无线传感器网络基站移动算法研究
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摘要
无线传感器网络(Wireless Sensor Networks，简称WSNs)是近年来发展起来的一项重要的信息技术，被广泛应用于工农业生产、环境监控和军事等领域。节点能量有限是限制传感器网络生存时间的一大瓶颈，因此如何有效地利用节点能量，延长网络生存时间，是需要研究的重要问题之一。为了解决该问题，一种重要方法就是通过基站的移动来有效利用节点能量并延长网络生存时间，以及提高网络的其它性能。本文针对不同类型的传感器网络提出了相应的基站移动算法，主要研究成果如下：
1.研究了事件驱动单基站传感器网络的基站移动算法。本文指出了预测随机事件的发生是不可能的。但是当一个事件发生或结束时，其内部的传感器节点可以将相关信息发送给基站。基站可以根据这些信息计算出一个新的优化位置。然后，指出了优化的位置应该使得基站到所有事件中的节点的距离之和最小。提出了一种启发式几何中心算法，把所有处于事件中的传感器节点的几何中心作为次优的基站位置。最后提出了该算法具体实现的方法。仿真结果表明，该算法显著提高了网络生存时间以及其它一些网络性能。
2.研究了事件驱动多基站传感器网络的基站移动算法。网络模型和基本思想与事件驱动单基站传感器网络的基站移动算法类似，不同之处在于网络中部署了多个基站。本文指出节点应把距离自己最近的基站作为发送数据的目标基站。当网络中有事件发生或结束时，基站可以收到相关信息，并根据这些信息计算各基站新的优化位置。事件中各节点到其目标基站的距离之和应该最小，因此本文提出了一种基于遗传算法的算法。该算法把每个解决方案作为一个个体，适应度函数考虑事件中各节点到其目标基站的距离之和，以及各基站当前位置与新位置之间的距离之和。通过若干代的选择、交叉、变异，最终得到一个优化的各基站位置解决方案。最后，提出了各基站如何合作和协调，以实现该算法。仿真结果表明，该算法显著提高了网络生存时间以及其它一些网络性能。
3.提出了基于遗传算法的下一轮基站位置选择算法。该算法针对时间驱动单基站网络提出。网络中部署了一个移动基站，所有节点定期感知并向基站发送数据，每个数据收集周期被称为一轮。每一轮结束时，根据各节点当前剩余能量，为下一轮选择一个优化的基站位置。本文提出了一种基于遗传算法的基站移动算法。该算法把基站位置作为个体，假设基站移动到该位置并进行一轮数据收集，计算出所有节点的假设剩余能量的方差，并把该方差作为适应度函数值。经过若干代的选择、交叉、变异，得到一个优化的基站位置。仿真结果表明，该算法显著延长了网络生存时间。
4.提出了基于线性规划的传感器网络基站移动算法。网络中部署了一个移动基站，所有节点连续的收集并且向基站发送数据。本文提出了基于线性规划的基站移动算法，假设基站在每个节点处停留并根据LET(Least Energy Tree)算法生成相应的路由树，计算出各节点的负载，再采用线性规划方法计算出基站在各节点处停留的时间。仿真结果表明，该算法有效地延长了网络生存时间。
5.提出了基于动态缓冲区的基站移动算法。为了延长网络生存时间，在网络中设置了缓冲区，基站在缓冲区内移动。所有节点收集到的数据先发送到缓冲区，再由缓冲区内节点发送到基站。为了进一步延长网络生存时间，本文提出了一种新的基于动态缓冲区的数据收集算法(Dynamic Buffer Zone Data Gathering，简称DBDG)，该算法把网络划分成若干个区域，让每个区域轮流充当缓冲区，并用线性规划的方法计算出每个区域充当缓冲区的合理时间，从而提高能量利用效率。实验表明，DBDG有效地延长了网络生存时间。
Wireless Sensor Networks (WSNs) is an important technology that has beendeveloped in recent years and widely used in many applications, such as industry,agriculture, environmental monitoring, military, and so on. Sensors’ limit energy is asevere bottleneck that restricts network lifetime. Therefore, how to effectively usesensors’ energy and extend network lifetime is a very important issue. To resolve thisproblerm, one important approach proposed is sink mobility, which can effectively usesensors’ energy, extend network lifetime and improve other performances. In this paper,algorithms for sink mobility are studied and some algorithms are proposed aiming atdifferent types of WSNs. The author’s major contributions are outlined as follows:
1. The algorithm for event-driven sensor networks with single mobile sink hasbeen studied. In this paper, it is pointed that it is impossible to forecast random events.However, a sensor can inform the sink when it detects that an event involving it beginsoccurring or the event involving it terminates. The sink can compute a new optimalposition based on the information. Then it is pointed that the sink's optimal positionshould be the position where the sum of all sensors in event area to the sink is minimum.A heuristic geometrical centre algorithm has been proposed in this paper. The algorithmregards the geometrical centre of all the sensors in event areas as a sub-optimal positionof the sink. And the detailed method that realizes the algorithm has been proposed. Thesimulation results show that the algorithm notably improves network lifetime as well asother performances.
2. The algorithm for event-driven sensor networks with multiple mobile sinks hasbeen studied. The network model and basic idea are similar with those of the algorithmfor single mobile sink proposed by us. The difference is that multiple mobile sinks aredeployed in network. In this paper, it is pointed that a sensor should select the sinkwhich is nearest to it as its target sink and report its data to the target sink. When eventsarise or terminate in network, the sinks can obtain the information and computes newoptimal positions for each sink. The sum of all the distances from all sensors in eventareas to their target sinks should be minimum. Therefore, a genetic algorithm basedalgorithm has been proposed in this paper. In the algorithm, a solution is regarded as anindividual. The following two factors are considered in the fitness function: the sum ofthe distances from all sensors in event areas to their target sinks, as well as the sum ofall sensors' distances from their current positions to their new positions. Aftergenerations of selection, crossover and mutation, an optimal solution for all sinks' new positions is finally obtained. The detailed method how the sinks collaborate andcoordinate to realize the algorithm has also been proposed. The simulation results showthat the algorithm notably improves network lifetime as well as other performances.
3. An algorithm for the sink's next round position selection which is based ongenetic algorithms has been proposed. The algorithm aims at periodic sensing sensornetworks with single mobile sink. A mobile sink is deployed in the network. All sensorssense and report data to the sink periodically. Each period of data gathering is referredto as a round. At the end of each round, an optimal position of the sink for the nextround is obtained depending on all sensors' current residual energy. A genetic algorithmsbased algorithm for sink mobility has been proposed in this paper. The algorithmregards a position for the sink as an individual. It is assumed that the sink moves to theposition and make a round of data gathering. Then the variance of all sensors'hypothetical residual energy is computed, which is the fitness function value of geneticalgorithm. After generations of selection, crossover and mutation, an optimal positionfor the sink is finally obtained. The simulation results show that the algorithm notablyextend network lifetime.
4. An algorithm for sink mobility based on linear programming has been proposed.A mobile sink is deployed in network. All sensors consecutively sense and report data tothe sink. In this paper, an algorithm for sink mobility based on linear programming hasbeen proposed. It is assumed that the sink sojourns at each sensor's position andcorresponding routing tree is constructed with LET (Least Energy Tree) algorithm. Andeach sensor's load is computed on the basis of the routing trees. Finally, the sojourn timeat each sensor are computed with linear programming. The simulation results show thatthe algorithm notably extend network lifetime.
5. An algorithm for sink mobility based on dynamic buffer zone has been proposed.In order to prolong network lifetime, buffer zone has been proposed to be deployed innetwork area. The sink moves in the buffer zone. All sensors send their sensed data tothe sensors in the buffer zone. And the sensors in the buffer zone send the data to thesink. To further extend network lifetime, dynamic buffer zone data gathering (DBDG)algorithm has been proposed in this paper. The aogorithm divides the whole networkarea into some areas. The areas take turns at working as the buffer zone. And the time azone work as the buffer zone is reasonably computed with the linear programming toimprove energy efficiency and extend network lifetime. The simulation results showthat DBDG notably extend network lifetime.
1.研究了事件驱动单基站传感器网络的基站移动算法。本文指出了预测随机事件的发生是不可能的。但是当一个事件发生或结束时，其内部的传感器节点可以将相关信息发送给基站。基站可以根据这些信息计算出一个新的优化位置。然后，指出了优化的位置应该使得基站到所有事件中的节点的距离之和最小。提出了一种启发式几何中心算法，把所有处于事件中的传感器节点的几何中心作为次优的基站位置。最后提出了该算法具体实现的方法。仿真结果表明，该算法显著提高了网络生存时间以及其它一些网络性能。
2.研究了事件驱动多基站传感器网络的基站移动算法。网络模型和基本思想与事件驱动单基站传感器网络的基站移动算法类似，不同之处在于网络中部署了多个基站。本文指出节点应把距离自己最近的基站作为发送数据的目标基站。当网络中有事件发生或结束时，基站可以收到相关信息，并根据这些信息计算各基站新的优化位置。事件中各节点到其目标基站的距离之和应该最小，因此本文提出了一种基于遗传算法的算法。该算法把每个解决方案作为一个个体，适应度函数考虑事件中各节点到其目标基站的距离之和，以及各基站当前位置与新位置之间的距离之和。通过若干代的选择、交叉、变异，最终得到一个优化的各基站位置解决方案。最后，提出了各基站如何合作和协调，以实现该算法。仿真结果表明，该算法显著提高了网络生存时间以及其它一些网络性能。
3.提出了基于遗传算法的下一轮基站位置选择算法。该算法针对时间驱动单基站网络提出。网络中部署了一个移动基站，所有节点定期感知并向基站发送数据，每个数据收集周期被称为一轮。每一轮结束时，根据各节点当前剩余能量，为下一轮选择一个优化的基站位置。本文提出了一种基于遗传算法的基站移动算法。该算法把基站位置作为个体，假设基站移动到该位置并进行一轮数据收集，计算出所有节点的假设剩余能量的方差，并把该方差作为适应度函数值。经过若干代的选择、交叉、变异，得到一个优化的基站位置。仿真结果表明，该算法显著延长了网络生存时间。
4.提出了基于线性规划的传感器网络基站移动算法。网络中部署了一个移动基站，所有节点连续的收集并且向基站发送数据。本文提出了基于线性规划的基站移动算法，假设基站在每个节点处停留并根据LET(Least Energy Tree)算法生成相应的路由树，计算出各节点的负载，再采用线性规划方法计算出基站在各节点处停留的时间。仿真结果表明，该算法有效地延长了网络生存时间。
5.提出了基于动态缓冲区的基站移动算法。为了延长网络生存时间，在网络中设置了缓冲区，基站在缓冲区内移动。所有节点收集到的数据先发送到缓冲区，再由缓冲区内节点发送到基站。为了进一步延长网络生存时间，本文提出了一种新的基于动态缓冲区的数据收集算法(Dynamic Buffer Zone Data Gathering，简称DBDG)，该算法把网络划分成若干个区域，让每个区域轮流充当缓冲区，并用线性规划的方法计算出每个区域充当缓冲区的合理时间，从而提高能量利用效率。实验表明，DBDG有效地延长了网络生存时间。
Wireless Sensor Networks (WSNs) is an important technology that has beendeveloped in recent years and widely used in many applications, such as industry,agriculture, environmental monitoring, military, and so on. Sensors’ limit energy is asevere bottleneck that restricts network lifetime. Therefore, how to effectively usesensors’ energy and extend network lifetime is a very important issue. To resolve thisproblerm, one important approach proposed is sink mobility, which can effectively usesensors’ energy, extend network lifetime and improve other performances. In this paper,algorithms for sink mobility are studied and some algorithms are proposed aiming atdifferent types of WSNs. The author’s major contributions are outlined as follows:
1. The algorithm for event-driven sensor networks with single mobile sink hasbeen studied. In this paper, it is pointed that it is impossible to forecast random events.However, a sensor can inform the sink when it detects that an event involving it beginsoccurring or the event involving it terminates. The sink can compute a new optimalposition based on the information. Then it is pointed that the sink's optimal positionshould be the position where the sum of all sensors in event area to the sink is minimum.A heuristic geometrical centre algorithm has been proposed in this paper. The algorithmregards the geometrical centre of all the sensors in event areas as a sub-optimal positionof the sink. And the detailed method that realizes the algorithm has been proposed. Thesimulation results show that the algorithm notably improves network lifetime as well asother performances.
2. The algorithm for event-driven sensor networks with multiple mobile sinks hasbeen studied. The network model and basic idea are similar with those of the algorithmfor single mobile sink proposed by us. The difference is that multiple mobile sinks aredeployed in network. In this paper, it is pointed that a sensor should select the sinkwhich is nearest to it as its target sink and report its data to the target sink. When eventsarise or terminate in network, the sinks can obtain the information and computes newoptimal positions for each sink. The sum of all the distances from all sensors in eventareas to their target sinks should be minimum. Therefore, a genetic algorithm basedalgorithm has been proposed in this paper. In the algorithm, a solution is regarded as anindividual. The following two factors are considered in the fitness function: the sum ofthe distances from all sensors in event areas to their target sinks, as well as the sum ofall sensors' distances from their current positions to their new positions. Aftergenerations of selection, crossover and mutation, an optimal solution for all sinks' new positions is finally obtained. The detailed method how the sinks collaborate andcoordinate to realize the algorithm has also been proposed. The simulation results showthat the algorithm notably improves network lifetime as well as other performances.
3. An algorithm for the sink's next round position selection which is based ongenetic algorithms has been proposed. The algorithm aims at periodic sensing sensornetworks with single mobile sink. A mobile sink is deployed in the network. All sensorssense and report data to the sink periodically. Each period of data gathering is referredto as a round. At the end of each round, an optimal position of the sink for the nextround is obtained depending on all sensors' current residual energy. A genetic algorithmsbased algorithm for sink mobility has been proposed in this paper. The algorithmregards a position for the sink as an individual. It is assumed that the sink moves to theposition and make a round of data gathering. Then the variance of all sensors'hypothetical residual energy is computed, which is the fitness function value of geneticalgorithm. After generations of selection, crossover and mutation, an optimal positionfor the sink is finally obtained. The simulation results show that the algorithm notablyextend network lifetime.
4. An algorithm for sink mobility based on linear programming has been proposed.A mobile sink is deployed in network. All sensors consecutively sense and report data tothe sink. In this paper, an algorithm for sink mobility based on linear programming hasbeen proposed. It is assumed that the sink sojourns at each sensor's position andcorresponding routing tree is constructed with LET (Least Energy Tree) algorithm. Andeach sensor's load is computed on the basis of the routing trees. Finally, the sojourn timeat each sensor are computed with linear programming. The simulation results show thatthe algorithm notably extend network lifetime.
5. An algorithm for sink mobility based on dynamic buffer zone has been proposed.In order to prolong network lifetime, buffer zone has been proposed to be deployed innetwork area. The sink moves in the buffer zone. All sensors send their sensed data tothe sensors in the buffer zone. And the sensors in the buffer zone send the data to thesink. To further extend network lifetime, dynamic buffer zone data gathering (DBDG)algorithm has been proposed in this paper. The aogorithm divides the whole networkarea into some areas. The areas take turns at working as the buffer zone. And the time azone work as the buffer zone is reasonably computed with the linear programming toimprove energy efficiency and extend network lifetime. The simulation results showthat DBDG notably extend network lifetime.
引文
[1] Akyildiz IF, Su W, Sankarasubramaniam Y, etc. Wireless sensor networks: Asurvey. Computer Networks.2002,4,38(4),393-422.
[2] G. J. Pottie, W. J. Kaiser. Wireless integrated network sensors. Communicationsof the ACM.2000,3,43(5),51-58.
[3] R. Min. Low power wireless sensor networks. The Proceedings of InternationalConference on VLSI Design.2001,1,1(1),5-21.
[4][美]S.Sitharama Lyenga, Richard R. Brooks,夏利,江汉红,魏刚,吴正国译.分布式传感器网络.版本.北京：电子工业出版社,2010.2-5.
[5]崔莉,鞠海玲,苗勇等.无线传感器网络研究进展.计算机研究与发展.2005,1,42(1),163-174.
[6] Garzas J. Joaquin Escudero, Calzon Carlos Bousono, Armada Ana Garcia. Anenergy-efficient adaptive modulation suitable for wireless sensor networks withSER and throughput constraints. Eurasip Journal on Wireless Communicationsand Networking.2007,6,2007(6),1-7.
[7]李文峰.无线传感器网络与移动机器人控制.第一版.北京：科学出版社,2009,1-2.
[8] Byrne J A.21ideas for21stCentury. Business Week.1999,8,1(8),78-167.
[9] Wade R,Mitehel W M,Ptter F. Ten Emerging Technologies that will Change theWorld. Technologies.2003,1,106(1),33-49.
[10]崔逊学,左从菊.无线传感器网络简明教程.第一版.北京：清华大学出版社,2011.1-3.
[11]张晓彤,班晓娟,段世红等.无线传感器网络与人工生命.第一版.北京：国防工业出版社,2008.2-9.
[12]王雪.无线传感器网络测量系统.第一版.北京：机械工业出版社,2007.56-401.
[13]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.软件学报.2003,10,14(10),1717-1727.
[14]王殊,胡富平,屈晓旭等.无线传感器网络的理论及应用.第一版.北京：北京航空航天大学出版社,2007.7-15.
[15]李善仓,张克旺.无线传感器网络原理与应用.第一版.北京：机械工业出版社,2008.12-29.
[16]景博,张劼,孙勇.智能网络传感器与无线传感器网络.第一版.北京：国防工业出版社,2011.29-30,402-405.
[17]彭绍亮,彭宇行等.无线传感器网络中高效传输技术.第一版.长沙：国防科技大学出版社,2010.6-7.
[18]于海滨,曾鹏.智能无线传感器网络系统.第一版.北京：科学出版社,2006.6-11.
[19] Y. Bi,L. Sun,N. Li. BoSS: a moving strategy for mobile sinks in wireless sensornetworks. International Journal of Sensor Networks.2009,6,5(3),173-184.
[20] K. Akkaya,M. Younis,M. Bangad. Sink repositioning for enhanced performancein wireless sensor networks. Computer Networks.2005,3,49(3),512–534.
[21]龚海刚,刘明,余昌远等.无线传感器网络环境下基于事件驱动应用的节能TDMA协议.电子学报.2007,10,35(10),1843-1848.
[22] W Ye,J Heidemann, D Estrin. Medium access control with coordinated, adaptivesleeping for wireless sensor networks. ACM/IEEE Transactions on Networking.2004,5,12(3),493–506.
[23] W. Rabiner, Heinzelman, et al. Energy-efficient communication protocols forwireless microsensor networks. The Proceedings of Hawaii InternationalConference on System Sciences.2000,1,2(3),223.
[24] Shah, R.C., Rabaey, J.M. Energy Aware Routing for Low Energy Ad Hoc SensorNetworks. The Proceedings of2002IEEE Wireless Communications andNetworking Conference Record.2002,3,1(5),350-355.
[25] Akkaya. Kemal, Younis. Mohamed. A survey on routing protocols for wirelesssensor networks. Ad Hoc Networks.2005,5,3(3),325-349.
[26] Jin Wang, Tinghuai Ma, Jinsung Cho, et al. An Energy Efficient and LoadBalancing Routing Algorithm for Wireless Sensor Networks. Computer Scienceand Information System.2011,10,4(8),991-1007.
[27]蔡海滨,琚小明,曹奇英.多级能量异构无线传感器网络的能量预测和可靠聚簇路由协议.计算机学报.2009,12,32(12),2393-2401.
[28] Aslam Nauman, Phillips William, Robertson William, et al. A multi-criterionoptimization technique for energy efficient cluster formation in wireless sensornetworks. Information Fusion.2011,7,12(3),1847-1864.
[29] Ishmanov Farruh, Malik Aamir Saeed, Kim Sung Won. Energy consumptionbalancing (ECB) issues and mechanisms in wireless sensor networks (WSNs): acomprehensive overview. European Transactions on Telecommunications.2011,6,22(4),151-167.
[30] Wang Jun, Cao Yongtao, Xie Junyuan, et al. Energy Efficient BackoffHierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks.Journal of Computer Science and Technology.2011,3,26(2),283-291.
[31] Yu Zhenhua, Fu Xiao, Cai, Yuanli, et al. A Reliable Energy-EfficientMulti-Level Routing Algorithm for Wireless Sensor Networks Using Fuzzy PetriNets. Sensors.2011,3,11(3),3381-3400.
[32] Lee Sungryou, Choe Han, Park Byoungchang, et al. LUCA: An Energy-efficientUnequal Clustering Algorithm Using Location Information for Wireless SensorNetworks. Wireless Personal Communications2011,2,56(4),715-731.
[33] Hu Yu, Wang Xiaohui. PSO-based Energy-balanced Double Cluster-headsClustering Routing for wireless sensor networks. Proceedings of InternationalConference on Advanced in Control Engineering and Information Science.2011,10,3(4),1011-1019.
[34] Wang Shaoqing, Nie Jingnan. Energy Efficiency Optimization of CooperativeCommunication in Wireless Sensor Networks. Eurasip Journal on WirelessCommunications and Networking.2010,5,2010(3),1828-1835.
[35] Al Ameen Moshaddique, Islam S. M. Riazul, Kwak Kyungsup. Energy SavingMechanisms for MAC Protocols in Wireless Sensor Networks. InternationalJournal of Distributed Sensor Networks.2010,2010,1-6.
[36] Ruihua Zhang, Zhiping Jia, Xinshun Xu. Nodes Deployment Mechanism Basedon Energy Efficiency in Wireless Sensor Networks. International Journal ofDistributed Sensor Networks.2009,2009,99-109.
[37] Martirosyan Anahit, Boukerche Azzedine, Pazzi Richard W. Nelem.Energy-aware and quality of service-based routing in wireless sensor networksand vehicular ad hoc networks. Annales des Telecommunications-Annals ofTelecommunications.2008,11-12,63(11-12),669-4681.
[38] Su Xun. A combinatorial algorithmic approach to energy efficient informationcollection in wireless sensor networks. ACM Transactions on Sensor Networks.2007,3,3(1),1-21.
[39] Tiwari Ankit, Ballal Prasanna, Lewis Frank L. Energy-efficient wireless sensornetwork design and implementation for condition-based maintenance. ACMTransactions on Sensor Networks.2007,3,3(1),1-23.
[40] Bai Fan, Munasinghe Kumudu S, Jamalipour Abbas. Accuracy, latency, andenergy cross-optimization in wireless sensor networks through infectionspreading. International Journal of Communication Systems.2011,5,24(5),628-646.
[41] Liu Anfeng, Wu Xianyou, Chen Zhigang. Research on the energy hole problembased on unequal cluster-radius for wireless sensor networks. ComputerCommunications.2010,2,33(3),302-321.
[42] Powell Olivier, Leone Pierre, Rolim Jose. Energy optimal data propagation inwireless sensor networks. Journal of Parallel and Distributed Computing.2007,3,67(3),302-317.
[43] Ioannis C, Athanasios K, Sotiris N. Efficient data propagation strategies inwireless sensor networks using a single mobile sink. Computer Communications.2008,3,31(5),896-914.
[44] Juang Philo, Oki Hidekazu, Wang Yong, et al. Energy-efficient computing forwildlife tracking: Design tradeoffs and early experiences with ZebraNet. TheProceedings of International Conference on Architectural Support forProgramming Languages and Operating Systems-ASPLOS.2002,10,6(7),96-107.
[45] Kansal. A, Rahimi. M, Estrin. D, et al. Controlled mobility for sustainablewireless sensor networks. The Proceedings of2004First Annual IEEECommunications Society Conference on Sensor and Ad Hoc Communicationsand Networks.2004,10,4(5),1-6.
[46] Mohammad Hossein Anisi, Abdul Hanan Abdullah, Shukor Abd Razak, et al. AnOverview of Data Routing Approaches for Wireless Sensor Networks. Sensors.2012,3,12(4),3964-3996.
[47]孟中楼,王殊,王骐.分簇式无线传感器网络汇聚节点移动策略研究.华中科技大学学报(自然科学版).2009,6,37(6),67-70.
[48] Roy. Sumit, Jain. Sushant, Brunette. Waylon. Data MULEs: Modeling andanalysis of a three-tier architecture for sparse sensor networks. Ad HocNetworks.2003,9,1(2-3),215–233.
[49] Richard W. N. Pazzi, Azzedine Boukerche. Mobile data collector strategy fordelay-sensitive applications over wireless sensor networks. ComputerCommunications.2008,3,31(5),1028–1039.
[50] Wang. Z. Maria, Basagni. Stefano, Melachrinoudis. Emanuel, et al. Exploitingsink mobility for maximizing sensor networks lifetime. Proceedings of the38thAnnual Hawaii International Conference on System Sciences.2005,1,3(4),1-9.
[51]石高涛,廖明宏.传感器网络中具有负载平衡的移动协助数据收集模式.软件学报.2007,9,18(9),2235-2244.
[52] Luo J, Hubaux J. Joint mobility and routing for lifetime elongation in wirelesssensor networks. Proceedings-IEEE INFOCOM2005. The Conference onComputer Communications-24th Annual Joint Conference of the IEEEComputer and Communications Societies.2005,3,3(1),1735–1746.
[53] D. Jea,A. Somasundara,M. Srivastava. Multiple controlled mobile elements(Data Mules) for data collection in sensor networks. Proceedings-First IEEEInternational Conference, DCOSS2005.2005,6,1(1),244–257.
[54] Bi Yanzhong, Niu Jianwei, Sun Limin, et al. Moving schemes for mobile sinksin wireless sensor networks. Proceedings of the IEEE International Performance,Computing, and Communications Conference.2007,4,2(3),101-108.
[55] Boukerche. A,Pazzi. R.W.N,Araujo. R.B. HPEQ-a hierarchical periodic,event-driven and query-based wireless sensor network protocol. Proceedings ofthe IEEE Conference on Local Computer Networks-30th Anniversary.2005,11,6(7),8.
[56] Chang-Soo Ok, Seokcheon Lee, Prasenjit Mitra, etc. Distributed energybalanced routing for wireless sensor networks. Computers&IndustrialEngineering.2009,8,57(1),125-135.
[57] Braginsky. David, Estrin. Deborah. Rumor routing algorithm for sensornetworks. Proceedings of the ACM International Workshop on Wireless SensorNetworks and Applications.2002,9,8(7),22-31.
[58] Rogers. Alex, David. Esther, Jennings. Nicholas R. Self-organized routing forwireless microsensor networks. Proceedings of the IEEE Transactions onSystems, Man, and Cybernetics Part A: Systems and Humans.2005,5,5(6),349-359.
[59] Butler. Z, Rus. D. Nicholas R. Event-based motion control for mobile-sensornetworks. IEEE Pervasive Computing.2003,10-12,2(4),34-42.
[60] Sudip Misra, P. Dias Thomasinous. A simple, least-time, and energy-efficientrouting protocol with one-level data aggregation for wireless sensor networks.Journals of Systems and Software.2010,5,83(5),852-860.
[61] Bai Hongxing, Chen Xi, Li Bin, et al. A location-free algorithm ofenergy-efficient connected coverage for high density wireless sensor networks.Discrete Event Dynamic Sysytem-Theory and Applications.2007,3,17(1),1-21.
[62] Yi Gu, Qishi Wu, Nageswara S. V. Rao. Optimizing Cluster Heads for EnergyEfficiency in Large-Scale Heterogeneous Wireless Sensor Networks.International Journal of Distributed Sensor Networks.2010,2010,1-9.
[63] Wang J, Howitt I. Optimal traffic distribution in minimum energy wirelesssensor networks.2005IEEE Global Telecommunications Conference.2005,6,3274-3278.
[64]朱艺华,沈丹丹,吴万登等.无线传感器网络优化生存时间的动态路由算法.电子学报.2009,5,37(5),1041-1045.
[65] Chang J, Tassiulas L. Maximum lifetime routing in wireless sensor networks.IEEE/ACM Transaction on Networking.2004,8,12(4),609-619.
[66] Gandham. S. R, Dawande. M, Prakash. R, etc. Energy efficient schemes forwireless sensor networks with multiple mobile base stations. IEEE GlobalTelecommunications Conference.2003,12,1(3),377-381.
[67] Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application-specificprotocol architecture for wireless microsensor networks. IEEE Transactions onWireless Communications.2002,10,1(4),660-670.
[68] Yi hua Zhu, Wan deng Wu, Victor. C. M. Leung. Energy efficient tree-basedmessage ferry routing schemes for wireless sensor networks.2008ThirdInternational Conference on Communications and Networking in China(CHINACOM).2008,8,1(1),844-848.
[69] Yi hua Zhu, Wan deng Wu, Victor. C. M. Leung. Energy efficient tree-basedmessage ferry routing schemes for wireless sensor networks. Mobile Networks&Applications.2011,2,16(1),58-70.
[70]唐伟,郭伟.多基站数据聚合无线传感器网络中的最大生命期地理位置路由.通信学报.2010,10,31(10),221-228.
[71]唐伟,郭伟.多基站无线传感器网络中能量高效的基站位置优选算法.通信学报.2010,11,31(11),65-72.
[72] Zhang Qing, Xie Zhipeng, Ling Bo, et al. A Maximum Lifetime Data GatheringAlgorithm for Wireless Sensor Networks. Journal of Software.2005,11,16(11),1946-1957.
[73]唐伟,郭伟.多基站数据聚合无线传感器网络中的最大生命期路由.通信学报.2010,3,31(3),37-44.
[74]周小佳,吴侠,闫斌.基于移动基站的动态无线传感器网络.西南交通大学学报.2011,10,46(5),793-802.
[75] Aris Papadopoulos, Alfredo Navarra, JulieA.McCann, et al. VIBE: An energyefficient routing protocol for denseand mobile sensor networks. Journal ofNetwork and Computer Applications.2011,2,6(2),1946-1957.
[76]路纲,周明天,牛新征等.无线传感器网络路由协议的寿命分析.2009,2,20(2),375-393.
[77] Attea Bara'a A, Khalil Enan A. A new evolutionary based routing protocol forclustered heterogeneous wireless sensor networks. Applied Soft Computing.2012,7,12(7),1950-1957.
[78]薛伟莲,迟忠先.一种能量有效的传感器网络无碰撞路由机制.大连理工大学学报.2011,3,51(2),286-290.
[79]徐小良,裘君娜.异构传感器网络中一种能量有效的簇头选择方法.传感技术学报.2009,3,22(3),395-400.
[80] Liu Zhixin, Zheng Qingchao, Xue Liang. A distributed energy-efficientclustering algorithm with improved coverage in wireless sensor networks.Future Generation Computer Systems-the International Journal of GridComputing and Escience.2012,5,28(5),780-790.
[81]尚凤军, Mehran Abolhasan,Tadeusz Wysocki.无线传感器网络的分布式能量有效非均匀成簇算法.通信学报.2009,10,30(10),34-43.
[82] Xiang Mina, Shi Wei-rena, Jiang Chang-jianga,et al. Energy efficient clusteringalgorithm for maximizing lifetime of wireless sensor networks.AEU-International Journal of Electronics and Communications.2010,4,64(4),289-298.
[83] Wang Q, Yang W. Energy consumption model for power management in wirelesssensor networks. Proceedings of4th Annual IEEE communications societyconference on sensor, mesh and ad hoc communications and network.2003,10,1(1),237-245.
[84] Ma Yajie, Guo Yike, Ghanem Moustafa. RECA: Referenced energy-based CDSalgorithm in wireless sensor networks. International Journal of CommunicationSystems.2010,1,23(1),125-138.
[85] Dimokas N, Katsaros D, Manolopoulos Y. Energy-efficient distributed clusteringin wireless sensor networks. Journal of Parallel and Distributed Computing.2010,4,70(4),371-383.
[86] A. Bari, S.Wazed, A.Jaekel, etc. A genetic algorithm based approach for energyefficient routing in two-tiered sensor networks. Ad Hoc Networks.2009,6,7(4),665-676.
[87] G. Gupta, M. Younis. Load-balanced clustering of wireless sensor networks.IEEE International Conference on Communications.2003,5,3(1),1848-1852.
[88] G. Gupta, M. Younis. Fault-tolerant clustering of wireless sensor networks.2003IEEE Wireless Communications and Networking Conference Record.2003,3,3(1),1579-1584.
[89] G. Gupta, M. Younis. Performance evaluation of load-balanced clustering ofwireless sensor networks.10th International Conference on Telecommunications.2003,2,2(1),1577-1583.
[90] Ming Liu, Jiannong Cao, Guihai Chen, et al. An Energy-Aware Routing Protocolin Wireless Sensor Networks. International Journal of Distributed SensorNetworks.2009,2009,445-462.
[91] A. Chandrakasan, R. Min, M. Bhardwaj, etc. Power aware wireless microsensorsystems. Proceedings of the28th European Solid-State Circuit Conference.2002,9,8(2),47-54.
[92]高玮,尹志喜.现代智能仿生算法及其应用.第一版.北京：科学出版社,2011.44-88.
[93]周明,孙树栋.遗传算法原理及应用.第一版.北京：国防工业出版社,1999.1-51.
[94] Melanie M. An Introduction to Genetic Algorithm. The first edition. Cambridge:MIT Press,1999.1-23.
[95]刘勇,康立山,陈毓婷.非数值并行算法(2)—遗传算法.第一版.北京：科学出版社,1995.1-20.
[96]刘勇,康立山,陈每琉婷.演化计算.第一版.北京：清华出版社,1998.1-17.
[97]丁永生.计算智能—理论、技术与应用.第一版.北京：科学出版社,2004.129-135.
[98]段海滨,张祥银,徐春芳.仿生计算智能.第一版.北京：科学出版社,2011.190-209.
[99]褚蕾蕾,陈绥阳,周梦.计算智能的数学基础.第一版.北京：科学出版社,2002.112-143.
[100] Chen Chao, Xia Jianghai, Liu Jiangping, et al. J.D. Nonlinear inversion ofpotential-field data using a hybrid-encoding genetic algorithm. Computers andGeosciences.2006,3,32(2),230-239.
[101]杨建刚.人工神经网络使用教程.第一版.杭州：浙江大学出版社,2002.173-202.
[102]倪云竹,吕光宏,黄彦辉.多用遗传算法解决基于分条技术的磁盘负载均衡问题.计算机学报.2006,11,29(11),1995-2001.
[103][美] M. Tim Jones著,黄宽厚,尹传环,董兴业等译.人工智能.第一版.北京：电子工业出版社,2010.136-145.
[104] Jeon Geonwook, Leep, H.R., Jae Young Shim. A vehicle routing problem solvedby using a hybrid genetic algorithm. Computers&Industrial Engineering.2007,11,53(4),680-692.
[105] James T.L, Barkhi, R, Johnson, J.D. Platform impact on performance of parallelgenetic algorithms: Design and implementation considerations. EngineeringApplications of Artificial Intelligence.2006,12,19(8),843-856.
[106]黄竞伟,康立山,陈毓屏.基于遗传算法的二叉树画树算法.软件学报.2000,8,11(8),1112-1117.
[107]杨凤凤,黄海风,梁甸农.基于遗传算法的分布式星载SAR-GMTI编队优化.电子学报.2007,6,35(6),1037-1041.
[108]郑健体,吉国力,吴瑞意.基于遗传算法的通讯网络最佳Steiner树构造.厦门大学学报(自然科学版).2008,5,47(3),318-322.
[109]张信明,曾依灵,干国政.用遗传算法寻找OLSR协议的最小MPR集.软件学报.2006,4,17(4),932-938.
[110] Xiang Li, Ningchuan Xiao, Claramunt. C. Initialization strategies to enhancingthe performance of genetic algorithms for the p-median problem. Computers&Industrial Engineering.2011,11,61(4),1024-1034.
[111] Derbel. Houda, Jarboui. Bassem, Hanafi. Said. Genetic algorithm with iteratedlocal search for solving a location-routing problem. Expert Systems withApplications.2012,2,39(3),2865-2871.
[112] Lingyun Wei, Mei Zhao. A niche hybrid genetic algorithm for globaloptimization of continuous multimodal functions. Applied Mathematics andComputation.2005,1,160(3),649-661.
[113]张彤,张华,王子才.浮点数编码的遗传算法及其应用.哈尔滨工业大学学报.2000,8,32(4),59-61.
[114] Quan Yuan, Zhiqing He, Huinan Leng. A hybrid genetic algorithm for a class ofglobal optimization problems with box constraints. Applied Mathematics andComputation.2008,4,197(2),924-929.
[115] Quan Yuan, Qian Feng. A hybrid genetic algorithm for twice continuouslydifferentiable NLP problems. Computers and Chemical Engineering.2010,1,34(1),36-41.
[116]谢晓锋,张文俊,杨之廉.一种防止浮点遗传算法早熟收敛的父代选择策略.控制与决策.2002,9,17(5),625-634.
[117]都伟,韩正之.一种自适应杂交算子的浮点遗传算法.系统仿真学报.2006,6,18(6),1711-1713.
[118]汪剑鸣,许镇琳.浮点遗传算法中一种新的杂交算子.控制理论与应用.2002,12,19(6),977-980.
[119]欧松,钟慕良,徐建闽.一类高精度非线性系统参数和阶次辨识的浮点遗传算法.华南理工大学学报.1997,12,25(12),94-99.
[120]沈春华,卢晶,徐柏龄.浮点数编码的遗传算法在系统辨识中的应用.应用科学学报.2001,12,19(4),299-302.
[121]王小平,曹立明.遗传算法—理论、应用与软件实现.第一版.西安：西安交通大学出版社,2002.37-38.
[122]薛文涛,王强,吴晓蓓.基于特异性免疫策略的遗传算法及应用.系统仿真学报.2008,8,20(16),4315-4322.
[123]潘伟,王学勇,井元伟.基于遗传算法的混合H2/H∞状态反馈控制器.控制与决策.2005,2,20(2),132-136.
[124]潘伟,刁华宗,井元伟.一种改进的实数自适应遗传算法.控制与决策.2006,7,21(7),792-800.
[125]李凌晶,孙力娟,王汝传等.能量有效的无线传感器网络可信路由协议.系统工程与电子技术.2010,12,32(12),2711-2715.
[126][美]Lenoid Nison Vaserstein,Christopher Cattelier Byrne著,谢金星,姜启源,张立平等译.线性规划导论.第一版.北京：机械工业出版社,2006.1-27.
[127]张干宗.线性规划.第二版.武汉：武汉大学出版社,2004.1-25.
[128]束金龙,闻人凯.线性规划理论与模型应用.第一版.北京：科学出版社,2004.1-48.
[129] Hedetniemi. S.M., Hedetniemi. S.T.,Liestman, A.L.. A survey of gossiping andbroadcasting in communication networks. Networks.1998,12,18(4),319-349.
[130] Haas. Zygmunt J., Halpern, Joseph Y., Li. Li. Gossip-based ad hoc routing.IEEE/ACM Transactions on Networking.2006,6,14(3),479-491.
[131] Tan. H.O., Halpern, Korpeoglu. I.. Power efficient data gathering andaggregation in wireless sensor networks. SIGMOD Record.2003,12,32(4),66-71.
[132] Ghaffari Ali, Rahmani Amirmasoud, Khademzadeh Ahmad. Energy-efficientand QoS-aware geographic routing protocol for wireless sensor networks. IEICEElectronics Exoress.2011,4,8(8),582-588.
[2] G. J. Pottie, W. J. Kaiser. Wireless integrated network sensors. Communicationsof the ACM.2000,3,43(5),51-58.
[3] R. Min. Low power wireless sensor networks. The Proceedings of InternationalConference on VLSI Design.2001,1,1(1),5-21.
[4][美]S.Sitharama Lyenga, Richard R. Brooks,夏利,江汉红,魏刚,吴正国译.分布式传感器网络.版本.北京：电子工业出版社,2010.2-5.
[5]崔莉,鞠海玲,苗勇等.无线传感器网络研究进展.计算机研究与发展.2005,1,42(1),163-174.
[6] Garzas J. Joaquin Escudero, Calzon Carlos Bousono, Armada Ana Garcia. Anenergy-efficient adaptive modulation suitable for wireless sensor networks withSER and throughput constraints. Eurasip Journal on Wireless Communicationsand Networking.2007,6,2007(6),1-7.
[7]李文峰.无线传感器网络与移动机器人控制.第一版.北京：科学出版社,2009,1-2.
[8] Byrne J A.21ideas for21stCentury. Business Week.1999,8,1(8),78-167.
[9] Wade R,Mitehel W M,Ptter F. Ten Emerging Technologies that will Change theWorld. Technologies.2003,1,106(1),33-49.
[10]崔逊学,左从菊.无线传感器网络简明教程.第一版.北京：清华大学出版社,2011.1-3.
[11]张晓彤,班晓娟,段世红等.无线传感器网络与人工生命.第一版.北京：国防工业出版社,2008.2-9.
[12]王雪.无线传感器网络测量系统.第一版.北京：机械工业出版社,2007.56-401.
[13]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.软件学报.2003,10,14(10),1717-1727.
[14]王殊,胡富平,屈晓旭等.无线传感器网络的理论及应用.第一版.北京：北京航空航天大学出版社,2007.7-15.
[15]李善仓,张克旺.无线传感器网络原理与应用.第一版.北京：机械工业出版社,2008.12-29.
[16]景博,张劼,孙勇.智能网络传感器与无线传感器网络.第一版.北京：国防工业出版社,2011.29-30,402-405.
[17]彭绍亮,彭宇行等.无线传感器网络中高效传输技术.第一版.长沙：国防科技大学出版社,2010.6-7.
[18]于海滨,曾鹏.智能无线传感器网络系统.第一版.北京：科学出版社,2006.6-11.
[19] Y. Bi,L. Sun,N. Li. BoSS: a moving strategy for mobile sinks in wireless sensornetworks. International Journal of Sensor Networks.2009,6,5(3),173-184.
[20] K. Akkaya,M. Younis,M. Bangad. Sink repositioning for enhanced performancein wireless sensor networks. Computer Networks.2005,3,49(3),512–534.
[21]龚海刚,刘明,余昌远等.无线传感器网络环境下基于事件驱动应用的节能TDMA协议.电子学报.2007,10,35(10),1843-1848.
[22] W Ye,J Heidemann, D Estrin. Medium access control with coordinated, adaptivesleeping for wireless sensor networks. ACM/IEEE Transactions on Networking.2004,5,12(3),493–506.
[23] W. Rabiner, Heinzelman, et al. Energy-efficient communication protocols forwireless microsensor networks. The Proceedings of Hawaii InternationalConference on System Sciences.2000,1,2(3),223.
[24] Shah, R.C., Rabaey, J.M. Energy Aware Routing for Low Energy Ad Hoc SensorNetworks. The Proceedings of2002IEEE Wireless Communications andNetworking Conference Record.2002,3,1(5),350-355.
[25] Akkaya. Kemal, Younis. Mohamed. A survey on routing protocols for wirelesssensor networks. Ad Hoc Networks.2005,5,3(3),325-349.
[26] Jin Wang, Tinghuai Ma, Jinsung Cho, et al. An Energy Efficient and LoadBalancing Routing Algorithm for Wireless Sensor Networks. Computer Scienceand Information System.2011,10,4(8),991-1007.
[27]蔡海滨,琚小明,曹奇英.多级能量异构无线传感器网络的能量预测和可靠聚簇路由协议.计算机学报.2009,12,32(12),2393-2401.
[28] Aslam Nauman, Phillips William, Robertson William, et al. A multi-criterionoptimization technique for energy efficient cluster formation in wireless sensornetworks. Information Fusion.2011,7,12(3),1847-1864.
[29] Ishmanov Farruh, Malik Aamir Saeed, Kim Sung Won. Energy consumptionbalancing (ECB) issues and mechanisms in wireless sensor networks (WSNs): acomprehensive overview. European Transactions on Telecommunications.2011,6,22(4),151-167.
[30] Wang Jun, Cao Yongtao, Xie Junyuan, et al. Energy Efficient BackoffHierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks.Journal of Computer Science and Technology.2011,3,26(2),283-291.
[31] Yu Zhenhua, Fu Xiao, Cai, Yuanli, et al. A Reliable Energy-EfficientMulti-Level Routing Algorithm for Wireless Sensor Networks Using Fuzzy PetriNets. Sensors.2011,3,11(3),3381-3400.
[32] Lee Sungryou, Choe Han, Park Byoungchang, et al. LUCA: An Energy-efficientUnequal Clustering Algorithm Using Location Information for Wireless SensorNetworks. Wireless Personal Communications2011,2,56(4),715-731.
[33] Hu Yu, Wang Xiaohui. PSO-based Energy-balanced Double Cluster-headsClustering Routing for wireless sensor networks. Proceedings of InternationalConference on Advanced in Control Engineering and Information Science.2011,10,3(4),1011-1019.
[34] Wang Shaoqing, Nie Jingnan. Energy Efficiency Optimization of CooperativeCommunication in Wireless Sensor Networks. Eurasip Journal on WirelessCommunications and Networking.2010,5,2010(3),1828-1835.
[35] Al Ameen Moshaddique, Islam S. M. Riazul, Kwak Kyungsup. Energy SavingMechanisms for MAC Protocols in Wireless Sensor Networks. InternationalJournal of Distributed Sensor Networks.2010,2010,1-6.
[36] Ruihua Zhang, Zhiping Jia, Xinshun Xu. Nodes Deployment Mechanism Basedon Energy Efficiency in Wireless Sensor Networks. International Journal ofDistributed Sensor Networks.2009,2009,99-109.
[37] Martirosyan Anahit, Boukerche Azzedine, Pazzi Richard W. Nelem.Energy-aware and quality of service-based routing in wireless sensor networksand vehicular ad hoc networks. Annales des Telecommunications-Annals ofTelecommunications.2008,11-12,63(11-12),669-4681.
[38] Su Xun. A combinatorial algorithmic approach to energy efficient informationcollection in wireless sensor networks. ACM Transactions on Sensor Networks.2007,3,3(1),1-21.
[39] Tiwari Ankit, Ballal Prasanna, Lewis Frank L. Energy-efficient wireless sensornetwork design and implementation for condition-based maintenance. ACMTransactions on Sensor Networks.2007,3,3(1),1-23.
[40] Bai Fan, Munasinghe Kumudu S, Jamalipour Abbas. Accuracy, latency, andenergy cross-optimization in wireless sensor networks through infectionspreading. International Journal of Communication Systems.2011,5,24(5),628-646.
[41] Liu Anfeng, Wu Xianyou, Chen Zhigang. Research on the energy hole problembased on unequal cluster-radius for wireless sensor networks. ComputerCommunications.2010,2,33(3),302-321.
[42] Powell Olivier, Leone Pierre, Rolim Jose. Energy optimal data propagation inwireless sensor networks. Journal of Parallel and Distributed Computing.2007,3,67(3),302-317.
[43] Ioannis C, Athanasios K, Sotiris N. Efficient data propagation strategies inwireless sensor networks using a single mobile sink. Computer Communications.2008,3,31(5),896-914.
[44] Juang Philo, Oki Hidekazu, Wang Yong, et al. Energy-efficient computing forwildlife tracking: Design tradeoffs and early experiences with ZebraNet. TheProceedings of International Conference on Architectural Support forProgramming Languages and Operating Systems-ASPLOS.2002,10,6(7),96-107.
[45] Kansal. A, Rahimi. M, Estrin. D, et al. Controlled mobility for sustainablewireless sensor networks. The Proceedings of2004First Annual IEEECommunications Society Conference on Sensor and Ad Hoc Communicationsand Networks.2004,10,4(5),1-6.
[46] Mohammad Hossein Anisi, Abdul Hanan Abdullah, Shukor Abd Razak, et al. AnOverview of Data Routing Approaches for Wireless Sensor Networks. Sensors.2012,3,12(4),3964-3996.
[47]孟中楼,王殊,王骐.分簇式无线传感器网络汇聚节点移动策略研究.华中科技大学学报(自然科学版).2009,6,37(6),67-70.
[48] Roy. Sumit, Jain. Sushant, Brunette. Waylon. Data MULEs: Modeling andanalysis of a three-tier architecture for sparse sensor networks. Ad HocNetworks.2003,9,1(2-3),215–233.
[49] Richard W. N. Pazzi, Azzedine Boukerche. Mobile data collector strategy fordelay-sensitive applications over wireless sensor networks. ComputerCommunications.2008,3,31(5),1028–1039.
[50] Wang. Z. Maria, Basagni. Stefano, Melachrinoudis. Emanuel, et al. Exploitingsink mobility for maximizing sensor networks lifetime. Proceedings of the38thAnnual Hawaii International Conference on System Sciences.2005,1,3(4),1-9.
[51]石高涛,廖明宏.传感器网络中具有负载平衡的移动协助数据收集模式.软件学报.2007,9,18(9),2235-2244.
[52] Luo J, Hubaux J. Joint mobility and routing for lifetime elongation in wirelesssensor networks. Proceedings-IEEE INFOCOM2005. The Conference onComputer Communications-24th Annual Joint Conference of the IEEEComputer and Communications Societies.2005,3,3(1),1735–1746.
[53] D. Jea,A. Somasundara,M. Srivastava. Multiple controlled mobile elements(Data Mules) for data collection in sensor networks. Proceedings-First IEEEInternational Conference, DCOSS2005.2005,6,1(1),244–257.
[54] Bi Yanzhong, Niu Jianwei, Sun Limin, et al. Moving schemes for mobile sinksin wireless sensor networks. Proceedings of the IEEE International Performance,Computing, and Communications Conference.2007,4,2(3),101-108.
[55] Boukerche. A,Pazzi. R.W.N,Araujo. R.B. HPEQ-a hierarchical periodic,event-driven and query-based wireless sensor network protocol. Proceedings ofthe IEEE Conference on Local Computer Networks-30th Anniversary.2005,11,6(7),8.
[56] Chang-Soo Ok, Seokcheon Lee, Prasenjit Mitra, etc. Distributed energybalanced routing for wireless sensor networks. Computers&IndustrialEngineering.2009,8,57(1),125-135.
[57] Braginsky. David, Estrin. Deborah. Rumor routing algorithm for sensornetworks. Proceedings of the ACM International Workshop on Wireless SensorNetworks and Applications.2002,9,8(7),22-31.
[58] Rogers. Alex, David. Esther, Jennings. Nicholas R. Self-organized routing forwireless microsensor networks. Proceedings of the IEEE Transactions onSystems, Man, and Cybernetics Part A: Systems and Humans.2005,5,5(6),349-359.
[59] Butler. Z, Rus. D. Nicholas R. Event-based motion control for mobile-sensornetworks. IEEE Pervasive Computing.2003,10-12,2(4),34-42.
[60] Sudip Misra, P. Dias Thomasinous. A simple, least-time, and energy-efficientrouting protocol with one-level data aggregation for wireless sensor networks.Journals of Systems and Software.2010,5,83(5),852-860.
[61] Bai Hongxing, Chen Xi, Li Bin, et al. A location-free algorithm ofenergy-efficient connected coverage for high density wireless sensor networks.Discrete Event Dynamic Sysytem-Theory and Applications.2007,3,17(1),1-21.
[62] Yi Gu, Qishi Wu, Nageswara S. V. Rao. Optimizing Cluster Heads for EnergyEfficiency in Large-Scale Heterogeneous Wireless Sensor Networks.International Journal of Distributed Sensor Networks.2010,2010,1-9.
[63] Wang J, Howitt I. Optimal traffic distribution in minimum energy wirelesssensor networks.2005IEEE Global Telecommunications Conference.2005,6,3274-3278.
[64]朱艺华,沈丹丹,吴万登等.无线传感器网络优化生存时间的动态路由算法.电子学报.2009,5,37(5),1041-1045.
[65] Chang J, Tassiulas L. Maximum lifetime routing in wireless sensor networks.IEEE/ACM Transaction on Networking.2004,8,12(4),609-619.
[66] Gandham. S. R, Dawande. M, Prakash. R, etc. Energy efficient schemes forwireless sensor networks with multiple mobile base stations. IEEE GlobalTelecommunications Conference.2003,12,1(3),377-381.
[67] Heinzelman W B, Chandrakasan A P, Balakrishnan H. An application-specificprotocol architecture for wireless microsensor networks. IEEE Transactions onWireless Communications.2002,10,1(4),660-670.
[68] Yi hua Zhu, Wan deng Wu, Victor. C. M. Leung. Energy efficient tree-basedmessage ferry routing schemes for wireless sensor networks.2008ThirdInternational Conference on Communications and Networking in China(CHINACOM).2008,8,1(1),844-848.
[69] Yi hua Zhu, Wan deng Wu, Victor. C. M. Leung. Energy efficient tree-basedmessage ferry routing schemes for wireless sensor networks. Mobile Networks&Applications.2011,2,16(1),58-70.
[70]唐伟,郭伟.多基站数据聚合无线传感器网络中的最大生命期地理位置路由.通信学报.2010,10,31(10),221-228.
[71]唐伟,郭伟.多基站无线传感器网络中能量高效的基站位置优选算法.通信学报.2010,11,31(11),65-72.
[72] Zhang Qing, Xie Zhipeng, Ling Bo, et al. A Maximum Lifetime Data GatheringAlgorithm for Wireless Sensor Networks. Journal of Software.2005,11,16(11),1946-1957.
[73]唐伟,郭伟.多基站数据聚合无线传感器网络中的最大生命期路由.通信学报.2010,3,31(3),37-44.
[74]周小佳,吴侠,闫斌.基于移动基站的动态无线传感器网络.西南交通大学学报.2011,10,46(5),793-802.
[75] Aris Papadopoulos, Alfredo Navarra, JulieA.McCann, et al. VIBE: An energyefficient routing protocol for denseand mobile sensor networks. Journal ofNetwork and Computer Applications.2011,2,6(2),1946-1957.
[76]路纲,周明天,牛新征等.无线传感器网络路由协议的寿命分析.2009,2,20(2),375-393.
[77] Attea Bara'a A, Khalil Enan A. A new evolutionary based routing protocol forclustered heterogeneous wireless sensor networks. Applied Soft Computing.2012,7,12(7),1950-1957.
[78]薛伟莲,迟忠先.一种能量有效的传感器网络无碰撞路由机制.大连理工大学学报.2011,3,51(2),286-290.
[79]徐小良,裘君娜.异构传感器网络中一种能量有效的簇头选择方法.传感技术学报.2009,3,22(3),395-400.
[80] Liu Zhixin, Zheng Qingchao, Xue Liang. A distributed energy-efficientclustering algorithm with improved coverage in wireless sensor networks.Future Generation Computer Systems-the International Journal of GridComputing and Escience.2012,5,28(5),780-790.
[81]尚凤军, Mehran Abolhasan,Tadeusz Wysocki.无线传感器网络的分布式能量有效非均匀成簇算法.通信学报.2009,10,30(10),34-43.
[82] Xiang Mina, Shi Wei-rena, Jiang Chang-jianga,et al. Energy efficient clusteringalgorithm for maximizing lifetime of wireless sensor networks.AEU-International Journal of Electronics and Communications.2010,4,64(4),289-298.
[83] Wang Q, Yang W. Energy consumption model for power management in wirelesssensor networks. Proceedings of4th Annual IEEE communications societyconference on sensor, mesh and ad hoc communications and network.2003,10,1(1),237-245.
[84] Ma Yajie, Guo Yike, Ghanem Moustafa. RECA: Referenced energy-based CDSalgorithm in wireless sensor networks. International Journal of CommunicationSystems.2010,1,23(1),125-138.
[85] Dimokas N, Katsaros D, Manolopoulos Y. Energy-efficient distributed clusteringin wireless sensor networks. Journal of Parallel and Distributed Computing.2010,4,70(4),371-383.
[86] A. Bari, S.Wazed, A.Jaekel, etc. A genetic algorithm based approach for energyefficient routing in two-tiered sensor networks. Ad Hoc Networks.2009,6,7(4),665-676.
[87] G. Gupta, M. Younis. Load-balanced clustering of wireless sensor networks.IEEE International Conference on Communications.2003,5,3(1),1848-1852.
[88] G. Gupta, M. Younis. Fault-tolerant clustering of wireless sensor networks.2003IEEE Wireless Communications and Networking Conference Record.2003,3,3(1),1579-1584.
[89] G. Gupta, M. Younis. Performance evaluation of load-balanced clustering ofwireless sensor networks.10th International Conference on Telecommunications.2003,2,2(1),1577-1583.
[90] Ming Liu, Jiannong Cao, Guihai Chen, et al. An Energy-Aware Routing Protocolin Wireless Sensor Networks. International Journal of Distributed SensorNetworks.2009,2009,445-462.
[91] A. Chandrakasan, R. Min, M. Bhardwaj, etc. Power aware wireless microsensorsystems. Proceedings of the28th European Solid-State Circuit Conference.2002,9,8(2),47-54.
[92]高玮,尹志喜.现代智能仿生算法及其应用.第一版.北京：科学出版社,2011.44-88.
[93]周明,孙树栋.遗传算法原理及应用.第一版.北京：国防工业出版社,1999.1-51.
[94] Melanie M. An Introduction to Genetic Algorithm. The first edition. Cambridge:MIT Press,1999.1-23.
[95]刘勇,康立山,陈毓婷.非数值并行算法(2)—遗传算法.第一版.北京：科学出版社,1995.1-20.
[96]刘勇,康立山,陈每琉婷.演化计算.第一版.北京：清华出版社,1998.1-17.
[97]丁永生.计算智能—理论、技术与应用.第一版.北京：科学出版社,2004.129-135.
[98]段海滨,张祥银,徐春芳.仿生计算智能.第一版.北京：科学出版社,2011.190-209.
[99]褚蕾蕾,陈绥阳,周梦.计算智能的数学基础.第一版.北京：科学出版社,2002.112-143.
[100] Chen Chao, Xia Jianghai, Liu Jiangping, et al. J.D. Nonlinear inversion ofpotential-field data using a hybrid-encoding genetic algorithm. Computers andGeosciences.2006,3,32(2),230-239.
[101]杨建刚.人工神经网络使用教程.第一版.杭州：浙江大学出版社,2002.173-202.
[102]倪云竹,吕光宏,黄彦辉.多用遗传算法解决基于分条技术的磁盘负载均衡问题.计算机学报.2006,11,29(11),1995-2001.
[103][美] M. Tim Jones著,黄宽厚,尹传环,董兴业等译.人工智能.第一版.北京：电子工业出版社,2010.136-145.
[104] Jeon Geonwook, Leep, H.R., Jae Young Shim. A vehicle routing problem solvedby using a hybrid genetic algorithm. Computers&Industrial Engineering.2007,11,53(4),680-692.
[105] James T.L, Barkhi, R, Johnson, J.D. Platform impact on performance of parallelgenetic algorithms: Design and implementation considerations. EngineeringApplications of Artificial Intelligence.2006,12,19(8),843-856.
[106]黄竞伟,康立山,陈毓屏.基于遗传算法的二叉树画树算法.软件学报.2000,8,11(8),1112-1117.
[107]杨凤凤,黄海风,梁甸农.基于遗传算法的分布式星载SAR-GMTI编队优化.电子学报.2007,6,35(6),1037-1041.
[108]郑健体,吉国力,吴瑞意.基于遗传算法的通讯网络最佳Steiner树构造.厦门大学学报(自然科学版).2008,5,47(3),318-322.
[109]张信明,曾依灵,干国政.用遗传算法寻找OLSR协议的最小MPR集.软件学报.2006,4,17(4),932-938.
[110] Xiang Li, Ningchuan Xiao, Claramunt. C. Initialization strategies to enhancingthe performance of genetic algorithms for the p-median problem. Computers&Industrial Engineering.2011,11,61(4),1024-1034.
[111] Derbel. Houda, Jarboui. Bassem, Hanafi. Said. Genetic algorithm with iteratedlocal search for solving a location-routing problem. Expert Systems withApplications.2012,2,39(3),2865-2871.
[112] Lingyun Wei, Mei Zhao. A niche hybrid genetic algorithm for globaloptimization of continuous multimodal functions. Applied Mathematics andComputation.2005,1,160(3),649-661.
[113]张彤,张华,王子才.浮点数编码的遗传算法及其应用.哈尔滨工业大学学报.2000,8,32(4),59-61.
[114] Quan Yuan, Zhiqing He, Huinan Leng. A hybrid genetic algorithm for a class ofglobal optimization problems with box constraints. Applied Mathematics andComputation.2008,4,197(2),924-929.
[115] Quan Yuan, Qian Feng. A hybrid genetic algorithm for twice continuouslydifferentiable NLP problems. Computers and Chemical Engineering.2010,1,34(1),36-41.
[116]谢晓锋,张文俊,杨之廉.一种防止浮点遗传算法早熟收敛的父代选择策略.控制与决策.2002,9,17(5),625-634.
[117]都伟,韩正之.一种自适应杂交算子的浮点遗传算法.系统仿真学报.2006,6,18(6),1711-1713.
[118]汪剑鸣,许镇琳.浮点遗传算法中一种新的杂交算子.控制理论与应用.2002,12,19(6),977-980.
[119]欧松,钟慕良,徐建闽.一类高精度非线性系统参数和阶次辨识的浮点遗传算法.华南理工大学学报.1997,12,25(12),94-99.
[120]沈春华,卢晶,徐柏龄.浮点数编码的遗传算法在系统辨识中的应用.应用科学学报.2001,12,19(4),299-302.
[121]王小平,曹立明.遗传算法—理论、应用与软件实现.第一版.西安：西安交通大学出版社,2002.37-38.
[122]薛文涛,王强,吴晓蓓.基于特异性免疫策略的遗传算法及应用.系统仿真学报.2008,8,20(16),4315-4322.
[123]潘伟,王学勇,井元伟.基于遗传算法的混合H2/H∞状态反馈控制器.控制与决策.2005,2,20(2),132-136.
[124]潘伟,刁华宗,井元伟.一种改进的实数自适应遗传算法.控制与决策.2006,7,21(7),792-800.
[125]李凌晶,孙力娟,王汝传等.能量有效的无线传感器网络可信路由协议.系统工程与电子技术.2010,12,32(12),2711-2715.
[126][美]Lenoid Nison Vaserstein,Christopher Cattelier Byrne著,谢金星,姜启源,张立平等译.线性规划导论.第一版.北京：机械工业出版社,2006.1-27.
[127]张干宗.线性规划.第二版.武汉：武汉大学出版社,2004.1-25.
[128]束金龙,闻人凯.线性规划理论与模型应用.第一版.北京：科学出版社,2004.1-48.
[129] Hedetniemi. S.M., Hedetniemi. S.T.,Liestman, A.L.. A survey of gossiping andbroadcasting in communication networks. Networks.1998,12,18(4),319-349.
[130] Haas. Zygmunt J., Halpern, Joseph Y., Li. Li. Gossip-based ad hoc routing.IEEE/ACM Transactions on Networking.2006,6,14(3),479-491.
[131] Tan. H.O., Halpern, Korpeoglu. I.. Power efficient data gathering andaggregation in wireless sensor networks. SIGMOD Record.2003,12,32(4),66-71.
[132] Ghaffari Ali, Rahmani Amirmasoud, Khademzadeh Ahmad. Energy-efficientand QoS-aware geographic routing protocol for wireless sensor networks. IEICEElectronics Exoress.2011,4,8(8),582-588.