无线移动自组织及传感器网络中若干问题的研究
|
摘要
随着现代科学技术的不断发展,人们对网络通信技术的需求也不断提升,新的应用不断涌现,传统网络已经不能满足社会的需求,从而促进了无线网络技术的快速发展。
无线自组织网络由于其具有自组织、自愈合、快速反应等特性,在各行各业中的应用日益广泛。特别是近来人们关注较多的车载移动自组织网络、无人机载自组织网络等,有重大的理论和现实意义。无线传感器网络作为一种特殊的自组织网络,已经在军事、交通监控、安全敏感区域监控、智能家居和目标跟踪、公共安全检测等领域有广泛的应用前景。因此无线移动组织网络及传感器网络成为了无线自组织网络技术研究中的热点问题,由于其特殊性,因此具有一定的研究挑战。
本文在对无线自组织及传感器网络进行了系统全面的分析和总结基础上,对研究热点问题中的延迟容忍移动传感器网络数据传输策略、有向无线传感器网络覆盖问题及移动自组织网络路由等几个方面的内容深入细致地开展了相关研究工作,并取得了若干的创新与成果。本文的主要贡献包括:
1.针对延迟容忍移动传感器网络的特点,对其现有的数据传输策略进行了深入的研究,并在经典延迟容忍移动传感器网络数据传输策略FAD以及MPAD策略的基础上先后提出了一种基于优先度复制的数据传输策略PRD用于空间中间断连通的延迟容忍移动传感器网络数据传输以及一种基于数据融合的适配数据复制传输策略DAAD,该传输策略主要用于采集监控环境数据的延迟容忍移动传感器网络。PRD由选择复制策略和队列管理组成,前者根据节点将消息传递给汇聚点的可能性,选择下一跳进行复制传输;队列管理则利用引入传输优先度及复制数的消息生存时间决定队列中消息丢弃原则。DAAD由选择复制策略和具有数据融合功能的队列管理组成,前者适应性的选择下一跳传输的节点,复制消息进行数据传输;队列管理则决定队列中消息的生存与否等,同时利用数据融合,产生新消息,减少网络中多余数据消息的传输。仿真分析表明,PRD以及DAAD均能够有效提高数据交付率,同时具有较低的数据复制数以及传输延迟。
2.针对有向传感器网络覆盖增强问题,提出了基于人工鱼群优化的有向传感网络覆盖增强算法AFCEA以及基于遗传模拟退火算法的有向传感器网络覆盖增强算法GSACEA。在AFCEA中,将针对性改进的人工鱼群优化引入可转动模型下有向传感器网络覆盖增强问题最优解的求解,通过多条人工鱼的一系列行为,逐步接近网络覆盖的最大值。通过理论研究以及仿真实验分析,与其他典型的有向传感器网络覆盖增强算法相比,AFCEA算法可以以较少的迭代计算次数有效提高有向传感器网络的监测覆盖率。GSACEA算法则将遗传算法与模拟退火算法相结合并作了一定的改变,通过遗传的进化作用与模拟退火的作用逐步接近最优解,解决有向传感器网络覆盖增强问题。仿真验证表明,与其他经典的同类研究算法相比,GSACEA可以以较少的迭代次数有效提高有向传感器网络监测覆盖率。
3.在人工鱼群算法的基础上,创新性地提出了虚拟水流的概念,分析了虚拟水流对人工鱼群算法的影响,进而提出了一种基于虚拟水流人工鱼群优化的有向传感网络覆盖增强算法VSAFCEA。在VSAFCEA中,将引入虚拟水流并重新定义行为模式的人工鱼群优化引入可转动模型下有向传感器网络覆盖增强问题最优解的求解,通过多条人工鱼在虚拟水流下的一系列行为,逐步接近网络覆盖的最大值。通过理论研究以及仿真实验分析,与其他有向传感器网络覆盖增强算法,包括AFCEA相比,VSAFCEA算法可以以较少的迭代计算次数有效提高有向传感器网络的监测覆盖率。
4.对无人机自组织网络等高速移动环境下的无线移动自组织网络特性进行了相关分析,针对节点移动快速、网络拓扑变化剧烈等特性,提出了一种上下文感知优化链路状态协议CAOLSR,采用了一种上下文信息机制,引入了节点地理信息位置等信息,并综合多个网络上下文影响参数,将节点间相对移动预测、前后访问时间以及节点连接度情况引入MPR选择,并设计了CAOLSR-MPR算法流程。此外,CAOLSR还通过引入Fisheye技术,分层次对传输控制消息的发送频率进行了控制,减少了移动性对路由精度的影响。最后通过多组仿真实验表明,在节点快速移动与拓扑快速变化环境下与HOLSR、OLSR、DSDV相比,CAOLSR具有更为良好的性能。
在本文的最后,总结了全文的工作,并对未来的研究工作进行了展望。
With the development of modern network technology, the requirements of human society for communication technology in usual life and new applications are proposed unceasingly. Traditionary networks can’t fit the requirements of modern society, so the wireless networks are developed rapidly.
Wireless Ad Hoc networks are applicated in our society more and more, because they have some characteristics, such as self-organization, self-concrescence and rapid reaction. Wireless Ad Hoc networks have very important significance in theory research and application, especially in some domains these we put more attentions, such as vehicular mobile Ad Hoc networks and Unmanned Aerial Vehicles networks. As a special Ad Hoc network, wireless sensor networks have many applications in military affairs, traffic, special area stakeout, intelligence family, target tracking and public security. So wireless mobile Ad Hoc networks and sensor networks are two hot research domains in wireless Ad Hoc networks, for these special characteristics, there are some research challenges.
Based on a systematical summary of wireless Ad Hoc networks and sensor networks, this dissertation focuses on some related sub-researches, such as data delivery scheme of delay tolerant mobile sensor networks, coverage-enhancing of directed sensor networks and routing of mobile Ad Hoc networks , and gains several innovations and achievements. Our research works and contributions of this dissertation are summarized as follows:
1. In this dissertation, we propose a new data delivery scheme-PRD (Priority Replication Delivery scheme) for pervasive data gathering in DTMSN (Delay Tolerant Mobile Sensor Networks) that network with intermittent connectivity in space, and we propose another scheme-DAAD(Data Aggregation-based Adaptive Data Reproductive Delivery Scheme) for environment monitoring in DTMSN. PRD consists of two key components for data transmission and queue management, respectively. The former makes decisions on when and where to transmit data messages according to the node delivery probability. The latter employs the message survival time based on priority and delivery copies to decide dropping for minimizing transmission overhead. DAAD consists of data transmission scheme and queue management based on data fusion, respectively. The former makes decisions on when and where to transmit data messages. The latter manages the message queue based on data. Simulation results show that the proposed two data delivery schemes achieve the higher message delivery ratio with the lower transmission overhead and data delivery delay than other DTMSN data delivering approaches.
2. In this dissertation, we propose an AFCEA (Artificial Fish-swarm based Coverage-Enhancing Algorithm) and a GSACEA (Genetic Simalated Annealing based Coverage-Enhancing Algorithm) for the problem of coverage-enhangcing in directed sensor networks. Directed sensor is a kind of directional system, and its coverage is different with omni-directional system. In AFCEA, we use our improved optimization algorithm into the solution for coverage-enhancing in directed sensor networks with rotational direction model. By analysis and some simulations, and compare AFCEA to other classic directed sensor networks coverage-enhancing algorithm, AFCEA can achieve higher directional sensor networks coverage with lesser iterative computing times. GSACEA combine the genetic algorithm and the simulated annealing algorithm into the solution for coverage-enhancing in directed sensor networks. By simulations, and compare to other classic directed sensor networks coverage-enhancing algorithm, GSACEA can achieve higher directional sensor networks coverage with lesser iterative computing times too.
3. Based on artificial fish-swarm algorithm, in this dissertation we propose a concept named virtual stream, and analyse virtual stream’s influence on artificial fish-swarm algorithm, then we propose a VSAFCEA (Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm). In VSAFCEA, we use our improved artificial fish-swarm optimization algorithm with virtual stream into the solution for coverage-enhancing in directed sensor networks with rotational direction model. By analysis and some simulations, and compare VSAFCEA to other classic directed sensor networks coverage-enhancing algorithm and AFCEA, VSAFCEA can achieve higher directional sensor networks coverage with lesser iterative computing times.
4. In this dissertation, a Context-aware optimized link state routing protocol for networks with fast-moving nodes is proposed; CAOLSR (Context-aware Optimized Link State Routing Protocol) adopts a context-aware mechanism, and selects MPR (Multi Point Relays) based on relative movement of nodes, recent access-time and connection number of nodes, and adopts a special flow of MPR selection. In addition, by the introduction of Fisheye, reduces the influence from mobility on the routing accuracy. Experimental results have shown that CAOLSR can achieve good performance and outperform HOLSR (Hierarchical Optimized Link State Routing Protocol), OLSR (Optimized Link State Routing Protocol) and DSDV (Destination Sequenced Distance Vector) in networks with fast-moving nodes.
Summaries and prospects have also been put forward in the final of this dissertation.
无线自组织网络由于其具有自组织、自愈合、快速反应等特性,在各行各业中的应用日益广泛。特别是近来人们关注较多的车载移动自组织网络、无人机载自组织网络等,有重大的理论和现实意义。无线传感器网络作为一种特殊的自组织网络,已经在军事、交通监控、安全敏感区域监控、智能家居和目标跟踪、公共安全检测等领域有广泛的应用前景。因此无线移动组织网络及传感器网络成为了无线自组织网络技术研究中的热点问题,由于其特殊性,因此具有一定的研究挑战。
本文在对无线自组织及传感器网络进行了系统全面的分析和总结基础上,对研究热点问题中的延迟容忍移动传感器网络数据传输策略、有向无线传感器网络覆盖问题及移动自组织网络路由等几个方面的内容深入细致地开展了相关研究工作,并取得了若干的创新与成果。本文的主要贡献包括:
1.针对延迟容忍移动传感器网络的特点,对其现有的数据传输策略进行了深入的研究,并在经典延迟容忍移动传感器网络数据传输策略FAD以及MPAD策略的基础上先后提出了一种基于优先度复制的数据传输策略PRD用于空间中间断连通的延迟容忍移动传感器网络数据传输以及一种基于数据融合的适配数据复制传输策略DAAD,该传输策略主要用于采集监控环境数据的延迟容忍移动传感器网络。PRD由选择复制策略和队列管理组成,前者根据节点将消息传递给汇聚点的可能性,选择下一跳进行复制传输;队列管理则利用引入传输优先度及复制数的消息生存时间决定队列中消息丢弃原则。DAAD由选择复制策略和具有数据融合功能的队列管理组成,前者适应性的选择下一跳传输的节点,复制消息进行数据传输;队列管理则决定队列中消息的生存与否等,同时利用数据融合,产生新消息,减少网络中多余数据消息的传输。仿真分析表明,PRD以及DAAD均能够有效提高数据交付率,同时具有较低的数据复制数以及传输延迟。
2.针对有向传感器网络覆盖增强问题,提出了基于人工鱼群优化的有向传感网络覆盖增强算法AFCEA以及基于遗传模拟退火算法的有向传感器网络覆盖增强算法GSACEA。在AFCEA中,将针对性改进的人工鱼群优化引入可转动模型下有向传感器网络覆盖增强问题最优解的求解,通过多条人工鱼的一系列行为,逐步接近网络覆盖的最大值。通过理论研究以及仿真实验分析,与其他典型的有向传感器网络覆盖增强算法相比,AFCEA算法可以以较少的迭代计算次数有效提高有向传感器网络的监测覆盖率。GSACEA算法则将遗传算法与模拟退火算法相结合并作了一定的改变,通过遗传的进化作用与模拟退火的作用逐步接近最优解,解决有向传感器网络覆盖增强问题。仿真验证表明,与其他经典的同类研究算法相比,GSACEA可以以较少的迭代次数有效提高有向传感器网络监测覆盖率。
3.在人工鱼群算法的基础上,创新性地提出了虚拟水流的概念,分析了虚拟水流对人工鱼群算法的影响,进而提出了一种基于虚拟水流人工鱼群优化的有向传感网络覆盖增强算法VSAFCEA。在VSAFCEA中,将引入虚拟水流并重新定义行为模式的人工鱼群优化引入可转动模型下有向传感器网络覆盖增强问题最优解的求解,通过多条人工鱼在虚拟水流下的一系列行为,逐步接近网络覆盖的最大值。通过理论研究以及仿真实验分析,与其他有向传感器网络覆盖增强算法,包括AFCEA相比,VSAFCEA算法可以以较少的迭代计算次数有效提高有向传感器网络的监测覆盖率。
4.对无人机自组织网络等高速移动环境下的无线移动自组织网络特性进行了相关分析,针对节点移动快速、网络拓扑变化剧烈等特性,提出了一种上下文感知优化链路状态协议CAOLSR,采用了一种上下文信息机制,引入了节点地理信息位置等信息,并综合多个网络上下文影响参数,将节点间相对移动预测、前后访问时间以及节点连接度情况引入MPR选择,并设计了CAOLSR-MPR算法流程。此外,CAOLSR还通过引入Fisheye技术,分层次对传输控制消息的发送频率进行了控制,减少了移动性对路由精度的影响。最后通过多组仿真实验表明,在节点快速移动与拓扑快速变化环境下与HOLSR、OLSR、DSDV相比,CAOLSR具有更为良好的性能。
在本文的最后,总结了全文的工作,并对未来的研究工作进行了展望。
With the development of modern network technology, the requirements of human society for communication technology in usual life and new applications are proposed unceasingly. Traditionary networks can’t fit the requirements of modern society, so the wireless networks are developed rapidly.
Wireless Ad Hoc networks are applicated in our society more and more, because they have some characteristics, such as self-organization, self-concrescence and rapid reaction. Wireless Ad Hoc networks have very important significance in theory research and application, especially in some domains these we put more attentions, such as vehicular mobile Ad Hoc networks and Unmanned Aerial Vehicles networks. As a special Ad Hoc network, wireless sensor networks have many applications in military affairs, traffic, special area stakeout, intelligence family, target tracking and public security. So wireless mobile Ad Hoc networks and sensor networks are two hot research domains in wireless Ad Hoc networks, for these special characteristics, there are some research challenges.
Based on a systematical summary of wireless Ad Hoc networks and sensor networks, this dissertation focuses on some related sub-researches, such as data delivery scheme of delay tolerant mobile sensor networks, coverage-enhancing of directed sensor networks and routing of mobile Ad Hoc networks , and gains several innovations and achievements. Our research works and contributions of this dissertation are summarized as follows:
1. In this dissertation, we propose a new data delivery scheme-PRD (Priority Replication Delivery scheme) for pervasive data gathering in DTMSN (Delay Tolerant Mobile Sensor Networks) that network with intermittent connectivity in space, and we propose another scheme-DAAD(Data Aggregation-based Adaptive Data Reproductive Delivery Scheme) for environment monitoring in DTMSN. PRD consists of two key components for data transmission and queue management, respectively. The former makes decisions on when and where to transmit data messages according to the node delivery probability. The latter employs the message survival time based on priority and delivery copies to decide dropping for minimizing transmission overhead. DAAD consists of data transmission scheme and queue management based on data fusion, respectively. The former makes decisions on when and where to transmit data messages. The latter manages the message queue based on data. Simulation results show that the proposed two data delivery schemes achieve the higher message delivery ratio with the lower transmission overhead and data delivery delay than other DTMSN data delivering approaches.
2. In this dissertation, we propose an AFCEA (Artificial Fish-swarm based Coverage-Enhancing Algorithm) and a GSACEA (Genetic Simalated Annealing based Coverage-Enhancing Algorithm) for the problem of coverage-enhangcing in directed sensor networks. Directed sensor is a kind of directional system, and its coverage is different with omni-directional system. In AFCEA, we use our improved optimization algorithm into the solution for coverage-enhancing in directed sensor networks with rotational direction model. By analysis and some simulations, and compare AFCEA to other classic directed sensor networks coverage-enhancing algorithm, AFCEA can achieve higher directional sensor networks coverage with lesser iterative computing times. GSACEA combine the genetic algorithm and the simulated annealing algorithm into the solution for coverage-enhancing in directed sensor networks. By simulations, and compare to other classic directed sensor networks coverage-enhancing algorithm, GSACEA can achieve higher directional sensor networks coverage with lesser iterative computing times too.
3. Based on artificial fish-swarm algorithm, in this dissertation we propose a concept named virtual stream, and analyse virtual stream’s influence on artificial fish-swarm algorithm, then we propose a VSAFCEA (Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm). In VSAFCEA, we use our improved artificial fish-swarm optimization algorithm with virtual stream into the solution for coverage-enhancing in directed sensor networks with rotational direction model. By analysis and some simulations, and compare VSAFCEA to other classic directed sensor networks coverage-enhancing algorithm and AFCEA, VSAFCEA can achieve higher directional sensor networks coverage with lesser iterative computing times.
4. In this dissertation, a Context-aware optimized link state routing protocol for networks with fast-moving nodes is proposed; CAOLSR (Context-aware Optimized Link State Routing Protocol) adopts a context-aware mechanism, and selects MPR (Multi Point Relays) based on relative movement of nodes, recent access-time and connection number of nodes, and adopts a special flow of MPR selection. In addition, by the introduction of Fisheye, reduces the influence from mobility on the routing accuracy. Experimental results have shown that CAOLSR can achieve good performance and outperform HOLSR (Hierarchical Optimized Link State Routing Protocol), OLSR (Optimized Link State Routing Protocol) and DSDV (Destination Sequenced Distance Vector) in networks with fast-moving nodes.
Summaries and prospects have also been put forward in the final of this dissertation.
引文
[1] Toh, C.-K. Ad Hoc mobile wireless networks: protocols and systems. Prentice Hall, 2001
[2] Abolhasan M, Wysocki T, Dutkiewicz E, Abolhasan M. A review of routing protocols for mobile ad hoc networks. Ad Hoc Networks, 2004,2:1-22
[3] Mian A.N, Baldoni R, Beraldi R .A survey of service discovery protocols in multihop mobile Ad Hoc networks. IEEE Pervasive Computing, 2009,8:66-74
[4] Ververidis C.N, Polyzos G.C. Service discovery for mobile Ad Hoc networks: a survey of issues and techniques. IEEE Communications Surveys & Tutorials, 2008,10(3):30-45
[5] Michele Nogueira Lima, Aldri Luiz dos Santos, Guy Pujolle. A survey of survivability in mobile Ad Hoc Networks. IEEE Communications Surveys &tutorials, 2009,11:66-77
[6] Luo JH, Ye DX, Xue L, Fan MY. A survey of multicast routing protocols for mobile Ad-Hoc networks. IEEE Communications Surveys &tutorials, 2009,11(1):78-91
[7]尹长青.无线自组网络若干技术的研究:[博士学位论文].上海:复旦大学,2004
[8] D.Estrin, R. Govindan, J. Heidemann, etal. Next century challenges: scalable coordination in sensor networks. Proc. 5th ACM/IEEE International Conference on Mobile Computing and Networking. Seattle, WA, USA, 1999:263-270
[9] I.F.Akyildiz, W.Su, Y.Sankarasubramaniam, etal. Wireless sensor networks: a survey. Computer Networks, 2002, 38: 393-422
[10] S.Kumar, F.Zhao, D.Shepherd. Collaborative signal and information processing in microsensor networks. IEEE Signal Processing Magazine, 2002,19(2):13-14
[11] C. Y Chong, S. Kumar. Sensor networks evolution, opportunities, and challenges. Proceedings of the IEEE, 2003, 91(8): 1247-1256
[12]孙利民,李建中,陈渝,等.无线传感器网络.北京:清华大学出版社,2005
[13] G J. Pottie, W. J. Kaiser. Embedding the Internet: wireless integrated network sensors. Communications of the ACM, 2000, 43(5): 51-58
[14]任丰原,黄海宁,林闯.无线传感器网络.软件学报, 2003,14(7):1282-1291
[15]马华东,陶丹.多媒体传感器网络及其研究进展.软件学报, 2006,17(9) 4-6
[16]杨少军.无线传感器网络若干关键技术研究:[博士学位论文].西安:西北工业大学,2006
[17]王媛丽.无线传感器网络中路由相关的若干问题的研究:[博士学位论文].长沙:国防科技大学,2006
[18]崔逊学,赵湛,王成编.无线传感器网络的领域应用与设计技术.北京:国防工业出版社, 2009
[19]张可,张伟.地质灾害地区多媒体传感器网络智能应用关键技术分析.综合电子系统技术教育部重点实验室2009学术年会,2009:174-177
[20]孔宁.物联网资源寻址关键技术研究:[博士学位论文].北京:中国科学院研究生院(计算机网络信息中心), 2008
[21]周伯生,吴介一,张飒兵. MANET路由协议研究进展.计算机研究与发展, 2002, 39(10):1165-1177
[22] M R Pearlman, Z J Haas. Determining the optimal configuration for the zone routing protocol. IEEE Journal on Selected Areas in Communications (Special Issue on Wireless Ad Hoc Networks), 1999, 17(8): 1395~1414
[23]曹英烈.移动Ad hoc网络路由算法研究:[博士学位论文].广州:华南理工大学,2006
[24] Chlamtac I, Syrotiuk V R, Woodward B A. A distance routing effect algorithm for mobility(DREAM).Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking(MOBICOM). Dallas:IEEE Inc,1998,76-84
[25] Vaidya N H. Location-Aided Routing(LAR)in mobile Ad Hoc networks. Proceedings of the ACM/IEEE Intemational Conference on Mobile Computing and Networking (MOBICOM). Dallas:IEEE Inc,1998,66-75
[26] K Fall. A delay-tolerant network architecture for challenged internets. In Proceedings of ACM SIGCOMM 2003 Conference on Computer Communications. Karlsruhe:ACM Press, 2003,27-34
[27] Scott Burleigh, Adrian Hooke, Leigh Torgerson, etal. Delay-Tolerant Networking: An approach to interplanetary Internet. IEEE Communications Magazine, 2003,6:128-136
[28]郑炜,王澄.延迟容忍网络中的路由算法研究.信息技术, 2008,7:68-70
[29]朱金奇,刘明,龚海刚,等.延迟容忍移动传感器网络中基于选择复制的数据传输.软件学报, 2009,20(8):2227-2240
[30] A Mainwaring, J Polastre, R Szewczyk, etal. Wireless sensor networks for habitat monitoring. In Proc. of ACM International Workshop on Wireless Sensor Networks and Applications Atlanta: ACM Press,2002,88-97
[31] Dermot G,Vinny C, Stephen F. Sensor networking with delay tolerance (SENDT), 2006, http://down.dsg.cs.tcd.ie/sendt/
[32] R C Shah, S Roy, S Jain, etal. Data MULEs: modeling a three-tier architecture for sparse sensor networks. In Proc. of the First International Workshop on Sensor Network Protocols and Applications. Anchorage: IEEE Computer Society Press, 2003,30-41
[33] Philo J, Hidekazu O, Yong W. Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet. ACM Operating System Review, 2002, 36(5):96-107
[34] T Small, Z J Haas. The shared wireless infostation model– a new Ad Hoc networking paradigm (or where there is a whale, there is a Way). In Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC’03). Annapolis: ACM Press, 2003,233-244
[35] D L. Hall and J.Linas. Handbook of multi sensor data fusion. CRC Press, 2001
[36]宋吉鹏.无线多媒体传感器网络数据融合路由及仿真平台研究:[硕士学位论文].成都:电子科技大学,2009
[37] Krishnamachari B, Estrin D, Wicker S. Modeling data-centric routing in wireless sensor networks. USC Computer Engineering Technical Report CENG, 2002,02-14
[38] Ian F. Akyildiz, Tommaso Melodia, Kaushik R.Chowdhury. A survey on wireless multimedia sensor networks. Computer Networks, 2007,51:921-960
[39]陶丹,马华东.视频传感器网络中基于相关性图像融合算法.计算机辅助设计与图形学学报, 2007,19(5):656-666
[40] Ian F. Akyildiz, Tommaso Melodia, Kaushik R. Chowdhury. Wireless multimedia sensor networks: applications and testbeds. Proceedings of the IEEE, 2008 ,96(10):1588-1605
[41]蒋杰.无线传感器网络覆盖控制研究:[博士学位论文].长沙:国防科学技术大学,2005
[42]任颜,张思东,张宏科.无线传感器网络中覆盖控制理论与算法.软件学报, 2006, 17(3):422-433
[43]朱寅寅.无线传感器网络覆盖优化方法研究:[硕士学位论文].南京:南京理工大学,2009
[44] Slijepcevic S, Potkonjak M. Power efficient organization of wireless sensor networks. Proc.of the IEEE Int'1 Conf.on Communications (ICC). Helsinki: IEEE Press, 2001,472-476
[45] Veltri G, Huang Q, Qu G, etal. Minimal and maximal exposure path algorithms for wireless embedded sensor networks. Proc.of the ACM Int'1 Conf. Embedded Networked Sensor Systems (SenSys). New York: ACM Press, 2003,40-50
[46] Adlakha S, Srivastava M. Critical density thresholds for coverage in wireless sensor networks. Proc. of the IEEE Wireless Communications and Networking (WCNC). New Orleans: IEEE Press, 2003,1615-1620
[47] Y Wang, F Lin, H Wu. Poster: efficient data transmission in delay fault tolerant mobile sensor networks (DFT-MSN). In Proceedings of IEEE International Conference on Network Protocols (ICNP’05).Boston: IEEE Press, 2005, In proceedings CD
[48] J. Leguay, T. Friedman, V. Conan. DTN Routing in a Mobility Pattern Space. In Proceedings of ACM SIGCOMM’05 workshop on Delay Tolerant Networking and Related Topics. Philadelphia: ACM Press, 2005,276-283
[49]朱金奇.延迟容忍无线传感器网络中的动态数据收集技术及其研究:[博士学位论文].成都:电子科技大学, 2009
[50]周虹宇,周激流,林锋.一种容延迟移动传感器网络中的代码分发机制.四川大学学报(自然科学版), 2008,45(5):1089-1094
[51]朱金奇,刘明,许富龙.延迟容忍移动无线传感器网络路由分析.计算机工程与应用, 2009,45(6):13-15
[52] Y Wang , H Wu. Delay/Fault-Tolerant Mobile Sensor Network (DFT-MSN): a new paradigm for pervasive information gathering. IEEE Transactions on Mobile Computing, 2006, 6(8):1021-1034
[53] Amin Vahdat, David Becker. Epidemic routing for partially connected ad hoc networks. Technical Report CS-200006, Duke University, 2000
[54] Y Wang, H Y Wu. Replication-Based efficient data delivery scheme (RED) for Delay/Fault-Tolerant mobile sensor network (DFT-MSN). In Proc. Of Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops. Pisa: IEEE Press, 2006, 485-489
[55] Y Wang, H Y Wu, H Dang. Analytic study of Delay/Fault-Tolerant mobile sensor networks (DFT-MSN’s). Tech Report, Lafayette: CACS, University of Louisiana at Lafayette, 2006
[56] Jinqi Zhu, Jiannong Cao, Ming Liu, etal. Mobility Prediction-based Adaptive Data Gathering Protocol. In Proc. of IEEE Global Telecommunications Conference. New Orleans:IEEE Press, 2008,1-5
[57]熊永平,孙利民,牛建伟,等.机会网络.软件学报, 2009,20(1): 124?137
[58]陶丹,马华东,刘亮.基于虚拟势场的有向传感器网络覆盖增强算法.软件学报, 2007,3:1152-1163
[59]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践, 2002,11:34-38.
[60]李晓磊.一种新型的智能优化方法-人工鱼群算法:[博士学位论文].杭州:浙江大学,2003
[61]李晓磊,钱积新.人工鱼群算法:自下而上的寻优模式.过程系统工程年会论文集, 2001,76-82
[62]李晓磊,路飞,田国会,钱积新.组合优化问题的人工鱼群算法应用.山东大学学报(工学版), 2004,34(5):64-67
[63]王联国,洪毅,赵付青,等.一种改进的人工鱼群算法.计算机工程, 2008,34(19):192-194
[64]刘白,周永权.基于遗传算法的人工鱼群优化算法.计算机工程与设计, 2008,29(22):5827-5829
[65] Holland J H. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan, 1975
[66]汪定伟,王俊伟,王洪峰,等.智能优化方法.北京:高等教育出版社,2007
[67]郭科,陈聆,魏友华.最优化方法及其应用.北京:高等教育出版社,2007
[68] Srinivas M, Patnaik L M. Genetic algorithm: a survey. IEEE Computer, 1994,27(6):17-26
[69] Park Jong-Bae, Park Young-Moon, Won Jong-Ryul, etal. An improved genetic algorithm for generation expansion planning. IEEE Transactions on Power Systems, 2000, 15(3): 916-922
[70] Vasconcelos J A, Ramirez J A, Takahashi R H C, etal. Improvements in genetic algorithms. IEEE Transactions on Magnetics, 2001, 37(5): 3414-3417
[71]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社, 1999
[72]陈国良,王煦法,庄镇泉,等.遗传算法及其应用.北京:人民邮电出版社, 1996
[73]张铃,张拔.统计遗传算法.软件学报, 1997, 8(5): 335-344
[74] Kirkpatrick S, Gelatt C, Vecchi P. Optimaization by simulated annealing. Science, 1983,220:671-679
[75]康立山,谢石,等.非数值并行算法(第一册):模拟退火算法,北京:科学出版社, 2003
[76] X..Wang, M.Damodaran, S.I.Lee. Inverse design using parallel simulated annealing and computational fluid dynamics, AIAA Journal, 2002,40:791-795
[77] Chen W H, Srivastava B. Simulated annealing procedures for forming machine cells in group technology. European Journal of Operational Research, 1994,75:100-111
[78] Lundy M, Mess A. Convergence of an annealing algorithm. Mathematical Programming, 1986,34:111-124
[79]李颖娟,汪定伟.准时化生产计划的半无限规划模型与模拟退火方法.控制与决策, 1998, 13(5):603-607
[80] K.I. Smith, R.M Everson, J.E Fieldsend, etal. Dominance-Based Multiobjective Simulated Annealing. IEEE Transactions on Evolutionary Computation, 2008,12(3):323-342
[81] P.P.C.Yip, Yoh-Han Pao. Combinatorial optimization with use of guided evolutionary simulated annealing. IEEE Transactions on Neural Networks, 1995,6(2):290-295
[82] Lipo Wang, Sa Li, Tian F, etal. A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004,34(5):2119-2125
[83] Feng-Tse Lin, Cheng-Yan Kao, Ching-Chi Hsu. Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Transactions on Systems, Man and Cybernetics, 1993,23(6):1752-1767
[84] Glover F. Tabu search. ORSA Journal on Computing, 19891:190-206
[85] Ackley D H, Hinton G E, Seinowshi T J. A learning algorithm for Boltzmann machines. Cognitive Science, 1985,9:147-169
[86] Dorigo M, Maniezzo V, Colorni A. The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybernet, 1996,Part B,26(1):29-41
[87] Kennedy J, Eberhart R C. Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, 1995,11:1942-1948
[88] Linhares. Synthesizing a predatory search strategy for VLSI layouts. IEEE Trans. on Evolutionary Computation,1999,3(2):147-152
[89] Franklin B, Bergerman M. Cultural algorithms: concepts and experiments. Proceedings of the 2000 Congress on Evolutionary Computation, 2002,2:1245-1251
[90] Dong-Wei Guo, Chui-Liu Kong. A new artificial life algorithm to solve time-varying optimization problem. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004,4: 2146-2148
[91] Metroplis N, Rosenbluth A, Rosenbluth M, etal. Equation of state calculation by fast computing machines. Journal of Cherimal Physics, 1953,21:1087-1092
[92]刘逻.遗传算法和模拟退火算法在车辆线路问题上的研究及应用:[硕士学位论文].长春:长春理工大学,2009
[93] Wang Lianguo, Hong Yi. A multiagent artificial fish swarm algorithm. Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, 2008,6:3161-3166
[94] Liu Chunbo, Wang Huijin, Luo Zhiping, etal. QoS multicast routing problem based on artificial fish-swarm algorithm. International Workshop on Education Technology and Computer Science, 2009,814-817
[95] Shan Xiaojuan, Jiang Mingyan, Li Jingpeng. The routing optimization based on improved artificial fish swarm algorithm. Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, 2006,6:3658-3662
[96] Wang Junwei, Wang Xingwei, Huang Min. Tabu artificial fish swarm algorithm based intelligent QoS multicast routing algorithm. International Conference on Computational Intelligence and Security, 2008,56-60
[97]张梅凤,邵诚,甘勇,等.基于变异算子与模拟退火混合的人工鱼群优化算法.电子学报, 2006,34(8):1381-1385
[98]曹承志,张坤,郑海英,等.基于人工鱼群算法的BP神经网络速度辨识器.系统仿真学报, 2009,21(4):1047-1050
[99]俞洋,殷志锋,田亚菲.基于自适应人工鱼群算法的多用户检测器.电子与信息学报, 2007,29(1):121-124
[100]黄华娟,周永权.求解全局优化问题的混合人工鱼群算法.计算机应用, 2008,28(12):3062-3067
[101]林伟廷,田菁,朱华勇,沈林成.鱼群算法求解无人机任务规划问题.计算机仿真. 2007,24(12):41-44
[102] Huadong Ma, Xi Zhang , Anlong Ming. A Coverage-Enhancing Method for 3D Directional Sensor Networks. IEEE INFOCOM, 2009,2791-2795
[103] Matlab-The language of Technical Computing, http://www.mathworks.com/pl_homepage
[104] Howard A, Matari? MJ, Sukhatme GS. Mobile sensor network deployment using potential field: A distributed scalable solution to the area coverage problem. Proc. of the 6th Int’l Symp. on Distributed Autonomous Robotics Systems (DARS 2002), 2002,299-308
[105] Poduri S, Sukhatme GS. Constrained coverage for mobile sensor networks. Proc. of the 2004 IEEE Int’l Conf. on Robotics & Automation. New York: IEEE Press, 2004,165-171
[106] Ke Zhang, Wei Zhang, Jiazhi Zeng. Preliminary study of routing and date integrity in mobile Ad Hoc UAV network. IEEE International Conference on Apperceiving Computing and Intelligent Analysis. Chengdu: UESTC Press, 2008,347-350
[107]周逊.基于Ad Hoc的无人机网络及其路由协议研究:[博士学位论文].成都:西南交通大学,2007
[108] Benzaid M, Minet P, Al Agha K. Integrating fast mobility in the OLSR routing protocol. 4th International Workshop on Mobile and Wireless Communications Network. France, 2002,217-221
[109] Rasheed T, Javaid U, Jerbi M, Al Agha, K. Scalable Multi-hop Ad Hoc Routing Using Modified OLSR Routing Protocol[C]. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Trento, 2007,1-6
[110] Voorhaen M, Blondia C. Analyzing the impact of neighbor sensing on the performance of the OLSR protocol. International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 2006,4:1-6
[111] Liang Ming, Gang Zhao, Guihai Xie, etal. HOLSR: A novel routing scheme of Ad Hoc wireless networks for pervasive computing. International Conference on Pervasive Computing and Applications, Shijiazhuang,2007,661-666
[112] Dang Nguyen, Pascale Minet. Analysis of MPR Selection in the OLSR Protocol. 21st International Conference on Advanced Information Networking and Applications Workshops, Le Chesnay, 2007,(2),887-892
[113] Ur Rahman Khan K, Reddy A.V, Zaman R.U, etal. An efficient DSDV routing protocol for wireless mobile Ad Hoc networks and its performance comparison. Second UKSIM European Symposium on Computer Modeling and Simulation, Hyderabad, 2008,506-511
[114] Hong Jiang, Garcia-Luna-Aceves, J.J. Performance comparison of three routing protocols for ad hoc networks. Tenth International Conference on Computer Communications and Networks, Santa Cruz, 2001,547-554
[115]冯慧斌,张顺颐,刘超,等.基于非合作博弈的无线自组织网络流量控制模型.电子与信息学报,2009,4:925?928
[116]张信明,曾依灵,干国政,等.用遗传算法寻找OLSR协议的最小MPR集.软件学报,2006,17(4):932-938
[117] Xun Zhou, Yu Lu, HongGe Ma. Routing improvement using multiple disjoint paths for ad hoc networks. IFIP International Conference on Wireless and Optical Communications Networks, 2006, 1-5
[118] Abdulla, M. Simon, R.A. Simulation study of common mobility models for opportunistic networks. Simulation Symposium, ANSS2008, 41st Annual, 2008,43-50
[119] Mirco Musolesi, Stephen Hailes, Cecilia Mascolo. Adaptive routing for intermittently connected mobile Ad Hoc networks, Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks, London, 2005,183-189
[120] Bence Pasztor, Mirco Musolesi, Cecilia Mascolo. Opportunistic mobile sensor data collection with SCAR. IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, London, 2007,1-12
[121] Wireless and Mobility Extensions to the ns-2 Network Simulator. CMU Monarch Project, http:/monarch.cs.cmu.edu/cmu-ns.html
[2] Abolhasan M, Wysocki T, Dutkiewicz E, Abolhasan M. A review of routing protocols for mobile ad hoc networks. Ad Hoc Networks, 2004,2:1-22
[3] Mian A.N, Baldoni R, Beraldi R .A survey of service discovery protocols in multihop mobile Ad Hoc networks. IEEE Pervasive Computing, 2009,8:66-74
[4] Ververidis C.N, Polyzos G.C. Service discovery for mobile Ad Hoc networks: a survey of issues and techniques. IEEE Communications Surveys & Tutorials, 2008,10(3):30-45
[5] Michele Nogueira Lima, Aldri Luiz dos Santos, Guy Pujolle. A survey of survivability in mobile Ad Hoc Networks. IEEE Communications Surveys &tutorials, 2009,11:66-77
[6] Luo JH, Ye DX, Xue L, Fan MY. A survey of multicast routing protocols for mobile Ad-Hoc networks. IEEE Communications Surveys &tutorials, 2009,11(1):78-91
[7]尹长青.无线自组网络若干技术的研究:[博士学位论文].上海:复旦大学,2004
[8] D.Estrin, R. Govindan, J. Heidemann, etal. Next century challenges: scalable coordination in sensor networks. Proc. 5th ACM/IEEE International Conference on Mobile Computing and Networking. Seattle, WA, USA, 1999:263-270
[9] I.F.Akyildiz, W.Su, Y.Sankarasubramaniam, etal. Wireless sensor networks: a survey. Computer Networks, 2002, 38: 393-422
[10] S.Kumar, F.Zhao, D.Shepherd. Collaborative signal and information processing in microsensor networks. IEEE Signal Processing Magazine, 2002,19(2):13-14
[11] C. Y Chong, S. Kumar. Sensor networks evolution, opportunities, and challenges. Proceedings of the IEEE, 2003, 91(8): 1247-1256
[12]孙利民,李建中,陈渝,等.无线传感器网络.北京:清华大学出版社,2005
[13] G J. Pottie, W. J. Kaiser. Embedding the Internet: wireless integrated network sensors. Communications of the ACM, 2000, 43(5): 51-58
[14]任丰原,黄海宁,林闯.无线传感器网络.软件学报, 2003,14(7):1282-1291
[15]马华东,陶丹.多媒体传感器网络及其研究进展.软件学报, 2006,17(9) 4-6
[16]杨少军.无线传感器网络若干关键技术研究:[博士学位论文].西安:西北工业大学,2006
[17]王媛丽.无线传感器网络中路由相关的若干问题的研究:[博士学位论文].长沙:国防科技大学,2006
[18]崔逊学,赵湛,王成编.无线传感器网络的领域应用与设计技术.北京:国防工业出版社, 2009
[19]张可,张伟.地质灾害地区多媒体传感器网络智能应用关键技术分析.综合电子系统技术教育部重点实验室2009学术年会,2009:174-177
[20]孔宁.物联网资源寻址关键技术研究:[博士学位论文].北京:中国科学院研究生院(计算机网络信息中心), 2008
[21]周伯生,吴介一,张飒兵. MANET路由协议研究进展.计算机研究与发展, 2002, 39(10):1165-1177
[22] M R Pearlman, Z J Haas. Determining the optimal configuration for the zone routing protocol. IEEE Journal on Selected Areas in Communications (Special Issue on Wireless Ad Hoc Networks), 1999, 17(8): 1395~1414
[23]曹英烈.移动Ad hoc网络路由算法研究:[博士学位论文].广州:华南理工大学,2006
[24] Chlamtac I, Syrotiuk V R, Woodward B A. A distance routing effect algorithm for mobility(DREAM).Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking(MOBICOM). Dallas:IEEE Inc,1998,76-84
[25] Vaidya N H. Location-Aided Routing(LAR)in mobile Ad Hoc networks. Proceedings of the ACM/IEEE Intemational Conference on Mobile Computing and Networking (MOBICOM). Dallas:IEEE Inc,1998,66-75
[26] K Fall. A delay-tolerant network architecture for challenged internets. In Proceedings of ACM SIGCOMM 2003 Conference on Computer Communications. Karlsruhe:ACM Press, 2003,27-34
[27] Scott Burleigh, Adrian Hooke, Leigh Torgerson, etal. Delay-Tolerant Networking: An approach to interplanetary Internet. IEEE Communications Magazine, 2003,6:128-136
[28]郑炜,王澄.延迟容忍网络中的路由算法研究.信息技术, 2008,7:68-70
[29]朱金奇,刘明,龚海刚,等.延迟容忍移动传感器网络中基于选择复制的数据传输.软件学报, 2009,20(8):2227-2240
[30] A Mainwaring, J Polastre, R Szewczyk, etal. Wireless sensor networks for habitat monitoring. In Proc. of ACM International Workshop on Wireless Sensor Networks and Applications Atlanta: ACM Press,2002,88-97
[31] Dermot G,Vinny C, Stephen F. Sensor networking with delay tolerance (SENDT), 2006, http://down.dsg.cs.tcd.ie/sendt/
[32] R C Shah, S Roy, S Jain, etal. Data MULEs: modeling a three-tier architecture for sparse sensor networks. In Proc. of the First International Workshop on Sensor Network Protocols and Applications. Anchorage: IEEE Computer Society Press, 2003,30-41
[33] Philo J, Hidekazu O, Yong W. Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet. ACM Operating System Review, 2002, 36(5):96-107
[34] T Small, Z J Haas. The shared wireless infostation model– a new Ad Hoc networking paradigm (or where there is a whale, there is a Way). In Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC’03). Annapolis: ACM Press, 2003,233-244
[35] D L. Hall and J.Linas. Handbook of multi sensor data fusion. CRC Press, 2001
[36]宋吉鹏.无线多媒体传感器网络数据融合路由及仿真平台研究:[硕士学位论文].成都:电子科技大学,2009
[37] Krishnamachari B, Estrin D, Wicker S. Modeling data-centric routing in wireless sensor networks. USC Computer Engineering Technical Report CENG, 2002,02-14
[38] Ian F. Akyildiz, Tommaso Melodia, Kaushik R.Chowdhury. A survey on wireless multimedia sensor networks. Computer Networks, 2007,51:921-960
[39]陶丹,马华东.视频传感器网络中基于相关性图像融合算法.计算机辅助设计与图形学学报, 2007,19(5):656-666
[40] Ian F. Akyildiz, Tommaso Melodia, Kaushik R. Chowdhury. Wireless multimedia sensor networks: applications and testbeds. Proceedings of the IEEE, 2008 ,96(10):1588-1605
[41]蒋杰.无线传感器网络覆盖控制研究:[博士学位论文].长沙:国防科学技术大学,2005
[42]任颜,张思东,张宏科.无线传感器网络中覆盖控制理论与算法.软件学报, 2006, 17(3):422-433
[43]朱寅寅.无线传感器网络覆盖优化方法研究:[硕士学位论文].南京:南京理工大学,2009
[44] Slijepcevic S, Potkonjak M. Power efficient organization of wireless sensor networks. Proc.of the IEEE Int'1 Conf.on Communications (ICC). Helsinki: IEEE Press, 2001,472-476
[45] Veltri G, Huang Q, Qu G, etal. Minimal and maximal exposure path algorithms for wireless embedded sensor networks. Proc.of the ACM Int'1 Conf. Embedded Networked Sensor Systems (SenSys). New York: ACM Press, 2003,40-50
[46] Adlakha S, Srivastava M. Critical density thresholds for coverage in wireless sensor networks. Proc. of the IEEE Wireless Communications and Networking (WCNC). New Orleans: IEEE Press, 2003,1615-1620
[47] Y Wang, F Lin, H Wu. Poster: efficient data transmission in delay fault tolerant mobile sensor networks (DFT-MSN). In Proceedings of IEEE International Conference on Network Protocols (ICNP’05).Boston: IEEE Press, 2005, In proceedings CD
[48] J. Leguay, T. Friedman, V. Conan. DTN Routing in a Mobility Pattern Space. In Proceedings of ACM SIGCOMM’05 workshop on Delay Tolerant Networking and Related Topics. Philadelphia: ACM Press, 2005,276-283
[49]朱金奇.延迟容忍无线传感器网络中的动态数据收集技术及其研究:[博士学位论文].成都:电子科技大学, 2009
[50]周虹宇,周激流,林锋.一种容延迟移动传感器网络中的代码分发机制.四川大学学报(自然科学版), 2008,45(5):1089-1094
[51]朱金奇,刘明,许富龙.延迟容忍移动无线传感器网络路由分析.计算机工程与应用, 2009,45(6):13-15
[52] Y Wang , H Wu. Delay/Fault-Tolerant Mobile Sensor Network (DFT-MSN): a new paradigm for pervasive information gathering. IEEE Transactions on Mobile Computing, 2006, 6(8):1021-1034
[53] Amin Vahdat, David Becker. Epidemic routing for partially connected ad hoc networks. Technical Report CS-200006, Duke University, 2000
[54] Y Wang, H Y Wu. Replication-Based efficient data delivery scheme (RED) for Delay/Fault-Tolerant mobile sensor network (DFT-MSN). In Proc. Of Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops. Pisa: IEEE Press, 2006, 485-489
[55] Y Wang, H Y Wu, H Dang. Analytic study of Delay/Fault-Tolerant mobile sensor networks (DFT-MSN’s). Tech Report, Lafayette: CACS, University of Louisiana at Lafayette, 2006
[56] Jinqi Zhu, Jiannong Cao, Ming Liu, etal. Mobility Prediction-based Adaptive Data Gathering Protocol. In Proc. of IEEE Global Telecommunications Conference. New Orleans:IEEE Press, 2008,1-5
[57]熊永平,孙利民,牛建伟,等.机会网络.软件学报, 2009,20(1): 124?137
[58]陶丹,马华东,刘亮.基于虚拟势场的有向传感器网络覆盖增强算法.软件学报, 2007,3:1152-1163
[59]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践, 2002,11:34-38.
[60]李晓磊.一种新型的智能优化方法-人工鱼群算法:[博士学位论文].杭州:浙江大学,2003
[61]李晓磊,钱积新.人工鱼群算法:自下而上的寻优模式.过程系统工程年会论文集, 2001,76-82
[62]李晓磊,路飞,田国会,钱积新.组合优化问题的人工鱼群算法应用.山东大学学报(工学版), 2004,34(5):64-67
[63]王联国,洪毅,赵付青,等.一种改进的人工鱼群算法.计算机工程, 2008,34(19):192-194
[64]刘白,周永权.基于遗传算法的人工鱼群优化算法.计算机工程与设计, 2008,29(22):5827-5829
[65] Holland J H. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan, 1975
[66]汪定伟,王俊伟,王洪峰,等.智能优化方法.北京:高等教育出版社,2007
[67]郭科,陈聆,魏友华.最优化方法及其应用.北京:高等教育出版社,2007
[68] Srinivas M, Patnaik L M. Genetic algorithm: a survey. IEEE Computer, 1994,27(6):17-26
[69] Park Jong-Bae, Park Young-Moon, Won Jong-Ryul, etal. An improved genetic algorithm for generation expansion planning. IEEE Transactions on Power Systems, 2000, 15(3): 916-922
[70] Vasconcelos J A, Ramirez J A, Takahashi R H C, etal. Improvements in genetic algorithms. IEEE Transactions on Magnetics, 2001, 37(5): 3414-3417
[71]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版社, 1999
[72]陈国良,王煦法,庄镇泉,等.遗传算法及其应用.北京:人民邮电出版社, 1996
[73]张铃,张拔.统计遗传算法.软件学报, 1997, 8(5): 335-344
[74] Kirkpatrick S, Gelatt C, Vecchi P. Optimaization by simulated annealing. Science, 1983,220:671-679
[75]康立山,谢石,等.非数值并行算法(第一册):模拟退火算法,北京:科学出版社, 2003
[76] X..Wang, M.Damodaran, S.I.Lee. Inverse design using parallel simulated annealing and computational fluid dynamics, AIAA Journal, 2002,40:791-795
[77] Chen W H, Srivastava B. Simulated annealing procedures for forming machine cells in group technology. European Journal of Operational Research, 1994,75:100-111
[78] Lundy M, Mess A. Convergence of an annealing algorithm. Mathematical Programming, 1986,34:111-124
[79]李颖娟,汪定伟.准时化生产计划的半无限规划模型与模拟退火方法.控制与决策, 1998, 13(5):603-607
[80] K.I. Smith, R.M Everson, J.E Fieldsend, etal. Dominance-Based Multiobjective Simulated Annealing. IEEE Transactions on Evolutionary Computation, 2008,12(3):323-342
[81] P.P.C.Yip, Yoh-Han Pao. Combinatorial optimization with use of guided evolutionary simulated annealing. IEEE Transactions on Neural Networks, 1995,6(2):290-295
[82] Lipo Wang, Sa Li, Tian F, etal. A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004,34(5):2119-2125
[83] Feng-Tse Lin, Cheng-Yan Kao, Ching-Chi Hsu. Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Transactions on Systems, Man and Cybernetics, 1993,23(6):1752-1767
[84] Glover F. Tabu search. ORSA Journal on Computing, 19891:190-206
[85] Ackley D H, Hinton G E, Seinowshi T J. A learning algorithm for Boltzmann machines. Cognitive Science, 1985,9:147-169
[86] Dorigo M, Maniezzo V, Colorni A. The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybernet, 1996,Part B,26(1):29-41
[87] Kennedy J, Eberhart R C. Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, 1995,11:1942-1948
[88] Linhares. Synthesizing a predatory search strategy for VLSI layouts. IEEE Trans. on Evolutionary Computation,1999,3(2):147-152
[89] Franklin B, Bergerman M. Cultural algorithms: concepts and experiments. Proceedings of the 2000 Congress on Evolutionary Computation, 2002,2:1245-1251
[90] Dong-Wei Guo, Chui-Liu Kong. A new artificial life algorithm to solve time-varying optimization problem. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004,4: 2146-2148
[91] Metroplis N, Rosenbluth A, Rosenbluth M, etal. Equation of state calculation by fast computing machines. Journal of Cherimal Physics, 1953,21:1087-1092
[92]刘逻.遗传算法和模拟退火算法在车辆线路问题上的研究及应用:[硕士学位论文].长春:长春理工大学,2009
[93] Wang Lianguo, Hong Yi. A multiagent artificial fish swarm algorithm. Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, 2008,6:3161-3166
[94] Liu Chunbo, Wang Huijin, Luo Zhiping, etal. QoS multicast routing problem based on artificial fish-swarm algorithm. International Workshop on Education Technology and Computer Science, 2009,814-817
[95] Shan Xiaojuan, Jiang Mingyan, Li Jingpeng. The routing optimization based on improved artificial fish swarm algorithm. Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, 2006,6:3658-3662
[96] Wang Junwei, Wang Xingwei, Huang Min. Tabu artificial fish swarm algorithm based intelligent QoS multicast routing algorithm. International Conference on Computational Intelligence and Security, 2008,56-60
[97]张梅凤,邵诚,甘勇,等.基于变异算子与模拟退火混合的人工鱼群优化算法.电子学报, 2006,34(8):1381-1385
[98]曹承志,张坤,郑海英,等.基于人工鱼群算法的BP神经网络速度辨识器.系统仿真学报, 2009,21(4):1047-1050
[99]俞洋,殷志锋,田亚菲.基于自适应人工鱼群算法的多用户检测器.电子与信息学报, 2007,29(1):121-124
[100]黄华娟,周永权.求解全局优化问题的混合人工鱼群算法.计算机应用, 2008,28(12):3062-3067
[101]林伟廷,田菁,朱华勇,沈林成.鱼群算法求解无人机任务规划问题.计算机仿真. 2007,24(12):41-44
[102] Huadong Ma, Xi Zhang , Anlong Ming. A Coverage-Enhancing Method for 3D Directional Sensor Networks. IEEE INFOCOM, 2009,2791-2795
[103] Matlab-The language of Technical Computing, http://www.mathworks.com/pl_homepage
[104] Howard A, Matari? MJ, Sukhatme GS. Mobile sensor network deployment using potential field: A distributed scalable solution to the area coverage problem. Proc. of the 6th Int’l Symp. on Distributed Autonomous Robotics Systems (DARS 2002), 2002,299-308
[105] Poduri S, Sukhatme GS. Constrained coverage for mobile sensor networks. Proc. of the 2004 IEEE Int’l Conf. on Robotics & Automation. New York: IEEE Press, 2004,165-171
[106] Ke Zhang, Wei Zhang, Jiazhi Zeng. Preliminary study of routing and date integrity in mobile Ad Hoc UAV network. IEEE International Conference on Apperceiving Computing and Intelligent Analysis. Chengdu: UESTC Press, 2008,347-350
[107]周逊.基于Ad Hoc的无人机网络及其路由协议研究:[博士学位论文].成都:西南交通大学,2007
[108] Benzaid M, Minet P, Al Agha K. Integrating fast mobility in the OLSR routing protocol. 4th International Workshop on Mobile and Wireless Communications Network. France, 2002,217-221
[109] Rasheed T, Javaid U, Jerbi M, Al Agha, K. Scalable Multi-hop Ad Hoc Routing Using Modified OLSR Routing Protocol[C]. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Trento, 2007,1-6
[110] Voorhaen M, Blondia C. Analyzing the impact of neighbor sensing on the performance of the OLSR protocol. International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 2006,4:1-6
[111] Liang Ming, Gang Zhao, Guihai Xie, etal. HOLSR: A novel routing scheme of Ad Hoc wireless networks for pervasive computing. International Conference on Pervasive Computing and Applications, Shijiazhuang,2007,661-666
[112] Dang Nguyen, Pascale Minet. Analysis of MPR Selection in the OLSR Protocol. 21st International Conference on Advanced Information Networking and Applications Workshops, Le Chesnay, 2007,(2),887-892
[113] Ur Rahman Khan K, Reddy A.V, Zaman R.U, etal. An efficient DSDV routing protocol for wireless mobile Ad Hoc networks and its performance comparison. Second UKSIM European Symposium on Computer Modeling and Simulation, Hyderabad, 2008,506-511
[114] Hong Jiang, Garcia-Luna-Aceves, J.J. Performance comparison of three routing protocols for ad hoc networks. Tenth International Conference on Computer Communications and Networks, Santa Cruz, 2001,547-554
[115]冯慧斌,张顺颐,刘超,等.基于非合作博弈的无线自组织网络流量控制模型.电子与信息学报,2009,4:925?928
[116]张信明,曾依灵,干国政,等.用遗传算法寻找OLSR协议的最小MPR集.软件学报,2006,17(4):932-938
[117] Xun Zhou, Yu Lu, HongGe Ma. Routing improvement using multiple disjoint paths for ad hoc networks. IFIP International Conference on Wireless and Optical Communications Networks, 2006, 1-5
[118] Abdulla, M. Simon, R.A. Simulation study of common mobility models for opportunistic networks. Simulation Symposium, ANSS2008, 41st Annual, 2008,43-50
[119] Mirco Musolesi, Stephen Hailes, Cecilia Mascolo. Adaptive routing for intermittently connected mobile Ad Hoc networks, Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks, London, 2005,183-189
[120] Bence Pasztor, Mirco Musolesi, Cecilia Mascolo. Opportunistic mobile sensor data collection with SCAR. IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, London, 2007,1-12
[121] Wireless and Mobility Extensions to the ns-2 Network Simulator. CMU Monarch Project, http:/monarch.cs.cmu.edu/cmu-ns.html