基于定向扩散和移动代理的无线传感器网时空信息集成框架研究

杨少军; 史浩山; 黄睿

基于定向扩散和移动代理的无线传感器网时空信息集成框架研究

计量
- 文章访问数: 2436
- HTML全文浏览量: 108
- PDF下载量: 870
- 被引次数: 3
出版历程
- 收稿日期: 2004-06-10
- 修回日期: 2005-03-22
- 刊出日期: 2005-12-19

Study of Spatio-Temporal Information Integration Framework Based on Directed Diffusion and Mobile-Agent for Wireless Sensor Networks

摘要

摘要: 无线传感器网络的有效节点分布往往是时空变化的，要求提供及时、可靠和高效的信息处理技术。提出一种基于Directed Diffusion和移动代理的时空信息集成框架。通过观测节点的兴趣泛洪和传感节点的目标检测信令泛洪，建立观测节点和有效节点间的双向梯度；目标检测信令在观测节点上触发的时间延迟窗生成节拍，在节拍中完成移动代理初始化和访问路径规划，其中节拍是空域信息集成的时间点，节拍序列则构成时域信息集成的时间轴；移动代理携带集成算法执行访问路径，完成时域和空域的信息集成。分析讨论了框架性能的关键参数，并通过飞机模型的二值图像识别仿真实验，证明了框架的有效性。
- 无线传感器网络;时空信息集成;定向扩散;移动代理
Abstract: In wireless sensor networks, the nodes with valued information are usually space-time varying. This paper presents a framework for spatio-temporal information integration based on directed diffusion and mobile agent. In the framework, the bi-direction gradients are constructed by the flood of the observers interest and the sensors Object Detected Signal (ODS). ODSs are collected and analyzed to program the visiting itineraries for mobile agents during a rhythm which is a period of time triggered by an ODS message, the space-time integration is completed by mobile agents with fusion algorithms accessing the nodes on the path. A rhythm acts as a label for spatial processing, and thus the series of rhythms become a time axis for temporal integration. In addition, the relations among some key parameters of framework are discussed. Finally, results based on experiments of collaborative target recognition demonstrate the effectiveness of the proposed framework.

HTML全文

参考文献(1)

Akyildiz I F, Su W, Sankarasubramaniam Y, Cayirci E. Wireless sensor networks: A survey[J].Computer Networks.2002, 38(4):393-422[2]Estrin D, Govindan R, Heidemann J, Kumar S. Next century challenges: scalable coordination in sensor networks. Proceedings of the 5th ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, 1999: 263-270.[3]Chong C Y, Kumar S. Sensor networks: evolution, opportunities, and challenges[J].Proc. IEEE.2003, 91(8):1247-1256[4]Kumar S, Zhao F, Shepherd D. Collaborative signal and information processing in microsensor networks. IEEE Signal Processing Magazine, 2002, 19(2): 13-14.[5]Zhao F, Zhin J, Reich J. Information-driven dynamic sensor[6]collaboration for tracking applications. IEEE Signal Processing[7]Magazine, 2002, 19(2): 61-72.[8]Guibas L J. Sensing, tracking, and reasoning with relations[J].IEEE Signal Processing Magazine.2002, 19(2):73-85[9]Qi H, Xu Y. Mobile-agent-based collaborative signal and information processing in sensor networks[J].Proc. IEEE.2003, 91(8):1172-1183[10]Wu Q, Rao N S V, Barhen J, et al.. On computing mobile agent routes for data fusion in distributed sensor networks[J].IEEE Trans. on Knowledge and Data Engineering.2004, 16(6):740-753[11]Intanagonwiwat C, Govindan R, Estrin D. Directed diffusion: A scalable and robust communication paradigm for sensor networks. Proceedings of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Boston, 2000: 56-67.[12]Silva F, Heidemann J, Govindan R, Estrin D. Directed diffusion. Technical report ISI-TR-2004-586, USC/Information Sciences Institute. January 2004.[13]Dempster A P. Upper and lower probabilities induced by a multi-value mapping[J].Annals of Mathematical Statistics.1967, 38(2):325-339[14]何友, 王国宏等. 多传感器信息融合及应用. 北京: 电子工业出版社, 2001: 287-303.

施引文献