无线传感反应网络实时高效路由与任务分配机制的研究
|
摘要
无线传感反应网络通过引入具有丰富资源、甚至可以移动的反应节点,极大地增强了现有的无线传感器网络结构。在这种网络中反应节点可以参与传感节点的探测任务,但更重要的是接收和处理来自传感节点收集的信息,并执行与应用相关的操作。这种网络系统可被广泛地用于战场监视与反应、核生化攻击检测与响应、工业控制、家庭自动化及环境监控与响应等许多领域。
为了快速对外部环境做出响应,首先必须保证数据收集的实时性,其次还必须兼顾传感节点的能耗,以延长网络生存周期。此外由于网络中存在多个反应节点,还必须解决反应节点之间的任务分配问题。本文主要针对数据收集的实时性与能耗之问的平衡问题、任务分配问题进行深入的研究。
首先针对无线传感反应网络应用的实时需求和传感节点能量低的特性,研究了基于跳步数受限的最小能量路径HBMECP问题。通过设定源节点到目的反应节点的跳步数,在降低网络总能耗的同时保证数据收集仍然满足实时需求。接着将HBMECP问题表述为整数线性规划(ILP)问题。通过对节点能量消耗模型的分析,推导出理想条件下单条路径上总能耗最小时的最优跳步数,并且当每跳距离相等时单条路径上的总能耗达到最小。在此基础上,本文具体设计出了一个HBMECP的分布式近似算法。即每个源节点依据最优跳步数和设定的跳步数确定最终所需的跳步数,同时修改基于地理位置路由算法,使每跳的距离尽可能趋于一致,从而达到保证实时收集数据条件下降低网络总能耗的目的。理论分析和仿真实验表明,无论在SA模型还是在MA模型下,HBMECP算法能够有效地实现数据收集的实时性与网络总能耗之间的平衡,同时只需增加较小的开销,并且适用于反应节点移动的情形。
本文接着针对距离反应节点越近传感节点能量消耗越快的现象,研究了基于容量约束的最大跳步数最小化CBMMH问题。它通过设置节点的容量约束,限制节点每轮转发某个事件的最大报文数,以降低节点的最大能耗,达到延长网络生存时间的目的。同时从源节点到目的反应节点采用最小跳步数机制转发报文,以保证数据收集的实时性。随后将CBMMH问题用多目标规划问题表述,并在此基础上提出了两种CBMMH近似算法:GCBMMH算法和DCBMMH算法。仿真实验表明,GCBMMH算法和DCBMMH算法能够有效地实现数据收集的实时性与节点最大能耗之间的平衡,从而达到延长网络生存期的目的,而且DCBMMH算法不需要全局信息。
本文接下来讨论无线传感反应网络的任务分配问题。首先依据反应节点类型、任务类型和时间要求的不同,对任务分配问题进行了分类。接着研究了反应节点通信半径不受限条件下单反应节点任务SAT的分配机制,提出了集中式和分布式两种SAT分配机制。在这两种分配机制中,采用反应节点完成事件所需时间作为效能函数,作为选择执行任务的反应节点的依据。在集中式分配机制中,决策中心记录反应节点完成先前所分配任务的时间及位置信息,用于效能函数的计算。在分布式分配机制中,提出了三种策略用来确定执行任务的反应节点:竞拍策略、设定起拍价的竞拍策略、惰性策略。大量仿真实验表明:在反应节点通信半径不受限的条件下集中式分配算法在通信开销和时间延迟上具有较好的性能,而当反应节点数大于同步事件数时,设定起拍价的竞拍策略和惰性策略的分布式分配机制同样能够取得较好的性能。
本文随后研究了反应节点通信半径受限条件下SAT的分配机制,提出了半自动式和全自动式两种集中式SAT分配机制。在半自动式的分配机制中,传感节点与反应节点之间和反应节点相互之间均不需要协调机制。在全自动式的分配机制中,只需要传感节点与反应节点之间进行协调。在这两种分配机制中,由于反应节点通信半径受限,决策中心发送执行任务的命令需要通过传感节点转发,同时还需知道接收任务的节点的概略位置。为此,本文提出了利用决策中心存储的各个反应节点完成任务的时间及位置来预测接收节点的位置算法。仿真实验表明:预测接收节点的位置算法精度较高;半自动式分配机制在初始化过程中开销较低,并且在事件并发数较少情况下,在响应延迟上具有较好的性能;而全自动式分配机制在事件并发数较大时在响应延迟和完成任务方面具有较好的性能。
最后本文研究了多反应节点任务MAT的分配机制,提出了满足任务时限要求下的最佳反应节点集合的选择问题,其目标是在保证完成任务时限的前提下最小化反应节点的总能耗,同时平衡参与响应的反应节点的能耗。本文首先将这种问题阐述为混合整数非线性规划MINLP问题,并提出了两种分布式MAT分配机制:非抢占式和抢占式分配机制。在两种分配机制中,决策节点都是利用其它节点提交的响应事件的信息和自己的状态来确定响应当前事件的反应节点集。不同的是在非抢占式分配机制中,已分配的任务执行不可被推后或者中断,而在抢占式分配机制中,先前已分配的优先级低的任务执行,可被中断以完成优先级高的任务。大量仿真实验表明:算法中反应节点利用空闲时机移向所负责的区域的情形比不移动的情形有更高的任务完成率;非抢占式和抢占式分配机制在完成任务的成功率上均要高于忙节点不参与任务分配的机制;抢占式分配机制以牺牲优先级低的任务和能耗为代价,比非抢占式分配机制完成更多优先级高的任务。
Wireless sensor and actor networks, or WSANs, greatly enhance the existing wireless sensor network architecture by introducing powerful and even mobile actors. The actors work with the sensors, but can perform much richer application-specific actions. There are many potential applications of WSANs, such as battlefield surveillance, nuclear, biological and chemical attack detection, industrial control, home automation and environmental monitoring.
To act responsively, first, data must be forwarded to the actors in time; then, the energy consumption of sensors must be considered, so as to prolong the network lifetime; furthermore, for there are many actors in the networks, task assignment problem between the actors must be solved. This thesis focuses on the questions of the balance between real-time and energy consumption in the processing of data collection, and task allocation.
First, this thesis aims at the real-time requirement of WSANs applications and the low-power sensors, and studies a HBMECP (Hop-Bounded and Minimum Energy Cost Path) problem. Through limiting the number of hops from source nodes to actors, it hopes to reduce the total energy consumption in the network while guaranteeing that data collection still meets the real-time requirement simultaneously. The HBMECP problem is formulated as an integer linear programming (ILP). Through analyzing the model of energy consumption, the optimal number of hops under the ideal condition is inferred when the total energy consumption in a path can be minimized, and only when the distance of each hop is equal, the total energy consumption in the path reaches minimum. The thesis proposes a distributed approximate algorithm of the HBMECP problem. Based on the optimal number of hops and the given number of hops, each source node determines the number of hops respectively. Simultaneously the geographical routing algorithm is revised to ensure that the distance of each hop is equal as far as possible, so it achieves the goal of decreasing the total energy consumption while guaranteeing the real-time requirement of data collection. Theoretical analysis and extensive simulations demonstrate that, regardless of the single-actor model or multi-actor model, the proposed algorithm can effectively reach the balance between the real-time requirement of data collection and the total energy consumption of the network with trivial expenses, and adapt to the case of mobile actors.
This thesis then considers the question that the sensors near to actors consume energy quickly, and studies the CBMMH (Capacity-Bounded and MIN-MAX Hops) problem. By setting the capacity constraint of the sensors, it can limit the number of the retransmitting packets greatly for some event, and reduce the maximal energy consumption of single sensor, and prolong the network lifetime. Meanwhile it adopts the minimal number of hops to retransmit data packets from the source nodes to the actors, and guarantees the real-time request of data collection. Then the CBMMH problem is formulated as a multi-objective programming, and two kinds of approximate algorithms of the CBMMH problem are proposed: GCBMMH and DCBMMH. Simulations show that, GCBMMH and DCBMMH can effectively reach the balance between the real-time requirement of data collection and the maximal energy consumption of single sensor, thus achieve the goal of extending the network lifetime; the DCBMMH algorithm doesn't need the global information.
Next, this thesis discusses the task allocation problem in the wireless sensor and actor networks. First this thesis classifies the task allocation problem by different type of actors, different type of tasks and different time requirement. Then this thesis studies the Single-Actor Task (SAT) assignment with unlimited actor radius, and proposes two kinds of SAT assignment mechanism: central mechanism and distributed mechanism. Under these two mechanisms, the utility function of the time is adopted to determine the actor which the task will be assigned to when actors will finish a task. In the central assignment mechanism, the decision-making node records the information of the time and the position when actors finish the former tasks assigned, and determines the actor which the coming task will be assigned to. In the distributed assignment mechanism, there are three kinds of strategy for determining task assignment via the actor-actor coordination: auction strategy, auction strategy with starting price, idle strategy. Simulations indicate that, under the condition which the actor radius is unlimited, the central assignment algorithm has better performance in terms of the communication overhead and the delay, and when the number of actors is greater than the number of concurrent events, auction strategy with starting price and idle strategy in the distributed assignment mechanism can obtain a good performance too.
This thesis next studies the SAT assignment mechanism under the condition of limited actor radius, and proposes two kinds of central SAT assignment mechanisms: semi-automated and automated. In the central mechanism, there are no sensor-actor coordination and actor-actor coordination. While in the distributed mechanism, there exists the sensor-actor coordination. Because the radius of the actors is limited, the decision-making node sends the task command to the selected actor via the sensors, so it must know the approximate position of the actor. Therefore, this thesis proposes an algorithm forecasting the position of the selected actor in the decision-making node, with the information of the time and the position when the actor finishes the tasks assigned before. Simulations show that the forecasting algorithm has a high precision, and that Semi-automated assignment mechanism which has a low initialization communication overhead has a lower delay under the condition of the small number of concurrent events, and that the automated assignment mechanism has a better performance at the delay and the number of the finished tasks when the number of concurrent event is greater.
Finally this thesis studies the Multi-Actor Task (MAT) assignment mechanism, and proposes the problem of actors' selection under the condition of satisfying the time-constraint of tasks. The goal of the problem is to minimize the total energy consumption of the actors while at the same time balancing the energy consumption among the actors. The thesis formulates the problem as Mix Integer Non-Linear Program (MINLP), and proposes two kinds of MAT assignment mechanisms: non-preemption and preemption. Under the assignment mechanisms, decision-making node determines the actors for responding to current task according to known information. In the non-preemption mechanism, the assigned task isn't deferred or interrupted, however it may be interrupted by a task with higher priority in the preemption mechanism. Simulations show that for the above algorithms it has higher task success rate in the circumstance of idle actors moving to own region than in the circumstance of idle actors having no motion, and that the task success rate of the mechanism for only idle actors participating in task assignment is lower than either of the non-preemption and preemption mechanisms', and that for the high-priority task preemption mechanism than non-preemption mechanism has higher success rate, at the price of reducing the success rate of low-priority task and increasing energy consumption.
为了快速对外部环境做出响应,首先必须保证数据收集的实时性,其次还必须兼顾传感节点的能耗,以延长网络生存周期。此外由于网络中存在多个反应节点,还必须解决反应节点之间的任务分配问题。本文主要针对数据收集的实时性与能耗之问的平衡问题、任务分配问题进行深入的研究。
首先针对无线传感反应网络应用的实时需求和传感节点能量低的特性,研究了基于跳步数受限的最小能量路径HBMECP问题。通过设定源节点到目的反应节点的跳步数,在降低网络总能耗的同时保证数据收集仍然满足实时需求。接着将HBMECP问题表述为整数线性规划(ILP)问题。通过对节点能量消耗模型的分析,推导出理想条件下单条路径上总能耗最小时的最优跳步数,并且当每跳距离相等时单条路径上的总能耗达到最小。在此基础上,本文具体设计出了一个HBMECP的分布式近似算法。即每个源节点依据最优跳步数和设定的跳步数确定最终所需的跳步数,同时修改基于地理位置路由算法,使每跳的距离尽可能趋于一致,从而达到保证实时收集数据条件下降低网络总能耗的目的。理论分析和仿真实验表明,无论在SA模型还是在MA模型下,HBMECP算法能够有效地实现数据收集的实时性与网络总能耗之间的平衡,同时只需增加较小的开销,并且适用于反应节点移动的情形。
本文接着针对距离反应节点越近传感节点能量消耗越快的现象,研究了基于容量约束的最大跳步数最小化CBMMH问题。它通过设置节点的容量约束,限制节点每轮转发某个事件的最大报文数,以降低节点的最大能耗,达到延长网络生存时间的目的。同时从源节点到目的反应节点采用最小跳步数机制转发报文,以保证数据收集的实时性。随后将CBMMH问题用多目标规划问题表述,并在此基础上提出了两种CBMMH近似算法:GCBMMH算法和DCBMMH算法。仿真实验表明,GCBMMH算法和DCBMMH算法能够有效地实现数据收集的实时性与节点最大能耗之间的平衡,从而达到延长网络生存期的目的,而且DCBMMH算法不需要全局信息。
本文接下来讨论无线传感反应网络的任务分配问题。首先依据反应节点类型、任务类型和时间要求的不同,对任务分配问题进行了分类。接着研究了反应节点通信半径不受限条件下单反应节点任务SAT的分配机制,提出了集中式和分布式两种SAT分配机制。在这两种分配机制中,采用反应节点完成事件所需时间作为效能函数,作为选择执行任务的反应节点的依据。在集中式分配机制中,决策中心记录反应节点完成先前所分配任务的时间及位置信息,用于效能函数的计算。在分布式分配机制中,提出了三种策略用来确定执行任务的反应节点:竞拍策略、设定起拍价的竞拍策略、惰性策略。大量仿真实验表明:在反应节点通信半径不受限的条件下集中式分配算法在通信开销和时间延迟上具有较好的性能,而当反应节点数大于同步事件数时,设定起拍价的竞拍策略和惰性策略的分布式分配机制同样能够取得较好的性能。
本文随后研究了反应节点通信半径受限条件下SAT的分配机制,提出了半自动式和全自动式两种集中式SAT分配机制。在半自动式的分配机制中,传感节点与反应节点之间和反应节点相互之间均不需要协调机制。在全自动式的分配机制中,只需要传感节点与反应节点之间进行协调。在这两种分配机制中,由于反应节点通信半径受限,决策中心发送执行任务的命令需要通过传感节点转发,同时还需知道接收任务的节点的概略位置。为此,本文提出了利用决策中心存储的各个反应节点完成任务的时间及位置来预测接收节点的位置算法。仿真实验表明:预测接收节点的位置算法精度较高;半自动式分配机制在初始化过程中开销较低,并且在事件并发数较少情况下,在响应延迟上具有较好的性能;而全自动式分配机制在事件并发数较大时在响应延迟和完成任务方面具有较好的性能。
最后本文研究了多反应节点任务MAT的分配机制,提出了满足任务时限要求下的最佳反应节点集合的选择问题,其目标是在保证完成任务时限的前提下最小化反应节点的总能耗,同时平衡参与响应的反应节点的能耗。本文首先将这种问题阐述为混合整数非线性规划MINLP问题,并提出了两种分布式MAT分配机制:非抢占式和抢占式分配机制。在两种分配机制中,决策节点都是利用其它节点提交的响应事件的信息和自己的状态来确定响应当前事件的反应节点集。不同的是在非抢占式分配机制中,已分配的任务执行不可被推后或者中断,而在抢占式分配机制中,先前已分配的优先级低的任务执行,可被中断以完成优先级高的任务。大量仿真实验表明:算法中反应节点利用空闲时机移向所负责的区域的情形比不移动的情形有更高的任务完成率;非抢占式和抢占式分配机制在完成任务的成功率上均要高于忙节点不参与任务分配的机制;抢占式分配机制以牺牲优先级低的任务和能耗为代价,比非抢占式分配机制完成更多优先级高的任务。
Wireless sensor and actor networks, or WSANs, greatly enhance the existing wireless sensor network architecture by introducing powerful and even mobile actors. The actors work with the sensors, but can perform much richer application-specific actions. There are many potential applications of WSANs, such as battlefield surveillance, nuclear, biological and chemical attack detection, industrial control, home automation and environmental monitoring.
To act responsively, first, data must be forwarded to the actors in time; then, the energy consumption of sensors must be considered, so as to prolong the network lifetime; furthermore, for there are many actors in the networks, task assignment problem between the actors must be solved. This thesis focuses on the questions of the balance between real-time and energy consumption in the processing of data collection, and task allocation.
First, this thesis aims at the real-time requirement of WSANs applications and the low-power sensors, and studies a HBMECP (Hop-Bounded and Minimum Energy Cost Path) problem. Through limiting the number of hops from source nodes to actors, it hopes to reduce the total energy consumption in the network while guaranteeing that data collection still meets the real-time requirement simultaneously. The HBMECP problem is formulated as an integer linear programming (ILP). Through analyzing the model of energy consumption, the optimal number of hops under the ideal condition is inferred when the total energy consumption in a path can be minimized, and only when the distance of each hop is equal, the total energy consumption in the path reaches minimum. The thesis proposes a distributed approximate algorithm of the HBMECP problem. Based on the optimal number of hops and the given number of hops, each source node determines the number of hops respectively. Simultaneously the geographical routing algorithm is revised to ensure that the distance of each hop is equal as far as possible, so it achieves the goal of decreasing the total energy consumption while guaranteeing the real-time requirement of data collection. Theoretical analysis and extensive simulations demonstrate that, regardless of the single-actor model or multi-actor model, the proposed algorithm can effectively reach the balance between the real-time requirement of data collection and the total energy consumption of the network with trivial expenses, and adapt to the case of mobile actors.
This thesis then considers the question that the sensors near to actors consume energy quickly, and studies the CBMMH (Capacity-Bounded and MIN-MAX Hops) problem. By setting the capacity constraint of the sensors, it can limit the number of the retransmitting packets greatly for some event, and reduce the maximal energy consumption of single sensor, and prolong the network lifetime. Meanwhile it adopts the minimal number of hops to retransmit data packets from the source nodes to the actors, and guarantees the real-time request of data collection. Then the CBMMH problem is formulated as a multi-objective programming, and two kinds of approximate algorithms of the CBMMH problem are proposed: GCBMMH and DCBMMH. Simulations show that, GCBMMH and DCBMMH can effectively reach the balance between the real-time requirement of data collection and the maximal energy consumption of single sensor, thus achieve the goal of extending the network lifetime; the DCBMMH algorithm doesn't need the global information.
Next, this thesis discusses the task allocation problem in the wireless sensor and actor networks. First this thesis classifies the task allocation problem by different type of actors, different type of tasks and different time requirement. Then this thesis studies the Single-Actor Task (SAT) assignment with unlimited actor radius, and proposes two kinds of SAT assignment mechanism: central mechanism and distributed mechanism. Under these two mechanisms, the utility function of the time is adopted to determine the actor which the task will be assigned to when actors will finish a task. In the central assignment mechanism, the decision-making node records the information of the time and the position when actors finish the former tasks assigned, and determines the actor which the coming task will be assigned to. In the distributed assignment mechanism, there are three kinds of strategy for determining task assignment via the actor-actor coordination: auction strategy, auction strategy with starting price, idle strategy. Simulations indicate that, under the condition which the actor radius is unlimited, the central assignment algorithm has better performance in terms of the communication overhead and the delay, and when the number of actors is greater than the number of concurrent events, auction strategy with starting price and idle strategy in the distributed assignment mechanism can obtain a good performance too.
This thesis next studies the SAT assignment mechanism under the condition of limited actor radius, and proposes two kinds of central SAT assignment mechanisms: semi-automated and automated. In the central mechanism, there are no sensor-actor coordination and actor-actor coordination. While in the distributed mechanism, there exists the sensor-actor coordination. Because the radius of the actors is limited, the decision-making node sends the task command to the selected actor via the sensors, so it must know the approximate position of the actor. Therefore, this thesis proposes an algorithm forecasting the position of the selected actor in the decision-making node, with the information of the time and the position when the actor finishes the tasks assigned before. Simulations show that the forecasting algorithm has a high precision, and that Semi-automated assignment mechanism which has a low initialization communication overhead has a lower delay under the condition of the small number of concurrent events, and that the automated assignment mechanism has a better performance at the delay and the number of the finished tasks when the number of concurrent event is greater.
Finally this thesis studies the Multi-Actor Task (MAT) assignment mechanism, and proposes the problem of actors' selection under the condition of satisfying the time-constraint of tasks. The goal of the problem is to minimize the total energy consumption of the actors while at the same time balancing the energy consumption among the actors. The thesis formulates the problem as Mix Integer Non-Linear Program (MINLP), and proposes two kinds of MAT assignment mechanisms: non-preemption and preemption. Under the assignment mechanisms, decision-making node determines the actors for responding to current task according to known information. In the non-preemption mechanism, the assigned task isn't deferred or interrupted, however it may be interrupted by a task with higher priority in the preemption mechanism. Simulations show that for the above algorithms it has higher task success rate in the circumstance of idle actors moving to own region than in the circumstance of idle actors having no motion, and that the task success rate of the mechanism for only idle actors participating in task assignment is lower than either of the non-preemption and preemption mechanisms', and that for the high-priority task preemption mechanism than non-preemption mechanism has higher success rate, at the price of reducing the success rate of low-priority task and increasing energy consumption.
引文
[1]I.F.Akyildiz and I.H.Kasimoglu,Wireless Sensor and Actor Networks:Research Challenges,Ad Hoc Networks,Vol.2,Issue 4,pp.351-367,October 2004.
[2]I.F.Akyildiz,W.Su,Y.Sankarasubramaniam,E.Cayirci,Wireless Sensor Networks:A Survey,Computer Networks,2002,38(4):393-422.
[3]Edgar H.Callaway,Wireless Sensor Networks:Architectures and Protocols,CRC Press,2003.
[4]孙利民,李建中,陈渝,朱红松,无线传感器网络,清华大学出版社,2005.
[5]任丰原,黄海宁,林闯,无线传感器网络,软件学报,2003,14(7):1282-1291.
[6]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,计算机研究与发展,2005,1:163-174.
[7]彭伟,卢锡城,无线传感器网络及其典型应用,计算机世界,2004.10.27.
[8]http://research.cens.ucla.edu
[9]http://www.janet.ucla.edu/WINS/
[10]http://bwrc.eecs.berkeley.edu
[11]http://webs.cs.berkeley.edu/
[12]http://www robotics.usc.edu/～embedded/
[13]http://www.isi.edu/scadds/
[14]http://www.ece.gatech.edu/research/labs/bwn/index.html
[15]http://www.cast.cse.ohio state.edu/exscal/
[16]http://www.wings.cs.sunysb.edu/
[17]http://www.eecs.harvard.edu/～mdw/proj/codeblue/
[18]http://mantis.cs.colorado.edu/index.php/tiki-index.php
[19]http://www.eng.yale.edu/enalab/
[20]http://nms.csail.mit.edu/
[21]http://www-mtl.mit.edu/researchgroups/icsystems/uamps/
[22]http://lion.cs.uiuc.edu/
[23]http://proj ects.cerias.purdue.edu/esp/
[24]http://www.zurich.ibm.com/sys/communication/sensors.html
[25]http://www.intel.com/research/exploratory/wireless sensors.htm
[26]http://research.microsoft.com/nec/
[27]http://www.xbow.com/Products/Wireless Sensor Networks.htm
[28]http://bwrc.eecs.berkeley.edu/Research/Pico Radio/Default.htm
[29]http://nesl.ee.ucla.edu/projects/ahlos/
[30]http://www.intel.com/research/exploratory/motes.htm
[31]http://www.tinyos.net
[32]http://nesl.ee.ucla.edu/projects/sos/
[33]K.Chakrabarty,S.S.Iyengar,H.Qi,E.Cho,Grid coverage for Surveillance and Target Location in Distributed Sensor Networks,IEEE Transactions on Computers,2002,51(12):1448-1453.
[34]S.S.Dhillon,K.Chakrabarty,S.S.Iyengar,Sensor Placement for Grid Coverage under Imprecise Detections,Proceedings of International Conferrence on Infomation Fusion,2002.
[35] S. S. Dhillon, K. Chakrabarty, Sensor Placement for Effective Coverage and Surveillance in Distributed Sensor Networks, Proceedings of IEEE Wireless Communications and Networking Conference (WCNC'03), 1609-1614,2003.
[36] Y. Zou, K. Chakrabarty, Sensor Deployment and Target Localization Based on Virtual Forces, Proceedings of IEEE INFOCOM'03, 1293-1303, San Francisco, California, USA, 2003.
[37] Y. Zou, K. Chakrabarty, Sensor Deployment and Target Localization in Distributed Sensor Networks, ACM Transactions on Embedded Computing Systems, 2004, 3(1): 61-91.
[38] Y. Zou, Coverage-Centric Sensor Deployment and Energy-Efficient Information Processing in Wireless Sensor Networks, Phd thesis, Duke University, 2004.
[39] H. Takagi and L. Kleinrock, "Optimal Transmission Ranges for Randomly Distributed Packet Radio Terminals," IEEE Trans. Commun., vol. 32, no. 3,1984, pp. 246-57.
[40] T. Hou and V. Li, Transmission range control in multihop packet radio networks, IEEE Transactions on Communications 1 (1986) (34), pp. 38-44.
[41] E. Kranakis, H. Singh and J. Urrutia, Compass routing on geometric networks, Proceedings of the 11th Canadian Conference on Computational Geometry (1999), pp. 51-54.
[42] G. Finn. Routing and addressing problems in large metropolitan-scale internet works. Technical Report ISI/RR-87-180, USC/ISI, March 1987.
[43] I. Stojmenovic and Xu Lin, "Loop-free Hybrid Single-Path/Flooding Routing Algorithms with Guaranteed Delivery for Wireless Networks,"IEEE Trans. Parallel Dist. Sys., vol. 12, no. 10,2001, pp. 1023-32.
[44] Stojmenovic and Xu Lin, "Power-Aware Localized Routing in Wireless Networks," IEEE Trans. Parallel Dist. Sys., vol. 12, no. 11, 2001, pp.1122-33.
[45] L. Blazevic et al.,"Self-organization in Mobile Ad Hoc Networks: The Approach of Terminodes," IEEE Commun. Mag., June 2001, pp. 166-75.
[46] R. Jain, A. Puri, and R. Sengupta, "Geographical Routing Using Partial Information for Wireless Ad Hoc Networks," IEEE Pers. Commun., Feb.2001, pp. 48-57.
[47] P. Bose et al., "Routing with Guaranteed Delivery in Ad Hoc Wireless Networks," 3rd IntT. Wksp. Discrete Algorithms Methods Mobile Comp.Commun., Seattle, WA, Aug. 20, 1999, pp. 48-55; also in ACM/Kluwer WLNets., vol. 7, no. 6, Nov. 2001, pp. 609-16.
[48] Stojmenovic, M. Seddigh, and J. Zunic, "Dominating Sets and Neighbor Elimination Based Broadcasting Algorithms in Wireless Networks," IEEE Trans. Parallel Dist. Sys., vol. 13, no. 1, Jan. 2002, pp. 14-25.
[49] Brad Karp and H.T. Kung, "Gpsr: Greedy perimeter stateless routing for wireless networks," in Proc. of the ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom), 2000.
[50] Fabian Kuhn, Roger Wattenhofer, and Aaron Zollinger, "Asymptotically optimal geometric mobile ad-hoc routing," in Proceedings of the 6~(th) International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications (DIALM), 2002.
[51] Fabian Kuhn, Roger Wattenhofer, and Aaron Zollinger, "Worst-Case Optimal and Average-Case Efficient Geometric Ad-Hoc Routing" in Proc. 4 th ACM Int. Symposium on Mobile Ad-Hoc Networking and Computing(MobiHoc), 2003.
[52] F. Kuhn, R. Wattenhofer, Y. Zhang, and A. Zollinger. Geometric Routing: Of Theory and Practice. In Proc. of the 22 nd ACM Symposium on the Principles of Distributed Computing (PODC), 2003.
[53] C. Intanagonwiwat, R. Govindan, D. Estrin, Directed diffusion: a scalable and robust communication paradigm for sensor networks, Proceedings of the ACM MobiCom'00, Boston, MA, 2000, pp. 56 - 67.
[54] Rahul C. Shah, Jan M. Rabaey, Energy Aware Routing for Low Energy Ad Hoc Sensor Networks, IEEE Wireless Communications and Networking Conference (WCNC), Orlando, FL.2002.
[55] L. Li, and J. Y. Halpern, "Minimum-Energy Mobile Wireless Networks Revisited," ICC '01, Helsinki, Finland,June 2001.
[56] V. Rodoplu, T.H. Meng, Minimum energy mobile wireless networks, IEEE Journal of Selected Areas in Communications 17 (8) (1999) 1333-1344.
[57] CHEN Y, LIU A, ZHANG S. A scalable solution to minimum cost forwarding in large sensor networks[A]. Proceedings of Tenth international Conference on Computer Communications and Networks[C]. Scottsdale,USA, 2001:304-309.
[58] Sohrabi K, Gao J, Ailawadhi V, Pottie GJ. Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 2000,7(5):16-27.
[59] F. Ye, H. Luo, J. Cheng, S. Lu, L. Zhang, A two-tier data dissemination model for large-scale wireless sensor networks, in: Proceedings of the ACM Mobicom'02 Eighth Annual International Conference on Mobile computing and networking, ACM Press, 2002, pp. 148-159.
[60] Weilian Su, Ian F. Akyildiz, A Stream Enabled Routing (SER) Protocol for Sensor Networks, in "Med-hoc-Net 2002", Sardegna, Italy, September 2002.
[61] H.S. Kim T.F. Abdelzaher, W.H. Kwon, Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks, in: Proceedings of the First International Conference on Embedded Networked Sensor Systems(Sensys'03), pp. 193 - 204, November 2003.
[62] W. Hu, N. Bulusu, S. Jha, An anycast service for hybrid sensor/actuator networks, The University of New South Wales (UNSW) CSE Technical Report 0331, October 2003.
[63] M. Caccamo, Lynn Y. Zhang, Lui Sha, G. Buttazzo, an Implicit Prioritized Access Protocol for Wireless Sensor Networks, Proceedings of the 23rd IEEE REAL-TIME SYSTEMS SYMPOSIUM (RTSS'02).
[64] M. Caccamo, The Capacity of Implicit EDF in Wireless Sensor Networks.RealTime Systems, 2003 Proceedings, 15th Euromicro Conference'03, 2003.
[65] Chenyang Lu, Brian M. Blum, Tarek F. Abdelzaher, John A. Stankovic, Tian He, RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks, in RealTime and Embedded Technology and Applications Symposium, 2002. Proceedings Eighth IEEE, 2002
[66] K. Akkaya, M. Younis, An Energy-Aware QoS Routing Protocol for Wireless Sensor Networks, Proc. of the IEEE Workshop on Mobile and Wireless Networks (MWN 2003), Providence, RI, USA, May 2003.
[67] K. Akkaya, M. Younis, Energy and QoS aware routing in wireless sensor networks, Cluster Computing 8, 179-188, 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
[68] T. He, J. Stankovic, C. Lu, T. Abdelzaher, SPEED: A real-time routing protocol for sensor networks, in: Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS), Providence, Rhode Island, USA, pp. 46-55, May 2003.
[69] E. Felemban, C.G. Lee, E. Ekici, R. Boder, and S. Vural, Probabilistic QoS Guarantee in Reliability and Timeliness Domains in Wireless Sensor Networks, in Proceedings of IEEE INFOCOM 2005, Miami, FL, USA, Mar.2005.
[70] H. Kim, Y. Seok, N. Choi,Y. Choi, and T. Kwon, Optimal Multi-sink Positioning and Energy-efficient Routing in Wireless Sensor Networks, in ICOIN 2005, Jeju Island, Korea, January 31- February 2, 2005, Proceedings.
[71] R. Chandra, Lili Qiu, K. Jain, M. Mahdian, Optimizing the Placement of Internet TAPs in Wireless Neighborhood Networks, ICNP, Oct. 2004.
[72] Kevin Yuen, Baochun Li, Ben Liang, Distributed Data Gathering in Multi-Sink Sensor Networks with Correlated Sources, to appear in the Proceedings of IFIP Networking 2006, Coimbra, Portugal, May 15-19, 2006
[73] H. Dubois-Ferriere and D. Estrin, Efficient and Practical Query Scoping in Sensor Networks, Tech. Rep. 2004-39, CENS/UCLA Tech Report, 2004.
[74] Tommaso Melodia, Dario Pompili, Vehbi C. Gungor and Ian F. Akyildiz, A Distributed Coordination Framework for Wireless Sensor and Actor Networks, Proceedings of ACM MobiHoc'05, Urbana-Champaign, Illinois,USA, 2005.
[75] D. V. Dinh, M. D. Vuong, H. P. Nguyen, H. X. Nguyen, Wireless sensor actor networks and routing performance analysis, International Workshop on Wireless Ad-hoc Network, London, UK, 23-26 May 2005.
[76] Charles E. Perkins and Pravin Bhagwat, Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers, In Proceedings of the SIGCOMM '94 Conference on Communications Architectures, Protocols and Applications, pp. 234-244,August 1994. A revised version of the paper is available from http://www.cs.umd.edu/projects/mcml/pub.html.
[77] Charles E. Perkins and Elizabeth M. Royer, Ad-hoc On-Demand Distance Vector Routing, In Proceedings of the Second IEEE Workshop on Mobile Computing Systems and Applications (WMCSA'99), pp. 90-100, New Orleans, LA, February 1999.
[78] David B. Johnson and David A. Maltz, Dynamic Source Routing in Ad Hoc Wireless Networks, In Mobile Computing, edited by Tomasz Imielinski and Hank Korth, Chapter 5, pp.153-181. Kluwer Academic Publishers, 1996.
[79] E. Cayirci, T. Coplu, and O. Emiroglu, "Power aware many to many routing in wireless sensor and actuator networks," in Proc. of the Second European Workshop, 31 Jan-2 Feb 2005, pp. 236 - 245.
[80] J. A. STANKOVIC, T. F. ABDELZAHER, C.Y. LU, L. SHA, J. C. HOU,Real-Time Communication and Coordination in Embedded Sensor Networks, in PROCEEDINGS OF THE IEEE, VOL. 91, NO. 7:1002-1022, JULY 2003.
[81] A. Shaikh, J. Rexford and K. Shin, "Dynamics of Quality-of-Service Routing with Inaccurate Link-State Information," Univ. of Michigan, Ann Arbor, MI, Tech. Rep. CSE-TR-350-97, Nov. 1997.
[82] A. Orda, "Routing with End to End QoS Guarantees in Broadband Networks," IEEE/ACM Trans. Networking, vol. 7. Pp: 365-374, June 1999
[83] S. Shenker, C. Partridge, and R. Guerin, "Specification of Guaranteed Quality of Service", RFC 2212, IETF, Sept. 1997
[84] B. Rajagopalan and H. Sandick, A Framework for QoS-based Routing in the Internet, RFC 2386, IETF, Aug. 1998
[85] [artcf06]E. C.-H. Ngai, M. R. Lyu, and J. Liu, A real-time communication framework for wireless sensor-actuator networks, in Proc. of the IEEE Aerospace Conference, Big Sky, Montana, U.S., Mar 2006.
[86] [rrods06]Edith C. H. Ngai, Yangfan Zhou, Michael R. Lyu, Jiangchuan Liu,Reliable Reporting of Delay-Sensitive Events in Wireless Sensor-Actuator Networks, MASS06, Third IEEE International Conference on Mobile Ad Hoc and Sensor Systems October 9 - October 12, 2006, Vancouver, Canada.
[87] T. Melodia, D. Pompili, I. F. Akyildiz, A Communication Architecture for Mobile Wireless Sensor and Actor Networks, in Proceedings of IEEE SECON 2006, Reston, VA, September 2006.
[88] R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, 3rd edition. Hoboken, NJ: John Wiley & Sons,Inc., 1996.
[89] H. Yuan, H. Ma, and H. Liao. Coordination mechanism in wireless sensor and actor networks. In 1st IEEE Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2006), volume 2: 627-634, 2006.
[90] A. Durresi and V. Paruchuri, "Geometric broadcast protocol for sensor and actor networks," Int. Conference on Advanced Information Networking and Applications, 28-30 Mar 2005, pp. 343 - 348.
[91] M. Coates, "Evaluating causal relationships in wireless sensor/actuator networks," IEEE Int. Conference on Acoustics, Speech, and Signal Processing, 18-23 Mar 2005, pp.937 - 940.
[92] Fei Hu and X.Cao, "Security in Wireless Actor & Sensor Networks (WASN):Towards A Hierarchical Re-Keying Design", International Conference on Information Technology Coding and Computing (ITCC'05), Las Vegas, NV,Apr. 2005. pp. 528-533.
[93] Fei Hu, Nirmala Sheony, Xin Liu, "Robust Security In Large-scale WASN (Wireless Actor & Sensor Networks): A Low Energy Implementation," IEEE International Conference on Networking, Sensing and Control (ICNSC'05), Tucson, Arizona, U.S.A. March 19-22,2005.
[94] Fei Hu, X. Cao and S. Kumar, "Trustworthiness in Wireless Sensor and Actuator Networks: Towards Low-complexity Reliability and Security", in IEEE GLOBECOM 2005, St. Louis, Dec. 2005.
[95] 曹晓梅,李成法,叶懋,陈贵海,WSAN网络密钥管理模型的研究,计算机应用与软件, 2005 Vol.22 No.10 P.31-33.
[96] K. Akkaya and M. Younis, "COLA: A Coverage and Latency aware Actor Placement for Wireless Sensor and Actor Networks", in the Proceedings of IEEE Vehicular Technology Conference (VTC) 2006, Sept. 2006, Montreal,CA.
[97] Peng Han, Chuanshan Gao, Min Yang, Dilin Mao, Bo Yu: ECLS: An Efficient Cooperative Localization Scheme For Wireless Sensor and Actor Networks. CIT 2005: 396-400
[98] Peng Han, Haiguang Cheng, Bo Yu, Dan Xu, Chuanshan Gao: LSBE: A Localization Scheme Based on Events for Wireless Sensor and Actor Networks. ISPA Workshops 2005: 403-411
[99] G. A. Shah, O. B. Akan, "Timing-based Sensor Localization (TSL) Algorithm for Wireless Sensor and Actor Networks," 2006.
[100] S. Ganeriwal, A. Kansal, and M. B. Srivastava, "Self aware actuation for fault repair in sensor networks," IEEE Int. Conference on Robotics and Automation, 26 Aprl May 2004, pp. 5244 - 5249.
[101] Eylem Ilker Oyman, Multiple Sink Location Problem and Energy Efficiency in Large Scale Wireless Sensor Networks, Phd thesis, Bogazici University,2004.
[102] Staddon, J., D. Balfanz and G. Durfee, Efficient Tracing of Failed Nodes in Sensor Networks, Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications (ACM WSNA 2002), pp.122-130, Atlanta, September 28, 2002.
[103] 谢政.“网络算法与复杂性理论(第二版)”.国防科技大学出版社,2003.
[104] http://www.ilog.com/products/cplex/
[105] J. G. McNeff, The global positioning system, IEEE Transactions on Microwave Theory and Techniques, vol. 50, pp. 645-652, Mar 2002.
[106] L. Hu and D. Evans, Localization for mobile sensor networks, in Proc. of ACM Mobicom, Philadelphia, PA, U.S., 26 Sep - 10ct 2004, pp. 99-110.
[107] A. Savvides, C. C. Han, and M. B. Srivastava, Dynamic fine-grained location in ad hoc networks of sensors, in Proc. of ACM Mobicom,Philadelphia, PA, U.S., 2001, pp. 166-179.
[108] T. He, C. Huang, B. Blum, J. Stankovic, and T. Abdelzaher, Range-Free Localization Schemes for Large Scale Sensor Networks, in Proc. of ACM Mobicom, San Diego, CA, U.S., 2003, pp. 81-95.
[109] Jinyang Li, J. Jannotti, D. S. J. De Couto, D. R. Karger, and R. Morris, A Scalable Location Service for Geographic Ad Hoc Routing, in Proc. of.ACM MobiCom, August 2000.
[110] Q. Li Rus, D., Global Clock Synchronization in Sensor Networks,Proceedings of IEEE INFOCOM'04, Hong Kong, China, 2004.
[111] M. Maroti, B. Kusy, G. Simon, A. Ledeczi, The Flooding Time Synchronization Protocol, Proceedings of the 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys'04), Baltimore, Maryland,USA, 2004.
[112] S. Ganeriwal, R. Kumar, S. Adlakha, M. B. Srivastava, Network-wide Time Synchronization in Sensor Networks, Tech. Rep. NESL Technical Report,UCLA, 2003.
[113] S. Ganeriwal, R. Kumar, M. Srivastava, Timing-sync Protocol for Sensor Networks, Proceedings of the 1st ACM Conference on Embedded Networked Sensor Systems (SenSys'03), Los Angles, CA, USA, 2003.
[114] J. Elson, D. Estrin, Time Synchronization for Wireless Sensor Networks, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS'01), Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, USA, 2001.
[115] J. Elson, K. Romer, Wireless Sensor Networks: A New Regime for Time Synchronization, ACM Mobile Computing and Communication Review (MC2R),2002, 6(4): 59-61.
[116] M. Bhardwaj, T. Garnett, and A. Chandrakasan, Upper bounds on the lifetime of sensor networks. in Proceedings of IEEE International Conference on Communications, vol. 3, Helsinki, Finland, June 2001, pp.785.790.
[117] P. Bose, P. Morin, I. Stojmenovic, and J. Urrutia, Routing with guaranteed delivery in ad hoc wireless networks, ACM Wireless Networks, vol. 7, no. 6,pp. 609-616, Nov. 2001.
[118] J-Sim. http://www.j-sim.org/
[119] G. Lu, B. Krishnamachari, and C.S. Raghavendra. An adaptive energy-efficient and low-latency mac for data gathering in wireless sensor networks. In IEEE Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS), 2004.
[120] Y. Sankarasubramaniam, O. B. Akan, and I. F. Akyildiz. Esrt:event-to-sink reliable transport in wireless sensor networks. In The 4th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing(MobiHoc),2003.
[121] Brian P. Gerkey, Maja J Matari'c, A formal analysis and taxonomy of task allocation in multi-robot systems, In Intl. J. of Robotics Research 23(9):939-954, September2004.
[122] Gale, D., The Theory of Linear Economic Models, McGraw-Hill Book Company, Inc., New York, 1960.
[123] Kuhn, H. W., The Hungarian Method for the Assignment Problem, Naval Research Logistics Quarterly 2(1), 83-97,1955.
[124] Bertsekas, D. P., The Auction Algorithm for Assignment and Other Network Flow Problems: A Tutorial, Interfaces 20(4), 133-149, 1990.
[125] Avis, D., A Survey of Heuristics for the Weighted Matching Problem,Networks 13, 475-493,1983.
[126] Kalyanasundaram, B. & Pruhs, K., 'Online Weighted Matching', J. of Algorithms 14, 478-488, 1993.
[127] Bruno, J. L., Coffman, E. G. & Sethi, R., 'Scheduling Independent Tasks To Reduce Mean Finishing Time', Communications of the ACM 17(7),382-387, 1974.
[128] Balas, E. & Padberg, M.W. (1976), 'Set Partitioning: A Survey', SIAM Review 18(4), 710-760.
[129] Garey, M. R. & Johnson, D. S., '"Strong" NPCompleteness Results:Motivation, Examples, and Implications', J. of the ACM 25(3), 499-508,1978.
[130] Balas, E. & Padberg, M. W., 'On the Set-Covering Problem', Operations Research 20(6), 1152-1161, 1972.
[131] Korte, B. & Vygen, J., Combinatorial Optimization: Theory and Algorithms,Springer-Verlag, Berlin, 2000.
[132] www3.ietf.org/proceedings/02mar/I-D/draft-ietf-manet-tora-spec-04.txt
[133]Matlab.http://www.mathworks.com
[2]I.F.Akyildiz,W.Su,Y.Sankarasubramaniam,E.Cayirci,Wireless Sensor Networks:A Survey,Computer Networks,2002,38(4):393-422.
[3]Edgar H.Callaway,Wireless Sensor Networks:Architectures and Protocols,CRC Press,2003.
[4]孙利民,李建中,陈渝,朱红松,无线传感器网络,清华大学出版社,2005.
[5]任丰原,黄海宁,林闯,无线传感器网络,软件学报,2003,14(7):1282-1291.
[6]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,计算机研究与发展,2005,1:163-174.
[7]彭伟,卢锡城,无线传感器网络及其典型应用,计算机世界,2004.10.27.
[8]http://research.cens.ucla.edu
[9]http://www.janet.ucla.edu/WINS/
[10]http://bwrc.eecs.berkeley.edu
[11]http://webs.cs.berkeley.edu/
[12]http://www robotics.usc.edu/～embedded/
[13]http://www.isi.edu/scadds/
[14]http://www.ece.gatech.edu/research/labs/bwn/index.html
[15]http://www.cast.cse.ohio state.edu/exscal/
[16]http://www.wings.cs.sunysb.edu/
[17]http://www.eecs.harvard.edu/～mdw/proj/codeblue/
[18]http://mantis.cs.colorado.edu/index.php/tiki-index.php
[19]http://www.eng.yale.edu/enalab/
[20]http://nms.csail.mit.edu/
[21]http://www-mtl.mit.edu/researchgroups/icsystems/uamps/
[22]http://lion.cs.uiuc.edu/
[23]http://proj ects.cerias.purdue.edu/esp/
[24]http://www.zurich.ibm.com/sys/communication/sensors.html
[25]http://www.intel.com/research/exploratory/wireless sensors.htm
[26]http://research.microsoft.com/nec/
[27]http://www.xbow.com/Products/Wireless Sensor Networks.htm
[28]http://bwrc.eecs.berkeley.edu/Research/Pico Radio/Default.htm
[29]http://nesl.ee.ucla.edu/projects/ahlos/
[30]http://www.intel.com/research/exploratory/motes.htm
[31]http://www.tinyos.net
[32]http://nesl.ee.ucla.edu/projects/sos/
[33]K.Chakrabarty,S.S.Iyengar,H.Qi,E.Cho,Grid coverage for Surveillance and Target Location in Distributed Sensor Networks,IEEE Transactions on Computers,2002,51(12):1448-1453.
[34]S.S.Dhillon,K.Chakrabarty,S.S.Iyengar,Sensor Placement for Grid Coverage under Imprecise Detections,Proceedings of International Conferrence on Infomation Fusion,2002.
[35] S. S. Dhillon, K. Chakrabarty, Sensor Placement for Effective Coverage and Surveillance in Distributed Sensor Networks, Proceedings of IEEE Wireless Communications and Networking Conference (WCNC'03), 1609-1614,2003.
[36] Y. Zou, K. Chakrabarty, Sensor Deployment and Target Localization Based on Virtual Forces, Proceedings of IEEE INFOCOM'03, 1293-1303, San Francisco, California, USA, 2003.
[37] Y. Zou, K. Chakrabarty, Sensor Deployment and Target Localization in Distributed Sensor Networks, ACM Transactions on Embedded Computing Systems, 2004, 3(1): 61-91.
[38] Y. Zou, Coverage-Centric Sensor Deployment and Energy-Efficient Information Processing in Wireless Sensor Networks, Phd thesis, Duke University, 2004.
[39] H. Takagi and L. Kleinrock, "Optimal Transmission Ranges for Randomly Distributed Packet Radio Terminals," IEEE Trans. Commun., vol. 32, no. 3,1984, pp. 246-57.
[40] T. Hou and V. Li, Transmission range control in multihop packet radio networks, IEEE Transactions on Communications 1 (1986) (34), pp. 38-44.
[41] E. Kranakis, H. Singh and J. Urrutia, Compass routing on geometric networks, Proceedings of the 11th Canadian Conference on Computational Geometry (1999), pp. 51-54.
[42] G. Finn. Routing and addressing problems in large metropolitan-scale internet works. Technical Report ISI/RR-87-180, USC/ISI, March 1987.
[43] I. Stojmenovic and Xu Lin, "Loop-free Hybrid Single-Path/Flooding Routing Algorithms with Guaranteed Delivery for Wireless Networks,"IEEE Trans. Parallel Dist. Sys., vol. 12, no. 10,2001, pp. 1023-32.
[44] Stojmenovic and Xu Lin, "Power-Aware Localized Routing in Wireless Networks," IEEE Trans. Parallel Dist. Sys., vol. 12, no. 11, 2001, pp.1122-33.
[45] L. Blazevic et al.,"Self-organization in Mobile Ad Hoc Networks: The Approach of Terminodes," IEEE Commun. Mag., June 2001, pp. 166-75.
[46] R. Jain, A. Puri, and R. Sengupta, "Geographical Routing Using Partial Information for Wireless Ad Hoc Networks," IEEE Pers. Commun., Feb.2001, pp. 48-57.
[47] P. Bose et al., "Routing with Guaranteed Delivery in Ad Hoc Wireless Networks," 3rd IntT. Wksp. Discrete Algorithms Methods Mobile Comp.Commun., Seattle, WA, Aug. 20, 1999, pp. 48-55; also in ACM/Kluwer WLNets., vol. 7, no. 6, Nov. 2001, pp. 609-16.
[48] Stojmenovic, M. Seddigh, and J. Zunic, "Dominating Sets and Neighbor Elimination Based Broadcasting Algorithms in Wireless Networks," IEEE Trans. Parallel Dist. Sys., vol. 13, no. 1, Jan. 2002, pp. 14-25.
[49] Brad Karp and H.T. Kung, "Gpsr: Greedy perimeter stateless routing for wireless networks," in Proc. of the ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom), 2000.
[50] Fabian Kuhn, Roger Wattenhofer, and Aaron Zollinger, "Asymptotically optimal geometric mobile ad-hoc routing," in Proceedings of the 6~(th) International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications (DIALM), 2002.
[51] Fabian Kuhn, Roger Wattenhofer, and Aaron Zollinger, "Worst-Case Optimal and Average-Case Efficient Geometric Ad-Hoc Routing" in Proc. 4 th ACM Int. Symposium on Mobile Ad-Hoc Networking and Computing(MobiHoc), 2003.
[52] F. Kuhn, R. Wattenhofer, Y. Zhang, and A. Zollinger. Geometric Routing: Of Theory and Practice. In Proc. of the 22 nd ACM Symposium on the Principles of Distributed Computing (PODC), 2003.
[53] C. Intanagonwiwat, R. Govindan, D. Estrin, Directed diffusion: a scalable and robust communication paradigm for sensor networks, Proceedings of the ACM MobiCom'00, Boston, MA, 2000, pp. 56 - 67.
[54] Rahul C. Shah, Jan M. Rabaey, Energy Aware Routing for Low Energy Ad Hoc Sensor Networks, IEEE Wireless Communications and Networking Conference (WCNC), Orlando, FL.2002.
[55] L. Li, and J. Y. Halpern, "Minimum-Energy Mobile Wireless Networks Revisited," ICC '01, Helsinki, Finland,June 2001.
[56] V. Rodoplu, T.H. Meng, Minimum energy mobile wireless networks, IEEE Journal of Selected Areas in Communications 17 (8) (1999) 1333-1344.
[57] CHEN Y, LIU A, ZHANG S. A scalable solution to minimum cost forwarding in large sensor networks[A]. Proceedings of Tenth international Conference on Computer Communications and Networks[C]. Scottsdale,USA, 2001:304-309.
[58] Sohrabi K, Gao J, Ailawadhi V, Pottie GJ. Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 2000,7(5):16-27.
[59] F. Ye, H. Luo, J. Cheng, S. Lu, L. Zhang, A two-tier data dissemination model for large-scale wireless sensor networks, in: Proceedings of the ACM Mobicom'02 Eighth Annual International Conference on Mobile computing and networking, ACM Press, 2002, pp. 148-159.
[60] Weilian Su, Ian F. Akyildiz, A Stream Enabled Routing (SER) Protocol for Sensor Networks, in "Med-hoc-Net 2002", Sardegna, Italy, September 2002.
[61] H.S. Kim T.F. Abdelzaher, W.H. Kwon, Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks, in: Proceedings of the First International Conference on Embedded Networked Sensor Systems(Sensys'03), pp. 193 - 204, November 2003.
[62] W. Hu, N. Bulusu, S. Jha, An anycast service for hybrid sensor/actuator networks, The University of New South Wales (UNSW) CSE Technical Report 0331, October 2003.
[63] M. Caccamo, Lynn Y. Zhang, Lui Sha, G. Buttazzo, an Implicit Prioritized Access Protocol for Wireless Sensor Networks, Proceedings of the 23rd IEEE REAL-TIME SYSTEMS SYMPOSIUM (RTSS'02).
[64] M. Caccamo, The Capacity of Implicit EDF in Wireless Sensor Networks.RealTime Systems, 2003 Proceedings, 15th Euromicro Conference'03, 2003.
[65] Chenyang Lu, Brian M. Blum, Tarek F. Abdelzaher, John A. Stankovic, Tian He, RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks, in RealTime and Embedded Technology and Applications Symposium, 2002. Proceedings Eighth IEEE, 2002
[66] K. Akkaya, M. Younis, An Energy-Aware QoS Routing Protocol for Wireless Sensor Networks, Proc. of the IEEE Workshop on Mobile and Wireless Networks (MWN 2003), Providence, RI, USA, May 2003.
[67] K. Akkaya, M. Younis, Energy and QoS aware routing in wireless sensor networks, Cluster Computing 8, 179-188, 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.
[68] T. He, J. Stankovic, C. Lu, T. Abdelzaher, SPEED: A real-time routing protocol for sensor networks, in: Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS), Providence, Rhode Island, USA, pp. 46-55, May 2003.
[69] E. Felemban, C.G. Lee, E. Ekici, R. Boder, and S. Vural, Probabilistic QoS Guarantee in Reliability and Timeliness Domains in Wireless Sensor Networks, in Proceedings of IEEE INFOCOM 2005, Miami, FL, USA, Mar.2005.
[70] H. Kim, Y. Seok, N. Choi,Y. Choi, and T. Kwon, Optimal Multi-sink Positioning and Energy-efficient Routing in Wireless Sensor Networks, in ICOIN 2005, Jeju Island, Korea, January 31- February 2, 2005, Proceedings.
[71] R. Chandra, Lili Qiu, K. Jain, M. Mahdian, Optimizing the Placement of Internet TAPs in Wireless Neighborhood Networks, ICNP, Oct. 2004.
[72] Kevin Yuen, Baochun Li, Ben Liang, Distributed Data Gathering in Multi-Sink Sensor Networks with Correlated Sources, to appear in the Proceedings of IFIP Networking 2006, Coimbra, Portugal, May 15-19, 2006
[73] H. Dubois-Ferriere and D. Estrin, Efficient and Practical Query Scoping in Sensor Networks, Tech. Rep. 2004-39, CENS/UCLA Tech Report, 2004.
[74] Tommaso Melodia, Dario Pompili, Vehbi C. Gungor and Ian F. Akyildiz, A Distributed Coordination Framework for Wireless Sensor and Actor Networks, Proceedings of ACM MobiHoc'05, Urbana-Champaign, Illinois,USA, 2005.
[75] D. V. Dinh, M. D. Vuong, H. P. Nguyen, H. X. Nguyen, Wireless sensor actor networks and routing performance analysis, International Workshop on Wireless Ad-hoc Network, London, UK, 23-26 May 2005.
[76] Charles E. Perkins and Pravin Bhagwat, Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers, In Proceedings of the SIGCOMM '94 Conference on Communications Architectures, Protocols and Applications, pp. 234-244,August 1994. A revised version of the paper is available from http://www.cs.umd.edu/projects/mcml/pub.html.
[77] Charles E. Perkins and Elizabeth M. Royer, Ad-hoc On-Demand Distance Vector Routing, In Proceedings of the Second IEEE Workshop on Mobile Computing Systems and Applications (WMCSA'99), pp. 90-100, New Orleans, LA, February 1999.
[78] David B. Johnson and David A. Maltz, Dynamic Source Routing in Ad Hoc Wireless Networks, In Mobile Computing, edited by Tomasz Imielinski and Hank Korth, Chapter 5, pp.153-181. Kluwer Academic Publishers, 1996.
[79] E. Cayirci, T. Coplu, and O. Emiroglu, "Power aware many to many routing in wireless sensor and actuator networks," in Proc. of the Second European Workshop, 31 Jan-2 Feb 2005, pp. 236 - 245.
[80] J. A. STANKOVIC, T. F. ABDELZAHER, C.Y. LU, L. SHA, J. C. HOU,Real-Time Communication and Coordination in Embedded Sensor Networks, in PROCEEDINGS OF THE IEEE, VOL. 91, NO. 7:1002-1022, JULY 2003.
[81] A. Shaikh, J. Rexford and K. Shin, "Dynamics of Quality-of-Service Routing with Inaccurate Link-State Information," Univ. of Michigan, Ann Arbor, MI, Tech. Rep. CSE-TR-350-97, Nov. 1997.
[82] A. Orda, "Routing with End to End QoS Guarantees in Broadband Networks," IEEE/ACM Trans. Networking, vol. 7. Pp: 365-374, June 1999
[83] S. Shenker, C. Partridge, and R. Guerin, "Specification of Guaranteed Quality of Service", RFC 2212, IETF, Sept. 1997
[84] B. Rajagopalan and H. Sandick, A Framework for QoS-based Routing in the Internet, RFC 2386, IETF, Aug. 1998
[85] [artcf06]E. C.-H. Ngai, M. R. Lyu, and J. Liu, A real-time communication framework for wireless sensor-actuator networks, in Proc. of the IEEE Aerospace Conference, Big Sky, Montana, U.S., Mar 2006.
[86] [rrods06]Edith C. H. Ngai, Yangfan Zhou, Michael R. Lyu, Jiangchuan Liu,Reliable Reporting of Delay-Sensitive Events in Wireless Sensor-Actuator Networks, MASS06, Third IEEE International Conference on Mobile Ad Hoc and Sensor Systems October 9 - October 12, 2006, Vancouver, Canada.
[87] T. Melodia, D. Pompili, I. F. Akyildiz, A Communication Architecture for Mobile Wireless Sensor and Actor Networks, in Proceedings of IEEE SECON 2006, Reston, VA, September 2006.
[88] R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, 3rd edition. Hoboken, NJ: John Wiley & Sons,Inc., 1996.
[89] H. Yuan, H. Ma, and H. Liao. Coordination mechanism in wireless sensor and actor networks. In 1st IEEE Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2006), volume 2: 627-634, 2006.
[90] A. Durresi and V. Paruchuri, "Geometric broadcast protocol for sensor and actor networks," Int. Conference on Advanced Information Networking and Applications, 28-30 Mar 2005, pp. 343 - 348.
[91] M. Coates, "Evaluating causal relationships in wireless sensor/actuator networks," IEEE Int. Conference on Acoustics, Speech, and Signal Processing, 18-23 Mar 2005, pp.937 - 940.
[92] Fei Hu and X.Cao, "Security in Wireless Actor & Sensor Networks (WASN):Towards A Hierarchical Re-Keying Design", International Conference on Information Technology Coding and Computing (ITCC'05), Las Vegas, NV,Apr. 2005. pp. 528-533.
[93] Fei Hu, Nirmala Sheony, Xin Liu, "Robust Security In Large-scale WASN (Wireless Actor & Sensor Networks): A Low Energy Implementation," IEEE International Conference on Networking, Sensing and Control (ICNSC'05), Tucson, Arizona, U.S.A. March 19-22,2005.
[94] Fei Hu, X. Cao and S. Kumar, "Trustworthiness in Wireless Sensor and Actuator Networks: Towards Low-complexity Reliability and Security", in IEEE GLOBECOM 2005, St. Louis, Dec. 2005.
[95] 曹晓梅,李成法,叶懋,陈贵海,WSAN网络密钥管理模型的研究,计算机应用与软件, 2005 Vol.22 No.10 P.31-33.
[96] K. Akkaya and M. Younis, "COLA: A Coverage and Latency aware Actor Placement for Wireless Sensor and Actor Networks", in the Proceedings of IEEE Vehicular Technology Conference (VTC) 2006, Sept. 2006, Montreal,CA.
[97] Peng Han, Chuanshan Gao, Min Yang, Dilin Mao, Bo Yu: ECLS: An Efficient Cooperative Localization Scheme For Wireless Sensor and Actor Networks. CIT 2005: 396-400
[98] Peng Han, Haiguang Cheng, Bo Yu, Dan Xu, Chuanshan Gao: LSBE: A Localization Scheme Based on Events for Wireless Sensor and Actor Networks. ISPA Workshops 2005: 403-411
[99] G. A. Shah, O. B. Akan, "Timing-based Sensor Localization (TSL) Algorithm for Wireless Sensor and Actor Networks," 2006.
[100] S. Ganeriwal, A. Kansal, and M. B. Srivastava, "Self aware actuation for fault repair in sensor networks," IEEE Int. Conference on Robotics and Automation, 26 Aprl May 2004, pp. 5244 - 5249.
[101] Eylem Ilker Oyman, Multiple Sink Location Problem and Energy Efficiency in Large Scale Wireless Sensor Networks, Phd thesis, Bogazici University,2004.
[102] Staddon, J., D. Balfanz and G. Durfee, Efficient Tracing of Failed Nodes in Sensor Networks, Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications (ACM WSNA 2002), pp.122-130, Atlanta, September 28, 2002.
[103] 谢政.“网络算法与复杂性理论(第二版)”.国防科技大学出版社,2003.
[104] http://www.ilog.com/products/cplex/
[105] J. G. McNeff, The global positioning system, IEEE Transactions on Microwave Theory and Techniques, vol. 50, pp. 645-652, Mar 2002.
[106] L. Hu and D. Evans, Localization for mobile sensor networks, in Proc. of ACM Mobicom, Philadelphia, PA, U.S., 26 Sep - 10ct 2004, pp. 99-110.
[107] A. Savvides, C. C. Han, and M. B. Srivastava, Dynamic fine-grained location in ad hoc networks of sensors, in Proc. of ACM Mobicom,Philadelphia, PA, U.S., 2001, pp. 166-179.
[108] T. He, C. Huang, B. Blum, J. Stankovic, and T. Abdelzaher, Range-Free Localization Schemes for Large Scale Sensor Networks, in Proc. of ACM Mobicom, San Diego, CA, U.S., 2003, pp. 81-95.
[109] Jinyang Li, J. Jannotti, D. S. J. De Couto, D. R. Karger, and R. Morris, A Scalable Location Service for Geographic Ad Hoc Routing, in Proc. of.ACM MobiCom, August 2000.
[110] Q. Li Rus, D., Global Clock Synchronization in Sensor Networks,Proceedings of IEEE INFOCOM'04, Hong Kong, China, 2004.
[111] M. Maroti, B. Kusy, G. Simon, A. Ledeczi, The Flooding Time Synchronization Protocol, Proceedings of the 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys'04), Baltimore, Maryland,USA, 2004.
[112] S. Ganeriwal, R. Kumar, S. Adlakha, M. B. Srivastava, Network-wide Time Synchronization in Sensor Networks, Tech. Rep. NESL Technical Report,UCLA, 2003.
[113] S. Ganeriwal, R. Kumar, M. Srivastava, Timing-sync Protocol for Sensor Networks, Proceedings of the 1st ACM Conference on Embedded Networked Sensor Systems (SenSys'03), Los Angles, CA, USA, 2003.
[114] J. Elson, D. Estrin, Time Synchronization for Wireless Sensor Networks, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS'01), Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, USA, 2001.
[115] J. Elson, K. Romer, Wireless Sensor Networks: A New Regime for Time Synchronization, ACM Mobile Computing and Communication Review (MC2R),2002, 6(4): 59-61.
[116] M. Bhardwaj, T. Garnett, and A. Chandrakasan, Upper bounds on the lifetime of sensor networks. in Proceedings of IEEE International Conference on Communications, vol. 3, Helsinki, Finland, June 2001, pp.785.790.
[117] P. Bose, P. Morin, I. Stojmenovic, and J. Urrutia, Routing with guaranteed delivery in ad hoc wireless networks, ACM Wireless Networks, vol. 7, no. 6,pp. 609-616, Nov. 2001.
[118] J-Sim. http://www.j-sim.org/
[119] G. Lu, B. Krishnamachari, and C.S. Raghavendra. An adaptive energy-efficient and low-latency mac for data gathering in wireless sensor networks. In IEEE Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS), 2004.
[120] Y. Sankarasubramaniam, O. B. Akan, and I. F. Akyildiz. Esrt:event-to-sink reliable transport in wireless sensor networks. In The 4th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing(MobiHoc),2003.
[121] Brian P. Gerkey, Maja J Matari'c, A formal analysis and taxonomy of task allocation in multi-robot systems, In Intl. J. of Robotics Research 23(9):939-954, September2004.
[122] Gale, D., The Theory of Linear Economic Models, McGraw-Hill Book Company, Inc., New York, 1960.
[123] Kuhn, H. W., The Hungarian Method for the Assignment Problem, Naval Research Logistics Quarterly 2(1), 83-97,1955.
[124] Bertsekas, D. P., The Auction Algorithm for Assignment and Other Network Flow Problems: A Tutorial, Interfaces 20(4), 133-149, 1990.
[125] Avis, D., A Survey of Heuristics for the Weighted Matching Problem,Networks 13, 475-493,1983.
[126] Kalyanasundaram, B. & Pruhs, K., 'Online Weighted Matching', J. of Algorithms 14, 478-488, 1993.
[127] Bruno, J. L., Coffman, E. G. & Sethi, R., 'Scheduling Independent Tasks To Reduce Mean Finishing Time', Communications of the ACM 17(7),382-387, 1974.
[128] Balas, E. & Padberg, M.W. (1976), 'Set Partitioning: A Survey', SIAM Review 18(4), 710-760.
[129] Garey, M. R. & Johnson, D. S., '"Strong" NPCompleteness Results:Motivation, Examples, and Implications', J. of the ACM 25(3), 499-508,1978.
[130] Balas, E. & Padberg, M. W., 'On the Set-Covering Problem', Operations Research 20(6), 1152-1161, 1972.
[131] Korte, B. & Vygen, J., Combinatorial Optimization: Theory and Algorithms,Springer-Verlag, Berlin, 2000.
[132] www3.ietf.org/proceedings/02mar/I-D/draft-ietf-manet-tora-spec-04.txt
[133]Matlab.http://www.mathworks.com