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149. S. Madden, R. Szewczyk, M. J. Franklin, and D. Culler. Supporting aggregate queries over ad-hoc wireless sensor networks. In 4th IEEE Workshop on Mobile Computing Systems and Applications, 2002, pp. 49 58. 150. D. Ganesan, B. Greenstein, D. Perelyubskiy, D. Estrin, and J. Heidemann. An evaluation of multi-resolution storage in resource-constrained sensor networks. Proceedings ACM Sensys 03, Los Angeles, November 5 7, 2003. 151. S. Madden, J. Hellerstein, and W. Hong. TinyDB: In-Network Query Processing in TinyOS. UC Berkeley EECS Department, 2003. 152. Y. Xue, B. Li, and K. Nahrstedt. Optimal resource allocation in wireless ad hoc networks: A price-based approach. IEEE Transactions on Mobile Computing, 5(4):347 364, 2006. 153. P. Marbach and R. Berry. Downlink resource allocation and pricing for wireless networks. In INFOCOM, 2002, 1470 1479. 154. S. Cui, R. Madan, A. Goldsmith, and S. Lall. Cross-layer energy minimization in Sensor Networks, Proceedings: Allerton Conference on Communications, Control, and Computing, Monticello, IL, September 2004, pp. 1891 1900. 155. S. Shakkottai, T. S. Rappaport, and P. C. Karlsson. Cross-layer design for wireless networks. IEEE Communications Magazine, 41:74 80, 2003. 156. V. Kawadia and P. R. Kumar. A cautionary perspective on cross-layer design. IEEE Transactions on Wireless Communications, 12(1):3 11, 2005. 157. B. Hofmann-Wellenhof, H. Lictenegger, and J. Collins GPS: Theory and Practice, Springer-Verlag, Berlin, 1997. 158. W. Kaiser, G. Pottie, M. Srivastava, G. Sukhatme, J. Villasenor, and D. Estrin. Networked infomechanical systems (NIMS) for ambient intelligience. In W. Weber, J. M. Rabaey, and E. Aarts, editors, In Ambient Intelligence, Springer, New York, 2005. 159. H. Luo and G. J. Pottie. Routing explicit side information ofr data compression in wireless sensor networks. In DCOSS 2005, Marina del Rey, CA, June 30 July 1, 2005. 160. H. Luo and G. J. Pottie. Balanced aggregation trees for routing correlated data in wireless sensor networks. In IEEE ISIT 2005, Adelaide Australia, September 4 9 2005, 14 18.
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Energy-Ef cient Algorithms in Wireless Sensor Networks
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15.1 INTRODUCTION Recent dramatic developments in microelectromechanical (MEMS) systems, wireless communications, and digital electronics have already led to the development of smallsized, low-power, low-cost sensor devices. Such extremely small devices integrate sensing, data processing, and wireless communication capabilities. Current devices have a size at the cubic centimeter scale, a CPU running at 4 MHz, some memory, and a wireless communication capability at a 4-kbps rate. Also, they are equipped with a small but effective operating system and are able to switch between sleeping and awake modes to save energy. Pioneering groups (like the Smart Dust Project at Berkeley, the Wireless Integrated Network Sensors Project at UCLA, and the Ultra low Wireless Sensor Project at MIT) pursue further important goals, like a total volume of a few cubic millimeters and extremely low energy consumption, by using alternative technologies, based on radio frequency (RF) or optical (laser) transmission. Examining each such device individually might appear to have small utility; however, the effective distributed coordination of large numbers of such devices may lead to the ef cient accomplishment of large sensing tasks. Large numbers of sensor nodes can be deployed in areas of interest (such as inaccessible terrains or disaster places) and use self-organization and collaborative methods to form a sensor network.
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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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Their wide range of applications is based on the possible use of various sensor types (i.e., thermal, visual, seismic, acoustic, radar, magnetic, etc.) in order to monitor a wide variety of conditions (e.g., temperature, object presence and movement, humidity, pressure, noise levels, etc.). Thus, sensor networks can be used for continuous sensing, event detection, location sensing, and as microsensing. Hence, sensor networks have important applications, including (a) military (like forces and equipment monitoring; battle eld surveillance; targeting; nuclear, biological, and chemical attack detection), (b) environmental applications (such as re detection, ood detection, precision agriculture), (c) health applications (like telemonitoring of human physiological data), and (d) home applications (e.g., smart environments and home automation). For an excellent survey of wireless sensor networks see, references 1 3. 15.1.1 Critical Challenges The ef cient and robust realization of such large, highly dynamic, complex, nonconventional networking environments is a challenging algorithmic and technological task. Features including the huge number of sensor devices involved, the severe power, computational, and memory limitations, their dense deployment, and frequent failures pose new design, analysis, and implementation aspects that are essentially different with respect to not only distributed computing and systems approaches but also ad-hoc networking techniques. We emphasize the following characteristic differences between sensor networks and ad hoc networks: r The number of sensor particles in a sensor network is extremely large compared to that in a typical ad hoc network. r Sensor networks are typically prone to faults. r Because of faults as well as energy limitations, sensor nodes may (permanently or temporarily) join or leave the network. This leads to highly dynamic network topology changes. r The density of deployed devices in sensor networks is much higher than in ad hoc networks. r The limitations in energy, computational power, and memory are much more severe in sensor networks. Because of the above rather unique characteristics of sensor networks, ef cient and robust distributed protocols and algorithms should exhibit the following critical properties: Scalability. Distributed protocols for sensor networks should be highly scalable, in the sense that they should operate ef ciently in extremely large networks composed of huge numbers of nodes. This feature calls for an urgent need to prove by analytical means and also validate (by large-scale simulations) certain ef ciency and robustness (and their tradeoffs) guarantees for asymptotic network sizes.
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