无线传感器网络中能量有效的目标监测技术研究
|
摘要
基于无线传感器网络(WSN)的目标监测是无线传感器网络最广泛和最重要的应用之一,也是当前研究热点之一。由于具有自适应性、灵活性和低能耗性等特点,基于无线传感器网络的目标监测在很多实际应用中取代了雷达监测、卫星监测等传统监测方式,已经渗透到军事、商业、生态、社会生活等诸多领域,所以目标监测具有极高的现实研究意义。
基于无线传感器网络的目标监测应用中,能量有效性和监测精确性是主要的两个技术指标。由于无线传感器网络中能量有限,所以能量有效的目标监测技术一直是研究重点,我们既可以在MAC协议中加入休眠唤醒机制,也可以设计一定的网络结构,减少不必要的计算和数据传输来节能。而监测精确性包括了正确报警率和误报率,既要在监测到异常信息时准确报警又要避免过高的误报率对资源的极大浪费。针对不同的应用场景,目标监测技术的研究重点会有所不同。
根据应用场景中节点的配置情况,我们可以将应用场景分为两类:节点随机配置的应用和节点可配置的应用。针对不同的应用,本文分别研究了协作决策和节能MAC协议。
在节点随机配置的应用中,网络结构未知,我们更多关注于网络结构优化、节点协作和决策等。传统目标监测算法的二元决策会导致误报率过高,并且存在网络覆盖“盲点”问题。因而,本文提出了基于信用度的分布式目标监测算法——k-CD算法。该算法:首先,形成虚拟节点集;其次,匹配决策融合;最后,通过触发式移动节点来解决网络覆盖“盲点”问题。仿真结果表明,相对于已有算法,k-CD算法平均能提高35%的监测准确率的同时降低62%的误报率,在不同的网络覆盖情况下网络生命周期也平均延长44%。
在节点可配置的应用中,可以根据实际应用对网络节点进行配置,因此其网络结构是固定的,节点协作等过程也已经固化,我们可以从底层协议角度去进一步提高节能性。本文提出了基于LE-MAC的LETD算法。LETD算法中采用的LE-MAC协议结合了竞争式MAC和调度式MAC,根据应用环境我们可以固定一部分的休眠唤醒调度,同时也保留竞争式MAC的动态机制,可以在不同网络流量时动态调整休眠唤醒周期比。LETD算法网络为二级网络,采用了假性载波侦听、数据预测等机制,实验结果表明,LETD算法和现有算法相比,可以节能12%~48%。
在上述理论研究的基础上,我们开发了基于无线传感器网络的园林火灾监测系统,本文详细介绍了该系统的各个功能模块,特别是MAC协议以及相关支撑技术的设计和实现。实际测试数据表明,和采用现有MAC协议比较,系统可以节能约45%,生命周期大约可以延长一倍。
Target detection based on wireless sensor networks (WSNs) is one of the most extensive and important applications, and also one of the current research focuses. With adaptability, flexibility, low power consumption and other characteristics, target detection based on wireless sensor networks has replaced traditional monitoring methods such like radar monitoring, satellite monitoring in many practical applications. It is involved with the military, commercial, ecological, social life and many other fields, so target detection has very high significance of reality research.
In the applications of target detection based on wireless sensor networks, energy efficiency and detection accuracy is the main two technical indicators. Since the energy is limited in wireless sensor networks, energy-efficient detection technology has always been the research focus. We could save energy by either adding sleep mechanism in the MAC protocol or designing certain network structure to reduce the unnecessary computation and data transfer. The detection accuracy includes alarm accuracy and false alarm rate, making alarm while detecting the exception and but avoiding the waste of resources of excessive false alarms. With different scenarios, target detection technology research focuses are different.
According to the node configuration in the scenarios, we can classify the scenarios into two categories: applications with random node configuration and applications with configurable nodes. With different applications, this paper studies the cooperative decision-making and energy-efficient MAC protocols respectively.
In the applications with random node configuration, the network structure is unknown so we focus more on improving network structure, node collaboration and decision-making. Binary decision in traditional detection algorithms will lead to high false alarm rate, and there is "blind spot" coverage problem. Therefore, this paper presents a distributed target detection algorithm based on credit degree - k-CD algorithm. The algorithm: First, forming the virtual node set; Second, matching decision fusion; Finally, using trigger mobile node to solve the "blind spot" coverage problem. Simulation results show that compared with existing algorithms, k-CD algorithm can improve 35% detection accuracy and reduce false alarm rate of 62% in average, and the network life cycle can be extended by an average of 44% in different network coverage.
In the applications with configurable nodes, we can configure the network nodes according to practical applications so the network structure and the process of node collaboration are fixed. Energy efficient can be further improving by designing bottom protocol. This paper presents LETD algorithm based on LE-MAC. LE-MAC protocol combines the polling MAC and the scheduling one: part of the sleep-wake schedule can be fixed according to the application environment while maintaining a competitive dynamic MAC mechanism to adjust different network traffic. In LETD algorithm the network is two-dimensional with the false carrier sense and data prediction mechanism. The experimental results show that, LETD algorithm compared with existing algorithms, can make 12% ~ 48% more energy savings.
On the basis of the above theory research, we developed a fire detecting system based on wireless sensor networks. This paper describes the modules of the system, especially the design and implementation of MAC protocol and related technical support. Practical test data show that compared with the system with existing MAC protocols, the system can save energy of about 45% and extend the life cycle twice.
基于无线传感器网络的目标监测应用中,能量有效性和监测精确性是主要的两个技术指标。由于无线传感器网络中能量有限,所以能量有效的目标监测技术一直是研究重点,我们既可以在MAC协议中加入休眠唤醒机制,也可以设计一定的网络结构,减少不必要的计算和数据传输来节能。而监测精确性包括了正确报警率和误报率,既要在监测到异常信息时准确报警又要避免过高的误报率对资源的极大浪费。针对不同的应用场景,目标监测技术的研究重点会有所不同。
根据应用场景中节点的配置情况,我们可以将应用场景分为两类:节点随机配置的应用和节点可配置的应用。针对不同的应用,本文分别研究了协作决策和节能MAC协议。
在节点随机配置的应用中,网络结构未知,我们更多关注于网络结构优化、节点协作和决策等。传统目标监测算法的二元决策会导致误报率过高,并且存在网络覆盖“盲点”问题。因而,本文提出了基于信用度的分布式目标监测算法——k-CD算法。该算法:首先,形成虚拟节点集;其次,匹配决策融合;最后,通过触发式移动节点来解决网络覆盖“盲点”问题。仿真结果表明,相对于已有算法,k-CD算法平均能提高35%的监测准确率的同时降低62%的误报率,在不同的网络覆盖情况下网络生命周期也平均延长44%。
在节点可配置的应用中,可以根据实际应用对网络节点进行配置,因此其网络结构是固定的,节点协作等过程也已经固化,我们可以从底层协议角度去进一步提高节能性。本文提出了基于LE-MAC的LETD算法。LETD算法中采用的LE-MAC协议结合了竞争式MAC和调度式MAC,根据应用环境我们可以固定一部分的休眠唤醒调度,同时也保留竞争式MAC的动态机制,可以在不同网络流量时动态调整休眠唤醒周期比。LETD算法网络为二级网络,采用了假性载波侦听、数据预测等机制,实验结果表明,LETD算法和现有算法相比,可以节能12%~48%。
在上述理论研究的基础上,我们开发了基于无线传感器网络的园林火灾监测系统,本文详细介绍了该系统的各个功能模块,特别是MAC协议以及相关支撑技术的设计和实现。实际测试数据表明,和采用现有MAC协议比较,系统可以节能约45%,生命周期大约可以延长一倍。
Target detection based on wireless sensor networks (WSNs) is one of the most extensive and important applications, and also one of the current research focuses. With adaptability, flexibility, low power consumption and other characteristics, target detection based on wireless sensor networks has replaced traditional monitoring methods such like radar monitoring, satellite monitoring in many practical applications. It is involved with the military, commercial, ecological, social life and many other fields, so target detection has very high significance of reality research.
In the applications of target detection based on wireless sensor networks, energy efficiency and detection accuracy is the main two technical indicators. Since the energy is limited in wireless sensor networks, energy-efficient detection technology has always been the research focus. We could save energy by either adding sleep mechanism in the MAC protocol or designing certain network structure to reduce the unnecessary computation and data transfer. The detection accuracy includes alarm accuracy and false alarm rate, making alarm while detecting the exception and but avoiding the waste of resources of excessive false alarms. With different scenarios, target detection technology research focuses are different.
According to the node configuration in the scenarios, we can classify the scenarios into two categories: applications with random node configuration and applications with configurable nodes. With different applications, this paper studies the cooperative decision-making and energy-efficient MAC protocols respectively.
In the applications with random node configuration, the network structure is unknown so we focus more on improving network structure, node collaboration and decision-making. Binary decision in traditional detection algorithms will lead to high false alarm rate, and there is "blind spot" coverage problem. Therefore, this paper presents a distributed target detection algorithm based on credit degree - k-CD algorithm. The algorithm: First, forming the virtual node set; Second, matching decision fusion; Finally, using trigger mobile node to solve the "blind spot" coverage problem. Simulation results show that compared with existing algorithms, k-CD algorithm can improve 35% detection accuracy and reduce false alarm rate of 62% in average, and the network life cycle can be extended by an average of 44% in different network coverage.
In the applications with configurable nodes, we can configure the network nodes according to practical applications so the network structure and the process of node collaboration are fixed. Energy efficient can be further improving by designing bottom protocol. This paper presents LETD algorithm based on LE-MAC. LE-MAC protocol combines the polling MAC and the scheduling one: part of the sleep-wake schedule can be fixed according to the application environment while maintaining a competitive dynamic MAC mechanism to adjust different network traffic. In LETD algorithm the network is two-dimensional with the false carrier sense and data prediction mechanism. The experimental results show that, LETD algorithm compared with existing algorithms, can make 12% ~ 48% more energy savings.
On the basis of the above theory research, we developed a fire detecting system based on wireless sensor networks. This paper describes the modules of the system, especially the design and implementation of MAC protocol and related technical support. Practical test data show that compared with the system with existing MAC protocols, the system can save energy of about 45% and extend the life cycle twice.
引文
[1] Cullar D, Estrin D, Strvastava M. 2004. Overview of sensor network[J]. Computer, 2004, 37(8): 41-49.
[2] Akyildiz I F, Su Weilian, Sankarasubramaniam Y, et al. 2002. A survey on sensor networks[C].Communications Magazine, IEEE, 40(8): 102-114.
[3]孙利民,李建中,陈渝等. 2005.无线传感器网络[M].北京:清华大学出版社.
[4]崔莉,鞠海玲,苗勇. 2005.无线传感器网络研究进展[J].计算机研究与发展42(1):163-179.
[5] The Cricket Indoor Location System. http://cricket.csail.mit.edu/.
[6] Fontana R J, Richley E, Barney J A. 2003. Commercialization of an ultra wideband precision asset location system[C]. IEEE Conf. on Ultra Wideband Systems and Technologies, Reston, VA, 369-373.
[7] Amaldi E, Capone A, Cesana M, et al. 2008. Coverage planning of wireless sensors for mobile target detection[C]. IEEE MASS, Atlanta, GA, 48-57.
[8] Clouqueur T, Phipatanasuphorn V, Ramanathan P, et al. 2003. Sensor Deployment Strategy for Detection of Targets Traversing a Region[J]. MOBILE NETWORKS AND APPLICATIONS, 8(4): 453-461.
[9] Yu L, Yuan L, Qu G, et al. 2006. A Energy-driven Detection Scheme with Guaranteed Accuracy[C]. IPSN, Nashville, TN, 284-291.
[10] Wang W, Srinivasan V, Chua K C, et al. 2007. Energy-efficient Coverage for Target Detection in Wireless Sensor Networks[C]. IPSN, Cambridge, MA, 313-322.
[11] Wei P, Chen J. 2004. Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks[J]. IEEE Transactions on Mobile Computing, 3(3), 258-271.
[12] Duarte M, Hu Y. 2004. Distance based decision fusion in a distributed wireless sensor network[J]. Telecommunication Systems, 26(2): 339-350.
[13] Katenka N, Levina E, Michailidis G. 2008. Local vote decision fusion for target detection in wireless sensor network[J]. IEEE Trans on Signal Processing, 56(1): 329-338.
[14] Chin T L, Ramanathan P, Saluja K K. 2006. Analytic Modeling of Detection Latency in Mobile Sensor Networks[C]. IPSN, Nashville, TN, 194-201.
[15] Chin T L, Ramanathan P, Saluja K K, et al. 2005. Exposure for Collaborative Detection using Mobile Sensor Networks[C]. Mobile Ad-hoc and Sensor Systems Conference, Washington, DC, 738-750.
[16] Movement-assisted Sensor Deployment. Mobile Computing, IEEE Transactions on, 2006(5): 640-652
[17] Liu B Y, Brass P, Dousse O, et al. 2005. Mobility imporves coverage of sensor networks[C]. Proc of the 6th ACM Int Symp on Mobile ad hoc Networking and Computing. New York, 300-308.
[18]李响. 2009.无线传感网络节点自定位技术的研究[D]:[硕士].合肥:中国科学技术大学,16-30.
[19] Priyantha N, Balakrishnan H, Demaine E, et al. 2003. Anchor-free distributed localization in sensor networks[R]. Technical Report TR-892, MIT LCS.
[20] Savvides a, Han C C, Srivastava M B. 2001. Dynamic fine-grained localization in ad-hoc networks of sensors[M], MobiCom, Rome, Italy, 166-179.
[21] Niculescn D, Badri N. 2003. Ad hoc positioning system (APS) using AOA[C]. INFOCOM, vol.3, 1734-1743.
[22] Bahl P, Padmanabhan V N. 2000. RADAR: an in-building RF-based user location and tracking system[C]. INFOCOM, vol.2, 775-784.
[23] Bulusu N, Heidemann J, Estrin D. 2001. Density Adaptive Algorithm for Beacon Placement in Wireless Sensor Networks[C]. ICDCS, Phoenix, AZ, 489-498.
[24] Bulusu N, Heidemann J, Estrin D. 2000. GPS less low-cost outdoor localization for very small devices[J]. Personal Communications, IEEE, 7(5), 28-34.
[25] Nagpal R, Shrobe H, Bachrach J. 2003. Organizing a global coordinate system from local information on an ad hoc sensor network. IPSN, Palo Alto.
[26] He T, Huang C, Blum B, et al. 2003. Range-free localization schemes for large scale networks[C]. MobiCom, New York, 81-95.
[27] Liu C, Wu K, He T. 2004. Sensor localization with ring overlapping based on comparison of received signal strength indicator[C]. Mobile Ad-hoc and Sensor Systems, 516-518.
[28] Yedavalli K, Krishnamachari B. 2008. Sequence-based localization in wireless sensor networks[J]. Mobile Computing, IEEE Transactions, 7(1), 81-94.
[29] Xu Y, Heidemann J, estrin D. 2001. Geography-informed energy conservation for ad hoc routing[C]. MobiCom, Rome, Italy, 70-84.
[30] Heinzelman W R, Chandrakasan A, Balakrishnan H. 2002. An application-specific protocol architecture for wireless microsensor networks[J]. IEEE Transactions on Wireless Communications, 1(4), 660-670.
[31] Deb B, Bhatnagar S, Nath B. 2001. A topology discovery algorithm for sensor networks with applications to network management. DCS Technical Report DCS-TR-441, RutgersUniversity.
[32] Slijepcevic S, Potkonjak M. 2001. Power efficient organization of wireless sensor networks.ICC, Helsinki, 472-476.
[33] Lin F Y S, Chiu P L. 2005. A near-optimal sensor placement algorithm to achieve complete coverage/discrimination in sensor networks[J]. IEEE Communications Letter, 9(1), 43-45.
[34] Megerian S, Koushanfar F, Potkonjak M, et al. 2005. Worst and best-case coverage in sensor networks[J]. IEEE Trans. On Mobile Computing, 4(1), 84-92.
[35] Huang C F, Tseng Y C. 2005. A survey of solutions to the coverage problems in wireless sensor networks[J]. Journal of Internet Technology, 6(1), 1-8.
[36]张丽.2007.无线传感执行网络中协作机制及算法的研究[D]:[硕士].武汉:武汉理工大学,15-21.
[37] IEEE Computer Society LAN MAN Standards Committee. IEEE Std 802.11-1999, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications[R]. 1999.
[38] Ye W, Heidemann J, Estrin D. 2002. An energy-efficient MAC protocol for wireless sensor networks[C]. Sensys, New York, NY.
[39] Van D T, Langendoen K. 2003. An adaptive energy-efficient MAC protocol for wireless sensor networks[C]. Sensys, Los Angeles, CA.
[40] Bao L, Garicia L A J J. 2001. A new approach to channel access scheduling for ad hoc networks[C]. MobiCom, Rome, Italy, 210-221.
[41] Rajendran V, Obraczka K, Garicia L A J J. 2003 Energy-efficient, collision-free medium access control for wireless sensor networks[C]. Sensys, Los Angeles, CA, 181-192.
[42] Sohrabi K, Gao J, Ailawadhi V, et al. 2000. Protocols for self-organization of a wireless sensor network[J]. IEEE Personal Communications Magazine, 7(5), 16-27.
[43] Nemeroff J, Garcia L, Hampled, et al. Application of sensor network communication[C]. //Military Communications Conference, 336-341.
[44] Mainwaring a, Polastre J, Szewczyk R, et al. 2002. Wireless Sensor Networks for habitat monitoring[C]. ACM Int Workshop on Wireless Sensor Networks and Applications.
[45] Szewczyk R, Osterweil E, Polastre j, et al. 2004. Habitat monitoring with sensor networks[J]. Communications of the ACM, 47(6), 34-40.
[46] http://www.xbow.com.
[47] Kottaoalli V, Kiremidjiana, Lynch J, et al. 2003. Two-tiered wireless sensor network architecture for structural health monitoring[C]. SPIE 10th Annual Int. Symposium on Smart Structures and Materials.
[48] Chintalapudi K, Paek J, Gnawali O, et al. 2005. Embedded Sensing of Structures: A RealityCheck[C]. RTCSA, 95-101.
[49] Jafari R, Encarnacao A, Zahoory A, et al. 2005. Wireless sensor networks for health monitoring[C]. MobiQuitous, 479-481.
[50] Jia X Y, Wang C. 2006. The security routing research for WSN in the application of Intelligent transport system[C]. Proceedings of the 2006 Int. Conference on Mechatronics and Automation, 2318-2323.
[51] Li D, Wong Y, Hu Y, et al. 2002. Detection, Classification and Tracking of Targets in Distributed Sensor Networks[J]. IEEE Signal Processing Magazine, 19(2): 17-29.
[52] Cerpa A, Elson J, Estrin D, et al. 2001. Habitat Monitoring: Application Driver for Wireless Communications Technology[C]. ACM SIGOMM Workshop on Data Communications in Latin American and the Caribbean, 20-41.
[53] Xing g, Wang X, Zhang Y, et al. 2003. Integrated Coverage and Connectivity Configuration in Wireless Sensor Netwoks[C]. ACM Sensys, 28-39.
[54] Clouqueur T, Saluja K K, Ramananathan P. 2004. Fault Tolerance in Collaborative Sensor Networks for Target Detection[J]. IEEE Trans. Computers, 53(3): 320-333.
[55] Niu R, Varshney P K, Moore M, et al. 2005. Decision fusion in a wireless sensor network with random number of sensors[C]. Acoustics, Speech, and Signal Processing, vol.4: 861-864.
[56] Niu R, Xing G L, Wang J P, et al. 2008. Collaborative Target Detection in Wireless Sensor Networks with Reactive Mobility[C]. Quality of Service, 2008. IWQoS 2008. 16th International Workshop on, 150-159.
[57] Xue W, Jun J M, Sheng W, et al. 2007. Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks[J]. Energy Efficiency and Intelligent Signal Processing for Wireless Sensing, vol.7:1193-1215.
[58] Varshney P. 1996. Distributed Detection and Data Fusion[M]. NY: Spinger-Verlag.
[59] Srinivasan W W V, Chua K C. 2007. Trade-offs between Mobility and Density for Coverage in Wireless Sensor Networks[C]. Mobicom, 2007:39-50
[60] http://www.tct.hut.fi/~esa/java/rwp/rwp-model.shtml
[61] Sun T, Chen L J, Han C C, et al. 2005. Reliable Sensor Networks for Planet Exploration[C]. Networking, Sensing and Control, 2005. Proceedings, 816-821.
[62]李洪峻李迅马宏绪. 2009.无线传感器网络MAC协议实时性研究[J].计算机工程35(23).
[63] Lu G, Krishnamachari B, Raghavendra C. 2004. An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks[C]. In: Proc 18th Int’lParallel and Distributed Processing Symp (IPDPS‘04), Santa Fe, New Mexico, 224~230.
[64] Elson J, Griod L, Esrein D. 2002. Fine-grained network time synchronization using reference broadcasts[C]. In: Proc 5th Symp Operating Systems Design and Implementation ( OSDI 2002 ), Boston, MA, 147-163.
[65] Elson J, Romer K. 2002. Wireless sensor networks: A new regime for time synchronization[C]. In: Proc 1st Workshop on Hot Topics in Networks (HotNets-I), Princeton, NJ, 149-154.
[2] Akyildiz I F, Su Weilian, Sankarasubramaniam Y, et al. 2002. A survey on sensor networks[C].Communications Magazine, IEEE, 40(8): 102-114.
[3]孙利民,李建中,陈渝等. 2005.无线传感器网络[M].北京:清华大学出版社.
[4]崔莉,鞠海玲,苗勇. 2005.无线传感器网络研究进展[J].计算机研究与发展42(1):163-179.
[5] The Cricket Indoor Location System. http://cricket.csail.mit.edu/.
[6] Fontana R J, Richley E, Barney J A. 2003. Commercialization of an ultra wideband precision asset location system[C]. IEEE Conf. on Ultra Wideband Systems and Technologies, Reston, VA, 369-373.
[7] Amaldi E, Capone A, Cesana M, et al. 2008. Coverage planning of wireless sensors for mobile target detection[C]. IEEE MASS, Atlanta, GA, 48-57.
[8] Clouqueur T, Phipatanasuphorn V, Ramanathan P, et al. 2003. Sensor Deployment Strategy for Detection of Targets Traversing a Region[J]. MOBILE NETWORKS AND APPLICATIONS, 8(4): 453-461.
[9] Yu L, Yuan L, Qu G, et al. 2006. A Energy-driven Detection Scheme with Guaranteed Accuracy[C]. IPSN, Nashville, TN, 284-291.
[10] Wang W, Srinivasan V, Chua K C, et al. 2007. Energy-efficient Coverage for Target Detection in Wireless Sensor Networks[C]. IPSN, Cambridge, MA, 313-322.
[11] Wei P, Chen J. 2004. Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks[J]. IEEE Transactions on Mobile Computing, 3(3), 258-271.
[12] Duarte M, Hu Y. 2004. Distance based decision fusion in a distributed wireless sensor network[J]. Telecommunication Systems, 26(2): 339-350.
[13] Katenka N, Levina E, Michailidis G. 2008. Local vote decision fusion for target detection in wireless sensor network[J]. IEEE Trans on Signal Processing, 56(1): 329-338.
[14] Chin T L, Ramanathan P, Saluja K K. 2006. Analytic Modeling of Detection Latency in Mobile Sensor Networks[C]. IPSN, Nashville, TN, 194-201.
[15] Chin T L, Ramanathan P, Saluja K K, et al. 2005. Exposure for Collaborative Detection using Mobile Sensor Networks[C]. Mobile Ad-hoc and Sensor Systems Conference, Washington, DC, 738-750.
[16] Movement-assisted Sensor Deployment. Mobile Computing, IEEE Transactions on, 2006(5): 640-652
[17] Liu B Y, Brass P, Dousse O, et al. 2005. Mobility imporves coverage of sensor networks[C]. Proc of the 6th ACM Int Symp on Mobile ad hoc Networking and Computing. New York, 300-308.
[18]李响. 2009.无线传感网络节点自定位技术的研究[D]:[硕士].合肥:中国科学技术大学,16-30.
[19] Priyantha N, Balakrishnan H, Demaine E, et al. 2003. Anchor-free distributed localization in sensor networks[R]. Technical Report TR-892, MIT LCS.
[20] Savvides a, Han C C, Srivastava M B. 2001. Dynamic fine-grained localization in ad-hoc networks of sensors[M], MobiCom, Rome, Italy, 166-179.
[21] Niculescn D, Badri N. 2003. Ad hoc positioning system (APS) using AOA[C]. INFOCOM, vol.3, 1734-1743.
[22] Bahl P, Padmanabhan V N. 2000. RADAR: an in-building RF-based user location and tracking system[C]. INFOCOM, vol.2, 775-784.
[23] Bulusu N, Heidemann J, Estrin D. 2001. Density Adaptive Algorithm for Beacon Placement in Wireless Sensor Networks[C]. ICDCS, Phoenix, AZ, 489-498.
[24] Bulusu N, Heidemann J, Estrin D. 2000. GPS less low-cost outdoor localization for very small devices[J]. Personal Communications, IEEE, 7(5), 28-34.
[25] Nagpal R, Shrobe H, Bachrach J. 2003. Organizing a global coordinate system from local information on an ad hoc sensor network. IPSN, Palo Alto.
[26] He T, Huang C, Blum B, et al. 2003. Range-free localization schemes for large scale networks[C]. MobiCom, New York, 81-95.
[27] Liu C, Wu K, He T. 2004. Sensor localization with ring overlapping based on comparison of received signal strength indicator[C]. Mobile Ad-hoc and Sensor Systems, 516-518.
[28] Yedavalli K, Krishnamachari B. 2008. Sequence-based localization in wireless sensor networks[J]. Mobile Computing, IEEE Transactions, 7(1), 81-94.
[29] Xu Y, Heidemann J, estrin D. 2001. Geography-informed energy conservation for ad hoc routing[C]. MobiCom, Rome, Italy, 70-84.
[30] Heinzelman W R, Chandrakasan A, Balakrishnan H. 2002. An application-specific protocol architecture for wireless microsensor networks[J]. IEEE Transactions on Wireless Communications, 1(4), 660-670.
[31] Deb B, Bhatnagar S, Nath B. 2001. A topology discovery algorithm for sensor networks with applications to network management. DCS Technical Report DCS-TR-441, RutgersUniversity.
[32] Slijepcevic S, Potkonjak M. 2001. Power efficient organization of wireless sensor networks.ICC, Helsinki, 472-476.
[33] Lin F Y S, Chiu P L. 2005. A near-optimal sensor placement algorithm to achieve complete coverage/discrimination in sensor networks[J]. IEEE Communications Letter, 9(1), 43-45.
[34] Megerian S, Koushanfar F, Potkonjak M, et al. 2005. Worst and best-case coverage in sensor networks[J]. IEEE Trans. On Mobile Computing, 4(1), 84-92.
[35] Huang C F, Tseng Y C. 2005. A survey of solutions to the coverage problems in wireless sensor networks[J]. Journal of Internet Technology, 6(1), 1-8.
[36]张丽.2007.无线传感执行网络中协作机制及算法的研究[D]:[硕士].武汉:武汉理工大学,15-21.
[37] IEEE Computer Society LAN MAN Standards Committee. IEEE Std 802.11-1999, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications[R]. 1999.
[38] Ye W, Heidemann J, Estrin D. 2002. An energy-efficient MAC protocol for wireless sensor networks[C]. Sensys, New York, NY.
[39] Van D T, Langendoen K. 2003. An adaptive energy-efficient MAC protocol for wireless sensor networks[C]. Sensys, Los Angeles, CA.
[40] Bao L, Garicia L A J J. 2001. A new approach to channel access scheduling for ad hoc networks[C]. MobiCom, Rome, Italy, 210-221.
[41] Rajendran V, Obraczka K, Garicia L A J J. 2003 Energy-efficient, collision-free medium access control for wireless sensor networks[C]. Sensys, Los Angeles, CA, 181-192.
[42] Sohrabi K, Gao J, Ailawadhi V, et al. 2000. Protocols for self-organization of a wireless sensor network[J]. IEEE Personal Communications Magazine, 7(5), 16-27.
[43] Nemeroff J, Garcia L, Hampled, et al. Application of sensor network communication[C]. //Military Communications Conference, 336-341.
[44] Mainwaring a, Polastre J, Szewczyk R, et al. 2002. Wireless Sensor Networks for habitat monitoring[C]. ACM Int Workshop on Wireless Sensor Networks and Applications.
[45] Szewczyk R, Osterweil E, Polastre j, et al. 2004. Habitat monitoring with sensor networks[J]. Communications of the ACM, 47(6), 34-40.
[46] http://www.xbow.com.
[47] Kottaoalli V, Kiremidjiana, Lynch J, et al. 2003. Two-tiered wireless sensor network architecture for structural health monitoring[C]. SPIE 10th Annual Int. Symposium on Smart Structures and Materials.
[48] Chintalapudi K, Paek J, Gnawali O, et al. 2005. Embedded Sensing of Structures: A RealityCheck[C]. RTCSA, 95-101.
[49] Jafari R, Encarnacao A, Zahoory A, et al. 2005. Wireless sensor networks for health monitoring[C]. MobiQuitous, 479-481.
[50] Jia X Y, Wang C. 2006. The security routing research for WSN in the application of Intelligent transport system[C]. Proceedings of the 2006 Int. Conference on Mechatronics and Automation, 2318-2323.
[51] Li D, Wong Y, Hu Y, et al. 2002. Detection, Classification and Tracking of Targets in Distributed Sensor Networks[J]. IEEE Signal Processing Magazine, 19(2): 17-29.
[52] Cerpa A, Elson J, Estrin D, et al. 2001. Habitat Monitoring: Application Driver for Wireless Communications Technology[C]. ACM SIGOMM Workshop on Data Communications in Latin American and the Caribbean, 20-41.
[53] Xing g, Wang X, Zhang Y, et al. 2003. Integrated Coverage and Connectivity Configuration in Wireless Sensor Netwoks[C]. ACM Sensys, 28-39.
[54] Clouqueur T, Saluja K K, Ramananathan P. 2004. Fault Tolerance in Collaborative Sensor Networks for Target Detection[J]. IEEE Trans. Computers, 53(3): 320-333.
[55] Niu R, Varshney P K, Moore M, et al. 2005. Decision fusion in a wireless sensor network with random number of sensors[C]. Acoustics, Speech, and Signal Processing, vol.4: 861-864.
[56] Niu R, Xing G L, Wang J P, et al. 2008. Collaborative Target Detection in Wireless Sensor Networks with Reactive Mobility[C]. Quality of Service, 2008. IWQoS 2008. 16th International Workshop on, 150-159.
[57] Xue W, Jun J M, Sheng W, et al. 2007. Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks[J]. Energy Efficiency and Intelligent Signal Processing for Wireless Sensing, vol.7:1193-1215.
[58] Varshney P. 1996. Distributed Detection and Data Fusion[M]. NY: Spinger-Verlag.
[59] Srinivasan W W V, Chua K C. 2007. Trade-offs between Mobility and Density for Coverage in Wireless Sensor Networks[C]. Mobicom, 2007:39-50
[60] http://www.tct.hut.fi/~esa/java/rwp/rwp-model.shtml
[61] Sun T, Chen L J, Han C C, et al. 2005. Reliable Sensor Networks for Planet Exploration[C]. Networking, Sensing and Control, 2005. Proceedings, 816-821.
[62]李洪峻李迅马宏绪. 2009.无线传感器网络MAC协议实时性研究[J].计算机工程35(23).
[63] Lu G, Krishnamachari B, Raghavendra C. 2004. An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks[C]. In: Proc 18th Int’lParallel and Distributed Processing Symp (IPDPS‘04), Santa Fe, New Mexico, 224~230.
[64] Elson J, Griod L, Esrein D. 2002. Fine-grained network time synchronization using reference broadcasts[C]. In: Proc 5th Symp Operating Systems Design and Implementation ( OSDI 2002 ), Boston, MA, 147-163.
[65] Elson J, Romer K. 2002. Wireless sensor networks: A new regime for time synchronization[C]. In: Proc 1st Workshop on Hot Topics in Networks (HotNets-I), Princeton, NJ, 149-154.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |