无线传感器网络自身健康状态融合机制研究
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摘要
由大量随机部署于监测区域的传感器节点通过无线自组织所形成的网络能够协作地完成各种监测任务,如:战场信息收集、有毒气体监测、煤矿安全监测、海洋生态监控等,这些环境十分复杂,可能导致节点工作不稳定,甚至失效,不能完成正常监测任务。通过对无线传感器网络的自身健康状态进行监视,可及时了解网络的故障区域并采取相应措施,使得网络能够迅速从故障中恢复从而保证网络能够提供正常服务。然而,无线传感器网络是资源受限的网络,如果将每个节点所收集的健康状态消息不加处理发送给sink节点将导致网络节点特别是靠近sink的节点转发大量消息,将使节点因能耗过大而过早死亡。因此有必要对其自身健康状态进行融合。本课题来源于国家自然科学基金,对无线传感器网络自身健康状态的融合机制进行研究。
论文介绍了无线传感器网络中数据融合方法的研究现状,针对无线传感器网络自身健康状态消息的整网汇报、周期性汇报特点,提出一种基于网格划分的融合树构建方法。该方法根据节点的地理位置信息将整个网络划分为网格,每个网格形成一个簇,根据节点的剩余能量及其所处的地理位置选取簇头节点,簇头节点因负责自身健康状态消息的融合、与成员节点和其他簇之间的通信,能量消耗较其他成员节点更快。为了避免簇头节点过早死亡和周期性成簇造成的不必要能量消耗,本文采取局部替换簇头节点的方法,即当簇头节点的能量低于其当选簇头时能量的一半时,则重新选取该簇的簇头。
在网格簇中,论文假设多节点覆盖区域中的信息只要被一个节点感知并成功上报,那么该区域中的信息都能被感知并上报,设计了对簇内感知覆盖与簇成员节点到簇头节点的链路状态进行融合的算法。
仿真实验表明,通过构建融合树,降低了平均能耗,从而延长了网络的寿命;在网格簇中,本文提出的自身健康状态融合算法能够较准确地反映网络中簇区域的节点健康状态。
The network constituted by a large number of sensor nodes which deployed randomly in the monitored area by means of wireless self-organization can be used for various of monitoring tasks cooperatively, such as battle field information collection, toxic gas monitoring, coal mine safety monitoring, and marine ecological monitoring. These environments are very complex, which may lead to instable work of the sensor nodes or even failure, so that can’t competent for the monitoring task. By monitoring the self health information in wireless sensor network, the failure area can be known in time and corresponding actions can be taken, which assures the network recover from failure quickly to provide the monitoring task normally. However, wireless sensor networks are resource-constrained. If each node sends the monitored health information to the sink without any processing, the nodes, especially the nodes near the sink will forward a large number of packets, which will lead premature death of the node due to a large amount of energy consumption. So the aggregating of the self health status becomes necessary. This study comes from National Nature Science Foundation and focus on self health status aggregation.
The thesis introduces the current state of the art of data fusion in wireless sensor network. For the characteristic of the self health information which is reported periodically by all the nodes, the thesis constructs an aggregation tree based on grid. The aggregation tree divides the network into grids according to the position information of the node, each grid forms a cluster. The cluster head is elected not only considers the residual energy, but also considers the position information. The cluster head will consume more energy due to take responsibility of data fusion and communicating with cluster members and other clusters. In order to avoid premature death of the cluster head and unnecessary energy consumption in reconstructing aggregation tree, the cluster head is replaced locally. Namely, the new cluster head will be reelected if the residual energy of the cluster head is less than half of the energy when it becomes cluster head.
In the grid cluster, the thesis designs an aggregation algorithm that aggregates the coverage in the cluster and the link quality of the member nodes to cluster head based on the assumption that the information in a multi-node covered area can be detected and reported only if at least one of the nodes detects and reports it successfully,
Simulation experiments indicate that the network consumes lesser energy and enjoys longer network lifetime through the aggregation tree. The self health status aggregation algorithm proposed in the thesis can accurately reflect the health information of the nodes in the cluster area.
论文介绍了无线传感器网络中数据融合方法的研究现状,针对无线传感器网络自身健康状态消息的整网汇报、周期性汇报特点,提出一种基于网格划分的融合树构建方法。该方法根据节点的地理位置信息将整个网络划分为网格,每个网格形成一个簇,根据节点的剩余能量及其所处的地理位置选取簇头节点,簇头节点因负责自身健康状态消息的融合、与成员节点和其他簇之间的通信,能量消耗较其他成员节点更快。为了避免簇头节点过早死亡和周期性成簇造成的不必要能量消耗,本文采取局部替换簇头节点的方法,即当簇头节点的能量低于其当选簇头时能量的一半时,则重新选取该簇的簇头。
在网格簇中,论文假设多节点覆盖区域中的信息只要被一个节点感知并成功上报,那么该区域中的信息都能被感知并上报,设计了对簇内感知覆盖与簇成员节点到簇头节点的链路状态进行融合的算法。
仿真实验表明,通过构建融合树,降低了平均能耗,从而延长了网络的寿命;在网格簇中,本文提出的自身健康状态融合算法能够较准确地反映网络中簇区域的节点健康状态。
The network constituted by a large number of sensor nodes which deployed randomly in the monitored area by means of wireless self-organization can be used for various of monitoring tasks cooperatively, such as battle field information collection, toxic gas monitoring, coal mine safety monitoring, and marine ecological monitoring. These environments are very complex, which may lead to instable work of the sensor nodes or even failure, so that can’t competent for the monitoring task. By monitoring the self health information in wireless sensor network, the failure area can be known in time and corresponding actions can be taken, which assures the network recover from failure quickly to provide the monitoring task normally. However, wireless sensor networks are resource-constrained. If each node sends the monitored health information to the sink without any processing, the nodes, especially the nodes near the sink will forward a large number of packets, which will lead premature death of the node due to a large amount of energy consumption. So the aggregating of the self health status becomes necessary. This study comes from National Nature Science Foundation and focus on self health status aggregation.
The thesis introduces the current state of the art of data fusion in wireless sensor network. For the characteristic of the self health information which is reported periodically by all the nodes, the thesis constructs an aggregation tree based on grid. The aggregation tree divides the network into grids according to the position information of the node, each grid forms a cluster. The cluster head is elected not only considers the residual energy, but also considers the position information. The cluster head will consume more energy due to take responsibility of data fusion and communicating with cluster members and other clusters. In order to avoid premature death of the cluster head and unnecessary energy consumption in reconstructing aggregation tree, the cluster head is replaced locally. Namely, the new cluster head will be reelected if the residual energy of the cluster head is less than half of the energy when it becomes cluster head.
In the grid cluster, the thesis designs an aggregation algorithm that aggregates the coverage in the cluster and the link quality of the member nodes to cluster head based on the assumption that the information in a multi-node covered area can be detected and reported only if at least one of the nodes detects and reports it successfully,
Simulation experiments indicate that the network consumes lesser energy and enjoys longer network lifetime through the aggregation tree. The self health status aggregation algorithm proposed in the thesis can accurately reflect the health information of the nodes in the cluster area.
引文
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[7] Souto Eduardo, Gomes Reinaldo, Sadok Djamel, Kelner Judith. Sampling Energy Consumption in Wireless Sensor Networks[C]. In: Proc. of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing ,vol.1, Taichung, Taiwan, 2006: 284-287.
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[28] Zhu Yujie, Vedantham Ramanuja, Park Seung-Jong, Sivakumar Raghupathy. A Scalable Correlation Aware Aggregation Strategy for Wireless Sensor Networks[C]. In: Proc. of the first International conference on wireless internet, Budapest, Hungary, 2005: 122-129.
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[30] Yu Liyang, Wang Neng, Zhang Wei, Zheng Chunlei. GROUP: a Grid-clustering Routing Protocol for Wireless Sensor Networks[C]. In: Proc. of 2006 IEEE International Conference on Wireless Communications, Networkding and Mobile Computing (WiCOM 2006),vol.3, Wuhan, China, 2006: 1-5.
[31] Zhao Yonggang Jerry, Govindan Ramesh, Estrin Deborah. Residual energy scan for monitoring sensor networks[C]. In: Proc. of Proceedings of the IEEE wireless communications andNetworking conference, WCNC2002,vol.1, Orlando, Florida, USA , 2002: 356-362.
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[34]周平,张胜,舒坚.基于预测模型的WSN节点能量融合机制[J].计算机工程, 2010,36(1):110-111, 130.
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[2]于宏毅,李鸥,张效义.无线传感器网络理论、技术与实现[M].北京:国防工业出版社, 2008.
[3]于海斌,曾鹏,梁韡.智能无线传感器网络系统[M].北京:科学出版社, 2006.
[4] Wang Yu, Wu Hongyi, Nelavelli Ravi, Tzeng NianFeng. Balance-Based Energy-Efficient Communication Protocols for Wireless Sensor Networks[C]. In: Proc. of the 26th IEEE International Conference on Distributed Computing Systems Workshop (ICDCSW'06), 2006.
[5]汪泉弟,孔卫,李彬,张占龙,杨永明.事件驱动型无线传感器网络生存期分析[J].重庆大学学报(自然科学版), 2007,30(6):30-34.
[6]刘慧中.事件驱动的传感器网络分簇路由协议研究[D].长沙:湖南大学, 2009.
[7] Souto Eduardo, Gomes Reinaldo, Sadok Djamel, Kelner Judith. Sampling Energy Consumption in Wireless Sensor Networks[C]. In: Proc. of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing ,vol.1, Taichung, Taiwan, 2006: 284-287.
[8]李方敏,徐文君,刘新华.无线传感器网络功率控制技术[J].软件学报, 2008,19(3):716-732.
[9]俞黎阳,王能,张卫.无线传感器网络中基于神经网络的数据融合模型[J].计算机科学, 2008,35(12):43-47.
[10]杨万海.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社, 2004.
[11]曲晓慧,安钢.数据融合方法综述及展望[J].舰船电子工程, 2003(2):2-5.
[12]康耀红.数据融合理论与应用[M].西安:西安电子科技大学出版社, 2006.
[13] Al-Karakia Jamal N., Ul-Mustafa Raza, Kamal Ahmed E. Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms[J]. Computer Networks, 2009,53(7):945-960.
[14] Meier A., Rein T., Beutel J., Thiele L. Coping with unreliable channels: Efficient link estimation for low-power wireless sensor networks[C]. In: Proc. of 5th International Conference on Networked Sensing Systems(INSS 2008)), 2008: 19-26.
[15]刘国彬.无线传感器网络基于事件驱动的数据融合的研究[D].天津:天津大学电气与自动化工程学院, 2006.
[16] Padmanabh, Kumar, Vuppala, Kumar Sunil. An adaptive data aggregation algorithm in wireless sensor network with bursty source[R].2009.
[17]毕艳忠,孙利民.传感器网络中的数据融合[J].计算机科学, 2004,31(7):101-103.
[18]周平,张胜,舒坚.无线传感器网络中一种高效的能量汇报融合机制[J].传感器与微系统,2009,28(8):70-72.
[19] Intanagonwiwa Chalermek, Govindan Ramesh, Estrin Deborah. Directed diffusion: A scalable and robust communication paradigm for sensor networks.[C]. In: Proc. of the Sixth Annual ACM/IEEE International Conference on Mobile Computing and Networking (Mobicom’2000), Boston, Massachusetts, 2000: 56-67.
[20] Kai Shao, Hong-wei Zhang, Yan Liang, Zhi-zhong Zhang, Jiang-tao Luo. Data fusion in wireless sensor network: A survey[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science), 2006,1(18):53-59.
[21]戴晓华,王智,蒋鹏,夏锋,孙优贤.无线传感器网络智能信息处理研究[J].传感技术学报, 2006,1(19):1-7.
[22] B Krishnamachari, D Estrin, S Wicker. Modeling data-centric routing in wireless sensor networks[C]. In: Proc. of IEEE INFOCOM, 2002.
[23] MIKAMI Shinji, AONISHI Takafumi, YOSHINO Hironori, OHTA Chikara, KAWAGUCHI Hiroshi, YOSHIMOTO Masahiko. Aggregation Efficiency-Aware Greedy Incremental Tree Routing for Wireless Sensor Networks[J]. IEICE Transactions on Communications, 2006, E89-B(10):2741-2751.
[24] M Ding, X Cheng, G Xue. Aggregation Tree Construction in Sensor Networks[C]. In: Proc. of the IEEE 58th Vehicular Technology Conference,vol.4, 2003: 2168-2172.
[25] W Heinzelman, A Chandrakasan, H Balakrishnan. Energy-efficient Communication Protocol for Wireless Microsensor NetWorks[C]. In: Proc. of 33rd Annual Hawaii Int’l Conf. on System Sciences, 2000: 3005-3014.
[26] S Lindsey, C Raghavendra. PEGASIS:Power-efficient Gathering in Sensor Information System[C]. In: Proc. of 2002 IEEE Aerospace Conference ,vol.3, 2002: 1125-1130.
[27] S Lindsey, C Raghavendra, K Sivalingam. Data gathering in sensor networks using the energy * delay metric[C]. In: Proc. of 15th International Parallel and Distributed Processing Symposium (IPDPS'01) Workshops,vol.3, San Francisco, California, USA, 2001: 23-27.
[28] Zhu Yujie, Vedantham Ramanuja, Park Seung-Jong, Sivakumar Raghupathy. A Scalable Correlation Aware Aggregation Strategy for Wireless Sensor Networks[C]. In: Proc. of the first International conference on wireless internet, Budapest, Hungary, 2005: 122-129.
[29]俞黎阳.无线传感器网络网格状分簇路由协议和数据融合算法的研究[D].上海:华东师范大学, 2008.
[30] Yu Liyang, Wang Neng, Zhang Wei, Zheng Chunlei. GROUP: a Grid-clustering Routing Protocol for Wireless Sensor Networks[C]. In: Proc. of 2006 IEEE International Conference on Wireless Communications, Networkding and Mobile Computing (WiCOM 2006),vol.3, Wuhan, China, 2006: 1-5.
[31] Zhao Yonggang Jerry, Govindan Ramesh, Estrin Deborah. Residual energy scan for monitoring sensor networks[C]. In: Proc. of Proceedings of the IEEE wireless communications andNetworking conference, WCNC2002,vol.1, Orlando, Florida, USA , 2002: 356-362.
[32] Han Song, Chan Edward. Continuous Residual Energy Monitoring in Wireless Sensor Networks [R].2005.
[33]方磊,黄韬,舒坚,刘琳岚,陈宇斌.事件驱动型无线传感器网络能量监视机制研究[J].传感器与微系统, 2008,27(10):14-17.
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