网络拥塞进行有效控制是节省WSN网络能量、改善网络QoS性能的关键手段。尽管在WSN拥塞控制方面已有一些研究成果,但是绝大多数成果都是基于简单的拥塞反馈和速率调节机制的,拥塞控制操作往往能耗过高,拥塞控制机制本身也都很少考虑网络传输的公平性和实时性等重要的网络性能指标。本文致力于WSN拥塞控制中的两个基本课题:WSN拥塞产生原因的研究、拥塞控制技术的研究,主要的研究内容具体如下:
(1)本文使用了“拥塞阈值”的概念,拥塞阈值是网络节点分组产生率的临界值,超过此值,网络节点的缓存就要溢出而发生拥塞,拥塞阈值的概念把WSN传输能力的分析和拥塞状态的分析联系在一起,拥塞阈值同时表示了WSN最大的传输能力和抵御网络拥塞的最大能力。通过引入“介数”的定义对网络节点负载进行描述,在理论上对拥塞阈值进行了研究,并给出其渐进意义下的上界。在对最大传输能力进行分析的过程中可以看出,无线信道的干扰、网络负载的分布、网络规模等严重影响了网络最大传输能力的上界,同时对如何设计有效的拥塞控制机制提供了启示。
(2)设计了一个基于速率调节的拥塞控制机制RbCC。其中,提出了一种能有效检测WSN拥塞的能量有效性机制—基于缓存速率变化率的拥塞检测;同时提出了一个新的双向扩展拥塞反馈机制,令拥塞节点局部上、下游节点能同时参与拥塞控制以更有效地的控制拥塞并能快速地向相关的源节点反馈拥塞。RbCC还包含了一个局部速率调节算法和基于网络应用准确性的闭环速率调节算法,使源速率能较快地收敛到一个稳定的水平,保持了网络吞吐量的稳定性,同时降低了网络分组丢弃的数量并改善了网络传输的公平性。
(3)基于速率调节的拥塞控制比较适合流数据的WSN应用,但很难适应对实时性和可靠性要求较高的WSN应用。本文设计了一个基于发送窗口分配的拥塞控制机制WbCC。WbCC不用检测节点的拥塞与否,它要求上一跳节点只有在下一跳节点具有可用缓存空间时才向其发送数据。为了保证网络实时性的要求和传输公平性的要求,本文根据分组产生的时间设计了一个简单有效的面向实时性的队列调度机制和一个发送窗口分配策略,它不需要为每个源节点维护流的信息并且只需执行简单的计算就能保证网络中早产生的分组被早发送到sink。
(4)采用拥塞控制机制不是缓解和避免WSN拥塞的唯一手段。针对分组产生率低、网络应用准确性要求不高、偶尔出现短暂突发数据流的WSN应用,提出了一个源流量控制算法STC,根据监测到同一事件的不同源节点的剩余能量水平对这些节点的源流量负载进行分配,使其满足网络应用准确性要求,并且通过均衡使用相关源节点的能量来优化网络生命周期。分组的传输消耗了WSN节点的大部分能量,本文在路由过程中考虑了拥塞控制问题,提出了一个具有拥塞感知特点的节能路由算法CsEeR。借鉴前面对WSN传输能力上界分析的结论,CsEeR以均衡网络负载为初衷,并充分考虑无线通信环境的干扰和剩余能量水平对单个节点传输能力的影响,CsEeR基本能避免网络拥塞的发生,并有效地延长了网络生命周期。
The nodes in WSN(Wireless Sensor Networks) have low computation capacity, con-strained energy and weak communication ability. WSN often experiences congestion dueto these constraints on resources and the communication with the many-to-one and multi-hop pattern. In WSN applications, effective congestion control is the key technique toprolong the network system lifetime and improve the network QoS performances. Al-though some work related to congestion control in WSN have been proposed, they oftenconsume more energy and can not guarantee some QoS metrics, such as transport fairnessand real time. This dissertation devotes to two fundamental problems in congestion issueof WSN, congestion causes and congestion control techniques. To be more specific,
(1) This dissertation introduces the concept of congestion threshold to describe thecritical phase of packet generation rate, beyond which some nodes will experience bufferover?ow, leading to network congestion. Congestion threshold connects the transport ca-pacity of WSN and the congestion of WSN, and represents both the maximal transportcapacity of WSN and the maximal ability of resisting congestion. By using betweennessto describe the traffic load per node, the author studies the congestion threshold of WSNand presents the asymptotically upper bound, and discusses the impacts of wireless en-vironment, traffic load, and network scale on the transport capacity of WSN, providingsome insights for the designing of congestion control in WSN.
(2) The author systematically designs a rate-based congestion control scheme forWSN, called RbCC. In RbCC, an energy-efficient congestion detection method is pre-sented, which can effectively detect congestion by referring the buffer change behavior.RbCC also involves a bi-directional congestion feedback to faster transfer the local con-gestion information toward the correlated source nodes. RbCC makes the source ratefaster converge into a stable level by a local rate adjustment scheme and a closed-loopsink-source rate adjustment, decreasing the number of dropped packets and improvingthe transport fairness.
(3) The rate-based congestion control is more suitable to the WSN application withstreaming data, and not suitable to the applications requiring highly the real-time andreliable performance. The author designs a window-based congestion control scheme WbCC, which lets the upstream nodes send packets in only the case that the related down-stream nodes have some available buffer spaces, instead of detecting node congestion. Forguaranteeing the real time and transport fairness, WbCC involves a simple but effectivereal-time-oriented queue schedule method and a sending window assigning method. InWbCC, the ?ow information corresponding to each source node is not necessary to bemaintained, and only low computation load is needed to guarantee that the packet earlygenerated can reach the sink early.
(4) The author investigates a general WSN applications in which the packet gener-ation is very low, the application fidelity is small and transient sudden source load occursoccasionally. He presents an algorithm STC to control the source traffic by referring toboth the diversity of remained energy of different source nodes and the fidelity require-ment of WSN application. STC improves the system lifetime. In addition, the authorconsiders the congestion control issue in the routing. He designs a congestion-sensitiveand energy-efficient algorithm CsEeR. With the above analyzing results of the transportcapacity of WSN, CsEeR aims at balancing the energy of network nodes and considersfully the impact of wireless interference and the energy level on the single node. CsEeRalmost avoids the congestion and prolongs effectively the system lifetime.
(1)本文使用了“拥塞阈值”的概念,拥塞阈值是网络节点分组产生率的临界值,超过此值,网络节点的缓存就要溢出而发生拥塞,拥塞阈值的概念把WSN传输能力的分析和拥塞状态的分析联系在一起,拥塞阈值同时表示了WSN最大的传输能力和抵御网络拥塞的最大能力。通过引入“介数”的定义对网络节点负载进行描述,在理论上对拥塞阈值进行了研究,并给出其渐进意义下的上界。在对最大传输能力进行分析的过程中可以看出,无线信道的干扰、网络负载的分布、网络规模等严重影响了网络最大传输能力的上界,同时对如何设计有效的拥塞控制机制提供了启示。
(2)设计了一个基于速率调节的拥塞控制机制RbCC。其中,提出了一种能有效检测WSN拥塞的能量有效性机制—基于缓存速率变化率的拥塞检测;同时提出了一个新的双向扩展拥塞反馈机制,令拥塞节点局部上、下游节点能同时参与拥塞控制以更有效地的控制拥塞并能快速地向相关的源节点反馈拥塞。RbCC还包含了一个局部速率调节算法和基于网络应用准确性的闭环速率调节算法,使源速率能较快地收敛到一个稳定的水平,保持了网络吞吐量的稳定性,同时降低了网络分组丢弃的数量并改善了网络传输的公平性。
(3)基于速率调节的拥塞控制比较适合流数据的WSN应用,但很难适应对实时性和可靠性要求较高的WSN应用。本文设计了一个基于发送窗口分配的拥塞控制机制WbCC。WbCC不用检测节点的拥塞与否,它要求上一跳节点只有在下一跳节点具有可用缓存空间时才向其发送数据。为了保证网络实时性的要求和传输公平性的要求,本文根据分组产生的时间设计了一个简单有效的面向实时性的队列调度机制和一个发送窗口分配策略,它不需要为每个源节点维护流的信息并且只需执行简单的计算就能保证网络中早产生的分组被早发送到sink。
(4)采用拥塞控制机制不是缓解和避免WSN拥塞的唯一手段。针对分组产生率低、网络应用准确性要求不高、偶尔出现短暂突发数据流的WSN应用,提出了一个源流量控制算法STC,根据监测到同一事件的不同源节点的剩余能量水平对这些节点的源流量负载进行分配,使其满足网络应用准确性要求,并且通过均衡使用相关源节点的能量来优化网络生命周期。分组的传输消耗了WSN节点的大部分能量,本文在路由过程中考虑了拥塞控制问题,提出了一个具有拥塞感知特点的节能路由算法CsEeR。借鉴前面对WSN传输能力上界分析的结论,CsEeR以均衡网络负载为初衷,并充分考虑无线通信环境的干扰和剩余能量水平对单个节点传输能力的影响,CsEeR基本能避免网络拥塞的发生,并有效地延长了网络生命周期。
The nodes in WSN(Wireless Sensor Networks) have low computation capacity, con-strained energy and weak communication ability. WSN often experiences congestion dueto these constraints on resources and the communication with the many-to-one and multi-hop pattern. In WSN applications, effective congestion control is the key technique toprolong the network system lifetime and improve the network QoS performances. Al-though some work related to congestion control in WSN have been proposed, they oftenconsume more energy and can not guarantee some QoS metrics, such as transport fairnessand real time. This dissertation devotes to two fundamental problems in congestion issueof WSN, congestion causes and congestion control techniques. To be more specific,
(1) This dissertation introduces the concept of congestion threshold to describe thecritical phase of packet generation rate, beyond which some nodes will experience bufferover?ow, leading to network congestion. Congestion threshold connects the transport ca-pacity of WSN and the congestion of WSN, and represents both the maximal transportcapacity of WSN and the maximal ability of resisting congestion. By using betweennessto describe the traffic load per node, the author studies the congestion threshold of WSNand presents the asymptotically upper bound, and discusses the impacts of wireless en-vironment, traffic load, and network scale on the transport capacity of WSN, providingsome insights for the designing of congestion control in WSN.
(2) The author systematically designs a rate-based congestion control scheme forWSN, called RbCC. In RbCC, an energy-efficient congestion detection method is pre-sented, which can effectively detect congestion by referring the buffer change behavior.RbCC also involves a bi-directional congestion feedback to faster transfer the local con-gestion information toward the correlated source nodes. RbCC makes the source ratefaster converge into a stable level by a local rate adjustment scheme and a closed-loopsink-source rate adjustment, decreasing the number of dropped packets and improvingthe transport fairness.
(3) The rate-based congestion control is more suitable to the WSN application withstreaming data, and not suitable to the applications requiring highly the real-time andreliable performance. The author designs a window-based congestion control scheme WbCC, which lets the upstream nodes send packets in only the case that the related down-stream nodes have some available buffer spaces, instead of detecting node congestion. Forguaranteeing the real time and transport fairness, WbCC involves a simple but effectivereal-time-oriented queue schedule method and a sending window assigning method. InWbCC, the ?ow information corresponding to each source node is not necessary to bemaintained, and only low computation load is needed to guarantee that the packet earlygenerated can reach the sink early.
(4) The author investigates a general WSN applications in which the packet gener-ation is very low, the application fidelity is small and transient sudden source load occursoccasionally. He presents an algorithm STC to control the source traffic by referring toboth the diversity of remained energy of different source nodes and the fidelity require-ment of WSN application. STC improves the system lifetime. In addition, the authorconsiders the congestion control issue in the routing. He designs a congestion-sensitiveand energy-efficient algorithm CsEeR. With the above analyzing results of the transportcapacity of WSN, CsEeR aims at balancing the energy of network nodes and considersfully the impact of wireless interference and the energy level on the single node. CsEeRalmost avoids the congestion and prolongs effectively the system lifetime.
引文
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69 R. Albert, A. Barabasi. Statistical Mechanism of Complex Networks. Rev. Mod.Phy. 2002, 74:47–97
70 M. Bhardwaj, T. Garnett, A. P. Chandrakasan. Upper Bounds on the Liftetime ofSensor Networks. ICC. 2001:785–790
71 L. E. Li, P. Sinha. Throughput and Energy Efficiency in Topology-controlled Multi-hop Wireless Sensor Networks. Proceedings of WSNA. 2003:132–140
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73 T. V. Dam, K. Langendoen. An Adaptive Energy-efficient Mac Protocol for Wire-less Sensor Networks. SenSys. Los Angeles, USA: ACM, 2003:171–180
74 W. Ye, J. Heidemann, D. Estrin. Medium Access Control with Coordination Adap-tive Sleeping for Wireless Sensor Networks. IEEE/ACM Trans. on Networking.2004, 12:493–506
75龚海刚,余昌远,刘明,等. Seel:无线传感器网络下自适应、低延迟的节能媒质接入控制协议.计算机研究与发展. 2007, 44(11):1866–1872
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77 C. Wan. A Resilent Transport System for Wireless Sensor Networks. Ph.D. thesis,Columbia University. 2005
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80 J. Kang, Y. Zhang, B. Nath. Tara: Thermal-aware Routing Algorithm for ImplantedSensor Networks. IEEE Transactions on Parallel and Distributed Systems. 2007,18(7):919–931
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84曹峰,刘丽萍,王智.能量有效的无线移动传感器网络部署.信息与控制.2006, 13(2):147–153
85 M. Cardei, J. Wu. Energy-efficient Coverage Problems in Wireless Ad-hoc SensorNetworks. Computer Communications. 2006, 29:413–420
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102 J. H. Chang, L. Tassiulas. Maximum Lifetime Routing in Wireless Sensor Net-works. IEEE/ACM Trans. on Networking. 2004, 12(4):609–619
103 R. C. Shah, J. M. Rabaey. Energy Aware Routing for Low Energy Ad Hoc SensorNetworks. Wireless Communications and Networks Conference. Orlando, USA:IEEE, 2002, 1:350–355
104 X. Y. Hong, M. Gerla, H. Wang. Load Balanced, Energy-aware Communicationsfor Mars Sensor Networks. Aerospace Conference. Montana, USA, 2002, 3:1109–1115
105 S. C. Huang, R. H. Jan. Energy Aware, Load Balanced Routing Schemes for Sen-sor Networks. 10th International Conference on Parallel and Distributed System.Newport Beach, USA, 2004:419–425
106胡宁,张德运.无线传感器网络的能量平衡路由.西安交通大学学报. 2006,40(6):676–680
107张重庆,李明禄,伍民友.数据收集传感器网络的负载平衡网络构建方法.软件学报. 2007, 18(5):1110–1121
108黄刘生,李虹,徐宏力,等.无线传感器网络中基于负载平衡的多路路由.中国科技大学学报. 2006, 36(8):887–892
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110 M. A. Youssef, M. F. Younis, K. A. Arisha. A Constrained Shortest-path Energy-aware Routing Algorithm for Wireless Sensor Networks. Wireless Communica-tions and Networking Conference. Orlando, FL, USA, 2002, 2:794–799
111郑国强,李建东,周志立.无线传感器网络mac协议研究进展.自动化学报.2008