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14. D. Bli` and G. Proietti. On the complexity of minimizing interference in adhoc and sensor o networks. Technical Report, Dipartimento di Informatica TRCS 009/1006, 2006. 15. T. Moscibroda and R. Wattenhofer. Coloring unstructured radio networks. In Proceedings of the 17th Annual ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 2005, pp. 39 48. 16. F. Meyer auf de Heide, C. Schindelhauer, K. Volbert, and M. Gr newald. Energy, congestion u and dilation in radio networks. In Proceedings of the 14th Annual ACM Symposium on Parallel Algorithms and Architectures (SPAA), 2002, pp. 230 237. 17. A. El Fawal, J.-Y. Le Boudec, Ruben Merz, B. Radunovic, J. Widmer, and G. M. Maggio. Trade-off analysis of PHY-aware MAC in low-rate low-power UWB networks. IEEE Communications Magazine, 43(12):147, 2005. 18. R. Raz and S. Safra. A sub-constant error-probability low-degree test, and sub-constant error-probability PCP characterization of NP. In Proceedings of the 29th Annual ACM Symposium on Theory of Computing (STOC), 1997, pp. 475 484. 19. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, MIT Press, Cambridge, MA, 2001. 20. D. Peleg, Distributed Computing: A Locality-sensitive Approach, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2000. 21. Y. Wang, X.-Yang Li, P.-J. Wan, and O. Frieder. Sparse power ef cient topology for wireless networks. Journal of Parallel and Distributed Computing, 2002. 22. F. Kuhn, T. Moscibroda, and R. Wattenhofer. The price of being near-sighted. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA), 2006. 23. M. Luby. A simple parallel algorithm for the maximal independent set problem. SIAM Journal of Computing, 15(4):1036 1053, 1986. 24. E. W. Dijkstra. Self-stabilizing systems in spite of distributed control. Communications of the ACM, 17(11):643 644, 1974. 25. B. Awerbuch and G. Varghese. Distributed program checking: A paradigm for building self-stabilizing distributed protocols. In Proceedings of the 32nd Annual IEEE Symposium on Foundations of Computer Science (FOCS), 1991. CD-ROM. 26. F. Kuhn, T. Moscibroda, and R. Wattenhofer. What cannot be computed locally!. In Proceedings of the 23rd ACM Symposium on the Principles of Distributed Computing (PODC), 2004. CD-ROM. 27. P.-J. Wan, K. M. Alzoubi, and O. Frieder. Distributed construction of connected dominating set in wireless ad hoc networks. Mobile Networks and Applications, 9(2):141 149, 2004. 28. T. Moscibroda, P. von Rickenbach, and R. Wattenhofer. Analyzing the energy-latency trade-off during the deployment of sensor networks. In Proceedings of the IEEE Infocom, 2006. CD-ROM. 29. O. Dousse, P. Thiran, and M. Hasler, Connectivity in ad-hoc and hybrid networks. In Proceedings of the IEEE Infocom, 2002. CD-ROM. 30. F. Kuhn, R. Wattenhofer, and A. Zollinger. Worst-case optimal and average-case ef cient geometric ad-hoc routing. In Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing (MobiHoc), 2003, pp. 267 278. 31. A. E. F. Clementi, G. Huiban, and P. Penna. On the approximation ratio of the MST-based heuristic for the energy-ef cient broadcast problem in static ad-hoc radio networks. In
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Proceedings of the 17th International Symposium on Parallel and Distributed Processing (IPDPS), Washington, DC, 2003. CD-ROM. 32. V. Raghunathan, C. Schurgers, S. Park, and M. Srivastava. Energy aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2):40 50, 2002. 33. J.-H. Chang and L. Tassiulas. Energy conserving routing in wireless ad-hoc networks. In Proceedings of the IEEE Infocom, 2000, pp. 22 31.
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Spatiotemporal Correlation Theory for Wireless Sensor Networks
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OZGUR B. AKAN
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5.1 INTRODUCTION Wireless sensor networks (WSNs) are generally composed of densely deployed sensor nodes that collaboratively observe and communicate their readings of a certain physical phenomenon [1]. In general, the main objective of the WSN is to reliably detect/estimate event features from the collective information provided by sensor nodes. Therefore, the energy and hence processing constraints of small wireless sensor nodes are overcome by this collective sensing notion that is realized via their networked deployment. To this end, accurate and ef cient operation of any WSN deployment requires that we maintain suf cient network and sensing coverage in the deployment eld. To assure network and sensing coverage, WSN applications require sensor nodes to be densely deployed in the eld. Dense deployment of sensor nodes makes the sensor observations highly correlated in space domain. Similarly, in periodic sensing applications, all consecutive sensor readings are temporally correlated. While the collaborative nature of the WSN brings signi cant advantages over traditional sensing including greater accuracy, larger coverage area, and extraction of localized features, the spatiotemporal correlation among the sensor observations is another signi cant and unique characteristic of the WSN which can be exploited to drastically enhance the overall network performance. In general, and based on the application, the physical phenomenon to be observed can be modeled as point source (e.g., target detection/tracking) or eld source (e.g., monitoring of magnetic eld and seismic activities) [2]. Events generating a signal that originates from a single point in the eld can be modeled as a point source. The cases where the physical phenomenon
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Algorithms and Protocols for Wireless Sensor Networks, Edited by Azzedine Boukerche Copyright 2009 by John Wiley & Sons Inc.
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