Abstract
Phenomena clouds are characterized by nondeterministic, dynamic variations of shapes, sizes, direction, and speed of motion along multiple axes. The phenomena detection and tracking should not be limited to some traditional applications such as oil spills and gas clouds but also be utilized to more accurately observe other types of phenomena such as walking motion of people. This wider range of applications requires more reliable, in-situ techniques that can accurately adapt to the dynamics of phenomena. Unfortunately, existing works which only focus on simple and well-defined shapes of phenomena are no longer sufficient.
In this article, we present a new class of applications together with several distributed algorithms to detect and track phenomena clouds, regardless of their shapes and movement direction. We first propose a distributed algorithm for in-situ detection and tracking of phenomena clouds in a sensor space. We next provide a mathematical model to optimize the energy consumption, on which we further propose a localized algorithm to minimize the resource utilization. Our proposed approaches not only ensure low processing and networking overhead at the centralized query processor but also minimize the number of sensors which are actively involved in the detection and tracking processes. We validate our approach using both real-life smart home applications and simulation experiments, which confirm the effectiveness of our proposed algorithms. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches.
- A. Abbasi and M. Younis. 2007. A survey on clustering algorithms for wireless sensor networks. Comput. Comm. 30, 2826--2841. Google ScholarDigital Library
- M. Ali, M. Mokbel, W. Aref, and I. Kamel. 2005. Detection and tracking of discrete phenomena in sensor-network databases. In Proceedings of the 17th International Conference on Scientific and Statistical Database Management (SSDBM'05). 163--172. Google ScholarDigital Library
- S. Bhattacharya, N. Atay, G. Alankus, C. Lu, O. B. Bayazit, and G. Roman. 2006. Roadmap query for sensor network assisted navigation in dynamic environments. In Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS'06). Google ScholarDigital Library
- R. Bose and A. Helal. 2008. Observing walking behavior of humans using distributed phenomenon detection and tracking mechanisms. In Proceedings of the 2nd International Workshop on Practical Applications of Sensor Networks, held in conjunction with the International Symposium on Applications and the Internet (SAINT'08). Google ScholarDigital Library
- R. Bose and A. Helal. 2009. Localized in-network detection and tracking of phenomena clouds using wireless sensor networks. In Proceedings of the International Conference on Intelligent Environments (IE'09).Google Scholar
- R. Bose, J. King, H. El-Zabadani, S. Pickles, and A. Helal. 2006. Building plug-and-play smart homes using the atlas platform. In Proceedings of the 4th International Conference on Smart Homes and Health Telematics.Google Scholar
- N. Bulusu, J. Heidemann, and D. Estrin. 2000. Gps-less low cost outdoor location for very small devices. IEEE Personal. Comm. 7, 5, 28--34.Google ScholarCross Ref
- M. Cardei, M. T. Thai, Y. Li, and W. Wu. 2005. Energy-efficient target coverage in wireless sensor networks. In Proceedings of the 24th Conference of the IEEE Communications Society (INFOCOM'05).Google Scholar
- K. K. Chintalapudi and R. Govindan. 2003. Localized edge detection in sensor fields. In Proceedings of the IEEE Workshop on Sensor Networks Protocols and Applications.Google Scholar
- S. Duttagupta, K. Ramamritham, and P. Kulkarni. 2008. Tracking dynamic boundary fronts using range sensors. In Proceedings of the 5th European Conference on Wireless Sensor Networks. Google ScholarDigital Library
- S. Duttagupta, K. Ramamritham, and P. Ramanathan. 2006. Distributed boundary tracking using sensor networks. In Proceedings of the 3rd IEEE International Conference on Mobile Ad Hoc and Sensor Systems.Google Scholar
- A. Helal, W. Mann, H. El-Zabadani, J. King, Y. Kaddoura, and E. Jansen. 2005. Gator tech smart house: A programmable pervasive space. Comput. 38, 3, 50--60. Google ScholarDigital Library
- X. Ji, H. Zha, J. Metzner, and G. Kesidis. 2004. Dynamic cluster structure for object detection and tracking in wireless ad-hoc sensor networks. In Proceedings of the IEEE International Conference on Communications. 3807--3811.Google Scholar
- Z. Jin and A. L. Bertozzi. 2007. Environmental boundary tracking and estimation using multiple autonomous vehicles. In Proceedings of the 46th IEEE Conference on Decision and Control. 4918--4923.Google Scholar
- P. Juang, H. Oki, Y. Wang, M. Martonosi, L. Peh, and D. Rubenstein. 2002. Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with zebranet. SIGARCH Comput. Archit. News 30, 5, 96--107. Google ScholarDigital Library
- J. Kim, K. Kim, C. S. Hussain, M. Cui, and M. Park. 2008. Energy-efficient tracking of continuous objects in wireless sensor networks. In Proceedings of the 5th International Conference on Ubiquitous Intelligence and Computing (UIC'08). Google ScholarDigital Library
- J. King, R. Bose, S. Pickles, A. Helal, and H. Yang. 2006. Atlas - A service-oriented sensor platform. In Proceeding of the 1st IEEE International Workshop on Practical Issues in Building Sensor Network Applications.Google Scholar
- P. Liao, M. Chang, and C. C. Kuo. 2004. Distributed edge detection with composite hypothesis test in wireless sensor networks. In Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM'04). 129--133.Google Scholar
- S. Madden, M. Franklin, J. Hellerstein, and W. Hong. 2002. Tag: A tiny aggregation service for ad-hoc sensor networks. In Proceedings of the 5th Annual Symposium on Operating Systems Design and Implementation. Google ScholarDigital Library
- D. Marthaler and A. L. Bertozzi. 2003. Collective motion algorithms for determining environmental boundaries. In Proceedings of the SIAM Conference on Applications of Dynamical Systems.Google Scholar
- D. McErlean and S. Narayanan. 2002. Distributed detection and tracking in sensor networks. In Proceedings of the 36th Asilomar Conference on Signals, Systems and Computers.Google Scholar
- B. N. D. Niculescu. 2003. Ad hoc positioning system (aps) using aoa. In Proceedings of the 22nd Annual Joint Conference of IEEE Computer and Communication Societies.Google ScholarCross Ref
- A. Omotayo, M. Hammad, and K. Barker. 2006. Effcient data harvesting for tracing phenomena in sensor networks. In Proceedings of the 18th International Conference on Scientific and Statistical Database Management (SSDBM'06). Google ScholarDigital Library
- Palm. 2000. The 29 palms experiment: Tracking vehicles with a uav-delivered sensor network. http://tinyos.millennium.berkeley.edu/29palms.htm.Google Scholar
- K. Ren, K. Zeng, and W. Lou. 2008. Secure and fault-tolerant event boundary detection in wireless sensor networks. IEEE Trans. Wirel. Comm. 7, 1, 352--363. Google ScholarDigital Library
- A. Savvides, J. Fang, and D. Lymberopoulos. 2004. Using mobile sensing nodes for dynamic boundary estimation. In Proceedings of the Workshop on Applications of Mobile Embedded Systems (WAMES'04).Google Scholar
- M. T. Thai, F. Wang, D. H. Du, and X. Jia. 2008. Coverage problems in wireless sensor networks: designs and analysis. Int. J. Sens. Netw. 3, 3, 191--200. Google ScholarDigital Library
- C. Zhang, Y. Zhang, and Y. Fang. 2006. Localized coverage boundary detection for wireless sensor networks. In Proceedings of the 3rd International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (QShine'06). ACM Press, New York, 12. Google ScholarDigital Library
- C. Zhong and M. Worboys. 2007. Energy-efficient continuous boundary monitoring insensor networks. http://www.spatial.maine.edu/czhong /boundarymonitoring.pdf.Google Scholar
Index Terms
- On detection and tracking of variant phenomena clouds
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