无线传感器网络容错目标检测算法研究
|
摘要
传感器网络的发展使得基于它们的新的应用越来越多的涌现出来,目标跟踪就是其中很有用途的应用之一。目标跟踪对于自然科学里面很多学科的研究,野生动植物研究以及军事情报收集等领域的方法更新及效率改善都具有十分重大的意义。传感器网络的目标跟踪应用中,目标检测是跟踪系统进行后续定位、跟踪等任务的前提和基础。现有的目标跟踪算法在目标侦测阶段很少考虑网络中错误节点的存在,而忽略了网络的鲁棒性。然而,在实际应用中,由于传感器网络的工作环境恶劣加之节点自身故障的影响,往往会导致某些节点失效或出错。而拜占庭错误节点的出现会导致错误节点在信息交换过程中随机乱发信息,令其它正常节点由于获得不准确的目标信息而产生错误的判决结果,进而影响目标的定位与跟踪。因此,容错处理成为传感器网络目标检测算法中一个重要的问题。
本文首先介绍了无线传感器网络的概念、特点、面临的技术挑战及其应用范围,分析了现有的目标定位及跟踪算法及其存在的问题,进而引出深入研究拜占庭错误背景下容错目标检测算法的必要性。
接着论文对现有的容错目标检测算法进行了分析比较。在值融合容错目标检测VFA算法中,正常节点将通过OM算法获得一组相同的观测值序列,通过舍弃序列中的n个最大及最小观测值,并对余下的观测值取平均,之后与门限值相比较做出判决以达到一致和容错检测的目的。然而在实际应用中,如果网络规模较大,节点个数较多,大规模执行OM算法将给系统带来巨大的通信压力,从而影响网络寿命。因此如何确定数据融合过程中参与OM算法的节点个数是值融合目标容错检测算法需要解决的关键问题。节点个数过多将增大能耗,降低网络寿命;节点个数偏少又可能因为没有全面、准确的获得目标特征信息而导致错误的目标检测结果。分层拜占庭容错目标检测HBA算法通过节点分层次、逐分组、小规模地多次执行SM算法,能够在一定程度上减少无线传感器网络在目标检测过程中的通信量。但HBA算法存在着通信轮数较多,检测时延较大等问题,不能满足实时性要求较高的目标检测应用。
本文在HBA算法和VFA算法的基础上,提出了分层值融合算法HVFA。类似于HBA算法,HVFA算法首先也将网络中所有的传感器节点分为若干
With the fast development of sensor networks, many new applications emerged in recent years. In these applications, dynamic target tracking is one of the most important issues. Target tracking is extensively desirable for diverse field of subjects in natural science, wild animal study and military information collection, wherein the target detection is the precondition and basis for the target location and target tracking. So far there have been a number of research results in the literature on the target detection algorithms for wireless sensor networks. Nonetheless most of the existing algorithms address exclusively the accuracy of target detection, but ignore the robustness against the faulty nodes in networks. However, in real applications, Byzantine faults may happen due to the harsh environment, where the nodes are assumed to send inconsistent and arbitrary values to other nodes during information fusion. Obviously, fault-tolerant target detection algorithms are highly desired to achieve the robustness to the Byzantine faults. Obvisouly, the fault-tolerance will become a crucial problem in target detection for wireless sensor networks.
In this paper, the development of sensor network and the challenges to be faced, the application fields the WSN together with the key technologies of sensor network are reviewed at first. Then some known algorithms for target location and tracking in wireless sensor network are discussed to highlight the crucial problems therein.
As an important target detection algorithm, the Value Fusion Algorithm are addressed, wherein the measurements of all nodes are collected through OM algorithm and the n extremes are discarded to exclude the abnormal readings by Byzantine faulty nodes. The problem of the VFA scheme is the cost of communication overhead, which will become a critical problem when the number of nodes is large enough. Due to the strict energy constraints in wireless sensor network, communication efficient algorithm is desirable. An energy-efficient HBA algorithm was proposed recently, wherein SM algorithm instead of OM algorithm are utilized hierarchically. Compared with the OM algorithm in the whole interest region, hierarchical SM algorithm in HBA scheme is able to reduce the energy dissipation significantly. However, the hierarchical processing sometimes brings about a number of communication rounds, thus giving rise to an unwanted long time delay.
In this paper, the hierarchical value fusion scheme (HVFA) is proposed based on both the VFA and HBA algorithms. Just like the HBA algorithm, all sensor nodes are partitioned into several subgroups and the SM algorithm is performed hierarchically among nodes to correct faults. However, unlike the HBA scheme where the SM algorithm will be performed thrice among the subgroup heads, the member nodes in each subgroup, and the shared nodes between subgroups when faulty head is detected, respectively. The SM algorithm will be performed only twice among subgroup heads and when shared nodes correct the faulty head in the HVFA scheme. As a result, the number of communication rounds in HVFA is much less than those in the HBA. It is validated through numerical anlaysis and computer simulations that, the HVFA algorithm can enable energy-efficient target detection with much less processing time delay compared with that of HBA algorithm.
As for VFA algorithm, one of the challenges is the determination of the number of extreme values to be excluded due to the "mean value" calculation during measurement exchange among neighboring nodes. As a comparison, we explore the utilization of "median value" to effectively remove those extreme values for target detection. In median value fault-tolerant algorithm, the median value of all neighboring readings is used to approximate the local observation to the possible present target. And it is validated through the simulations on the OPNET platform that, the algorithm can reduce the energy consumed and achieve a good performance of fault-tolerance and detection probability when the number of Byzantine faulty nodes is small and the node density is large enough.
本文首先介绍了无线传感器网络的概念、特点、面临的技术挑战及其应用范围,分析了现有的目标定位及跟踪算法及其存在的问题,进而引出深入研究拜占庭错误背景下容错目标检测算法的必要性。
接着论文对现有的容错目标检测算法进行了分析比较。在值融合容错目标检测VFA算法中,正常节点将通过OM算法获得一组相同的观测值序列,通过舍弃序列中的n个最大及最小观测值,并对余下的观测值取平均,之后与门限值相比较做出判决以达到一致和容错检测的目的。然而在实际应用中,如果网络规模较大,节点个数较多,大规模执行OM算法将给系统带来巨大的通信压力,从而影响网络寿命。因此如何确定数据融合过程中参与OM算法的节点个数是值融合目标容错检测算法需要解决的关键问题。节点个数过多将增大能耗,降低网络寿命;节点个数偏少又可能因为没有全面、准确的获得目标特征信息而导致错误的目标检测结果。分层拜占庭容错目标检测HBA算法通过节点分层次、逐分组、小规模地多次执行SM算法,能够在一定程度上减少无线传感器网络在目标检测过程中的通信量。但HBA算法存在着通信轮数较多,检测时延较大等问题,不能满足实时性要求较高的目标检测应用。
本文在HBA算法和VFA算法的基础上,提出了分层值融合算法HVFA。类似于HBA算法,HVFA算法首先也将网络中所有的传感器节点分为若干
With the fast development of sensor networks, many new applications emerged in recent years. In these applications, dynamic target tracking is one of the most important issues. Target tracking is extensively desirable for diverse field of subjects in natural science, wild animal study and military information collection, wherein the target detection is the precondition and basis for the target location and target tracking. So far there have been a number of research results in the literature on the target detection algorithms for wireless sensor networks. Nonetheless most of the existing algorithms address exclusively the accuracy of target detection, but ignore the robustness against the faulty nodes in networks. However, in real applications, Byzantine faults may happen due to the harsh environment, where the nodes are assumed to send inconsistent and arbitrary values to other nodes during information fusion. Obviously, fault-tolerant target detection algorithms are highly desired to achieve the robustness to the Byzantine faults. Obvisouly, the fault-tolerance will become a crucial problem in target detection for wireless sensor networks.
In this paper, the development of sensor network and the challenges to be faced, the application fields the WSN together with the key technologies of sensor network are reviewed at first. Then some known algorithms for target location and tracking in wireless sensor network are discussed to highlight the crucial problems therein.
As an important target detection algorithm, the Value Fusion Algorithm are addressed, wherein the measurements of all nodes are collected through OM algorithm and the n extremes are discarded to exclude the abnormal readings by Byzantine faulty nodes. The problem of the VFA scheme is the cost of communication overhead, which will become a critical problem when the number of nodes is large enough. Due to the strict energy constraints in wireless sensor network, communication efficient algorithm is desirable. An energy-efficient HBA algorithm was proposed recently, wherein SM algorithm instead of OM algorithm are utilized hierarchically. Compared with the OM algorithm in the whole interest region, hierarchical SM algorithm in HBA scheme is able to reduce the energy dissipation significantly. However, the hierarchical processing sometimes brings about a number of communication rounds, thus giving rise to an unwanted long time delay.
In this paper, the hierarchical value fusion scheme (HVFA) is proposed based on both the VFA and HBA algorithms. Just like the HBA algorithm, all sensor nodes are partitioned into several subgroups and the SM algorithm is performed hierarchically among nodes to correct faults. However, unlike the HBA scheme where the SM algorithm will be performed thrice among the subgroup heads, the member nodes in each subgroup, and the shared nodes between subgroups when faulty head is detected, respectively. The SM algorithm will be performed only twice among subgroup heads and when shared nodes correct the faulty head in the HVFA scheme. As a result, the number of communication rounds in HVFA is much less than those in the HBA. It is validated through numerical anlaysis and computer simulations that, the HVFA algorithm can enable energy-efficient target detection with much less processing time delay compared with that of HBA algorithm.
As for VFA algorithm, one of the challenges is the determination of the number of extreme values to be excluded due to the "mean value" calculation during measurement exchange among neighboring nodes. As a comparison, we explore the utilization of "median value" to effectively remove those extreme values for target detection. In median value fault-tolerant algorithm, the median value of all neighboring readings is used to approximate the local observation to the possible present target. And it is validated through the simulations on the OPNET platform that, the algorithm can reduce the energy consumed and achieve a good performance of fault-tolerance and detection probability when the number of Byzantine faulty nodes is small and the node density is large enough.
引文
[1]于海斌等,《智能无线传感器网络系统》,科学出版社,2006年。
[2]孙利民等,《无线传感器网络》,清华大学出版社,2005年。
[3]Akyildiz L F,Su W,Sankarasubramaniam Y,Cayirici E,Wireless Sensor Networks:A surey,Computer Networks,2002,38(4):393-422.
[4]Estrin D,Girod L,Pottie G,Srivastava M,Instrumenting the World with Wireless Sensor Networks,Proceedings of the International Conference on Acoustics,Speech and Signal Processing,pp 233-239.
[5]Elson J,Estrin D,Wireless Sensor Networks:A Bridge to the Physical World.Wireless Sensor Networks,2004:3-20.
[6]Estrin D,Culler D,Pister K,G Sukhatme,Connecting the Physical World with Pervasive Networks.IEEE Pervasive Computing Archive,2002,1(1):59-69.
[7]Liu J,Cheung P,Zhao F,Guibas L,A dual-space approach to tracking and sensor management in wireless sensor networks,Palo Alto Research Center Technical Report,pp.2002-10077,March,2002.
[8]Gupta R,Das S R,Tracking moving targets in a smart sensor network,The VTC Fall 2003 Symposium,Oct.2003.
[9]Zhao F,Shin J,Reich J,Information-driven dynamic sensor collaboration for tracking applications.IEEE Signal Processing Magazine,March 2002.
[10]Brooks R R,Ramanathan P,Sayeed A M,Distributed target classification and tracking in sensor networks,Proc of the IEEE,August 2003,pp 1163-1171.
[11]Mechitov K,Sundresh S,Kwon Y,Agha G,Cooperative tracking with binary-detection sensor networks,Proc of SenSys '03,November,2003.
[12]T.Clouqueur,K.K.Saluja,and E Ramanathan,"Fault Tolerance in Collaborative Sensor Networks for Target Detection," IEEE Trans.Computers,pp.320-333,Vol.53,No.3,March 2004.
[13]H.Yoshino,N.Hayashibara and T.Enokido.Hierarchical Protocol for Byzantine Agreement in a Peer-to-Peer Overlay Network,Proc of IEEE DEXA,2005.
[14]Min Ding,Dechang Chen,Andrew Thaeler & Xiuzhen Cheng,"Fault-Tolerant Target Location in Sensor Networks," Wireless Communication and Networking,Vol.4,pp.2362-2368,March 2007.
[15]康耀红,《数据融合理论与应用》,西安电子科技大学出版社,1997年。
[16]孙涛等,“目标识别中的信息融合技术”,自动化仪表,2001年2期。
[17]R.Viswanathan and P.Varshney,"Distributed Detection with Multiple Sensors:Part Ⅰ-Fundamentals," Proc.IEEE,pp.54-63,Jan.1997.
[18]Firooz A.Sadjadi,"Hypotheses testing in a distributed environment," IEEE Transactions on Aerospace and Electronic Systems,1986,22(2):134-137.
[19]Reibman A R,Nolte L W."Design and performance comparison of distributed detection networks," IEEE Transactions on Aerospace and Elec2tronic Systems,1987,23(6):789-797.
[20]Chair Z,Varshney P K."Optimal data fusion in multiple sensor detection systems," IEEE Transactions on Aerospace and Electronic Systems,1986,22(1):98-101.
[21]Thomopoulos S C A,Viswanathan R,Bougoulias D K."Optimal decision fusion in multiple sensor systems," IEEE Transactions on Aerospace and Electronic Systems,1987,23(5):644-653.
[22]Thomopoulos S C A,Viswanathan R,Bougoulias D K."Optimal distributed decision fusion," IEEE Transactions on Aerospace and Electronic System,1989,25(5):761-765.
[23]Nirwan Ansari,Edwin S.H.Hou,Bin2ou Zhu,Jiang guo Chen."Adaptive fusion by reinforcement learning for distributed detection systems," IEEE Transactions on Aerospace and Electronic System,1996,32(2):524-530.
[24]康耀红,《数据融合理论与应用》,西安电子科技大学出版社,1997.
[25]张文修,梁怡,《不确定性推理》,西安交通大学出版社,1996。
[26]耿立恩,潘旭峰,李晓雷,祝嘉光,Dempster Sharer,证据推理在数据融合中的应用,北京理工大学学报,1997,17(2):198-203.
[27]潘旭峰,耿立恩,李晓雷,基于同源数据融和技术的机械故障诊断方法,机械强度,1997,19(3):6-10。
[28]Mitchell R A,Westerkamp J J,Robust statistical feature based aircraft identification,IEEE Transactions on Aerospace and Electronic Sys2tems,1999,35(3):1077-1093.
[29]Ayrulu B,Barshan B,Utete S W,Target identification with multiple logical sonars using evidential reasoning and simple majority voting,IEEE International Conference on Robotics and Automation,1997,vol.3:2063-2068.
[30]倪明,单渊达,多端交直流混合网扩展规划的综合法,电力系统自动化,1999,23(1):15-19。
[31]向阳,史习智,证据理论合成的一点修正,上海交通大学学报,1999,33(3):357-361。
[32]肖人彬,王雪,费奇,周济,相关证据合成方法的研究,模式识别与人工智能,1993,6(3):227-234。
[33]Senthil Kumar A,Basu S K,Majumdar K L,Robust classification of multispectral data using multiple neural nteworks and fuzzy integral,IEEE Transactions on Geoscience and Remote Sensing,1997,35(3):787-790.
[34]Wang D,Wang X,Keller J M,Determining fuzzy integral densities using a genetic algorithm for pattern recognition,Fuzzy Information Processing Society,Annual Meeting of the North American,1997:263-267.
[35]Chaudhuri S P,Das S,Neural networks for data fusion,IEEE International Conference on Systems Engineering,1990:327-330.
[36]Wang Y,Goodman S D,Data fusion with neural networks,IEEE International Conference on Systems,1994,vol.1:640-645.
[37]Cires J,Romo P A,Zufiria P J,Comparative analysis of some neural network architectures for data fusion,IEEE International Conference on Neural Networks,1995,vol.1:79-83.
[38]Ruck D W,Rogers S K,Kabrisky M,Mills J P,Multisensor fusion classification with a multilayer perceptron,IJCNN International Joint Conference on Neural Networks,1990,vol.2:863-868.
[39]Loonis P,Zahzah E H,Bonnefoy J P,Multiclassifiers neural network fusion versus Dempster Shafer's orthogonal rule,IEEE International Conference on Neural Networks,1995,vol.4:2162-2165.
[40]Yair Shimshoni,Nathan Intrator,Classification of seismic signals by integrating ensembles of neural networks,IEEE Transactions on Signal Processing,1998,46(5):1194-1201.
[41]L.Lamport,R.Shostak,and M.Pease,"The Byzantine Generals Problem",ACM Trans.Programming Languages and Systems,vol.4,no.3,pp.382-401,July 1982.
[42]B.Krishnamachari,R.Bejar,"Distributed problem solving and the boundaries of self-configuration in multi-hop wireless networks",System Sciences,HICSS,2002.Page(s):3856 - 3865.
[43]李宏,安玮,徐晖,孙仲康,“一种多传感器信息融合点目标识别方法”,红外技术,1997,19(4):5-9。
[44]Ian F.Akyildiz,Weilian Su,Yogesh Sankarasubramaniam,and Erdal Cayirci,"A survey on sensor networks",IEEE Communications Magazine,pp.103-114,August,2002.
[45]Bhaskar Krishnamachari,Sitharama Iyengar,"Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks",IEEE Trans.Computers,Vol.53,No.3,pp.241-250,Mar.,2004.
[46]Xinjie Chang,"Networks Simulations with OPNET",Proceedings of the 1999 Winter Simulation Conference.
[47]Anna Hac,"Wireless Sensor Network Designs",John Wiley & Sons Ltd,2003.
[48]GI.Papadimitriou,A.S.Pomportsis,P.Nicopolitidis,M.S.Obaidt,"Wireless Networks",John Wiley & Sons Ltd,2003.
[49]王文博、张金文,《OPNET Modeler与网络仿真》,人民邮电出版社,2003。
[50]孙屹、孟晨,《OPNET通信仿真开发手册》,国防工业出版社,2005。
[2]孙利民等,《无线传感器网络》,清华大学出版社,2005年。
[3]Akyildiz L F,Su W,Sankarasubramaniam Y,Cayirici E,Wireless Sensor Networks:A surey,Computer Networks,2002,38(4):393-422.
[4]Estrin D,Girod L,Pottie G,Srivastava M,Instrumenting the World with Wireless Sensor Networks,Proceedings of the International Conference on Acoustics,Speech and Signal Processing,pp 233-239.
[5]Elson J,Estrin D,Wireless Sensor Networks:A Bridge to the Physical World.Wireless Sensor Networks,2004:3-20.
[6]Estrin D,Culler D,Pister K,G Sukhatme,Connecting the Physical World with Pervasive Networks.IEEE Pervasive Computing Archive,2002,1(1):59-69.
[7]Liu J,Cheung P,Zhao F,Guibas L,A dual-space approach to tracking and sensor management in wireless sensor networks,Palo Alto Research Center Technical Report,pp.2002-10077,March,2002.
[8]Gupta R,Das S R,Tracking moving targets in a smart sensor network,The VTC Fall 2003 Symposium,Oct.2003.
[9]Zhao F,Shin J,Reich J,Information-driven dynamic sensor collaboration for tracking applications.IEEE Signal Processing Magazine,March 2002.
[10]Brooks R R,Ramanathan P,Sayeed A M,Distributed target classification and tracking in sensor networks,Proc of the IEEE,August 2003,pp 1163-1171.
[11]Mechitov K,Sundresh S,Kwon Y,Agha G,Cooperative tracking with binary-detection sensor networks,Proc of SenSys '03,November,2003.
[12]T.Clouqueur,K.K.Saluja,and E Ramanathan,"Fault Tolerance in Collaborative Sensor Networks for Target Detection," IEEE Trans.Computers,pp.320-333,Vol.53,No.3,March 2004.
[13]H.Yoshino,N.Hayashibara and T.Enokido.Hierarchical Protocol for Byzantine Agreement in a Peer-to-Peer Overlay Network,Proc of IEEE DEXA,2005.
[14]Min Ding,Dechang Chen,Andrew Thaeler & Xiuzhen Cheng,"Fault-Tolerant Target Location in Sensor Networks," Wireless Communication and Networking,Vol.4,pp.2362-2368,March 2007.
[15]康耀红,《数据融合理论与应用》,西安电子科技大学出版社,1997年。
[16]孙涛等,“目标识别中的信息融合技术”,自动化仪表,2001年2期。
[17]R.Viswanathan and P.Varshney,"Distributed Detection with Multiple Sensors:Part Ⅰ-Fundamentals," Proc.IEEE,pp.54-63,Jan.1997.
[18]Firooz A.Sadjadi,"Hypotheses testing in a distributed environment," IEEE Transactions on Aerospace and Electronic Systems,1986,22(2):134-137.
[19]Reibman A R,Nolte L W."Design and performance comparison of distributed detection networks," IEEE Transactions on Aerospace and Elec2tronic Systems,1987,23(6):789-797.
[20]Chair Z,Varshney P K."Optimal data fusion in multiple sensor detection systems," IEEE Transactions on Aerospace and Electronic Systems,1986,22(1):98-101.
[21]Thomopoulos S C A,Viswanathan R,Bougoulias D K."Optimal decision fusion in multiple sensor systems," IEEE Transactions on Aerospace and Electronic Systems,1987,23(5):644-653.
[22]Thomopoulos S C A,Viswanathan R,Bougoulias D K."Optimal distributed decision fusion," IEEE Transactions on Aerospace and Electronic System,1989,25(5):761-765.
[23]Nirwan Ansari,Edwin S.H.Hou,Bin2ou Zhu,Jiang guo Chen."Adaptive fusion by reinforcement learning for distributed detection systems," IEEE Transactions on Aerospace and Electronic System,1996,32(2):524-530.
[24]康耀红,《数据融合理论与应用》,西安电子科技大学出版社,1997.
[25]张文修,梁怡,《不确定性推理》,西安交通大学出版社,1996。
[26]耿立恩,潘旭峰,李晓雷,祝嘉光,Dempster Sharer,证据推理在数据融合中的应用,北京理工大学学报,1997,17(2):198-203.
[27]潘旭峰,耿立恩,李晓雷,基于同源数据融和技术的机械故障诊断方法,机械强度,1997,19(3):6-10。
[28]Mitchell R A,Westerkamp J J,Robust statistical feature based aircraft identification,IEEE Transactions on Aerospace and Electronic Sys2tems,1999,35(3):1077-1093.
[29]Ayrulu B,Barshan B,Utete S W,Target identification with multiple logical sonars using evidential reasoning and simple majority voting,IEEE International Conference on Robotics and Automation,1997,vol.3:2063-2068.
[30]倪明,单渊达,多端交直流混合网扩展规划的综合法,电力系统自动化,1999,23(1):15-19。
[31]向阳,史习智,证据理论合成的一点修正,上海交通大学学报,1999,33(3):357-361。
[32]肖人彬,王雪,费奇,周济,相关证据合成方法的研究,模式识别与人工智能,1993,6(3):227-234。
[33]Senthil Kumar A,Basu S K,Majumdar K L,Robust classification of multispectral data using multiple neural nteworks and fuzzy integral,IEEE Transactions on Geoscience and Remote Sensing,1997,35(3):787-790.
[34]Wang D,Wang X,Keller J M,Determining fuzzy integral densities using a genetic algorithm for pattern recognition,Fuzzy Information Processing Society,Annual Meeting of the North American,1997:263-267.
[35]Chaudhuri S P,Das S,Neural networks for data fusion,IEEE International Conference on Systems Engineering,1990:327-330.
[36]Wang Y,Goodman S D,Data fusion with neural networks,IEEE International Conference on Systems,1994,vol.1:640-645.
[37]Cires J,Romo P A,Zufiria P J,Comparative analysis of some neural network architectures for data fusion,IEEE International Conference on Neural Networks,1995,vol.1:79-83.
[38]Ruck D W,Rogers S K,Kabrisky M,Mills J P,Multisensor fusion classification with a multilayer perceptron,IJCNN International Joint Conference on Neural Networks,1990,vol.2:863-868.
[39]Loonis P,Zahzah E H,Bonnefoy J P,Multiclassifiers neural network fusion versus Dempster Shafer's orthogonal rule,IEEE International Conference on Neural Networks,1995,vol.4:2162-2165.
[40]Yair Shimshoni,Nathan Intrator,Classification of seismic signals by integrating ensembles of neural networks,IEEE Transactions on Signal Processing,1998,46(5):1194-1201.
[41]L.Lamport,R.Shostak,and M.Pease,"The Byzantine Generals Problem",ACM Trans.Programming Languages and Systems,vol.4,no.3,pp.382-401,July 1982.
[42]B.Krishnamachari,R.Bejar,"Distributed problem solving and the boundaries of self-configuration in multi-hop wireless networks",System Sciences,HICSS,2002.Page(s):3856 - 3865.
[43]李宏,安玮,徐晖,孙仲康,“一种多传感器信息融合点目标识别方法”,红外技术,1997,19(4):5-9。
[44]Ian F.Akyildiz,Weilian Su,Yogesh Sankarasubramaniam,and Erdal Cayirci,"A survey on sensor networks",IEEE Communications Magazine,pp.103-114,August,2002.
[45]Bhaskar Krishnamachari,Sitharama Iyengar,"Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks",IEEE Trans.Computers,Vol.53,No.3,pp.241-250,Mar.,2004.
[46]Xinjie Chang,"Networks Simulations with OPNET",Proceedings of the 1999 Winter Simulation Conference.
[47]Anna Hac,"Wireless Sensor Network Designs",John Wiley & Sons Ltd,2003.
[48]GI.Papadimitriou,A.S.Pomportsis,P.Nicopolitidis,M.S.Obaidt,"Wireless Networks",John Wiley & Sons Ltd,2003.
[49]王文博、张金文,《OPNET Modeler与网络仿真》,人民邮电出版社,2003。
[50]孙屹、孟晨,《OPNET通信仿真开发手册》,国防工业出版社,2005。