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the reader to the selection of suitable scenarios and performance metric and (b) illustrate a general approach to reading experimental results. In particular, we illustrate controlled and uncontrolled mobility both in the case of networks with single-hop routing of the packets to a passing mobile relay (data MULEs architecture) and in the case of controlled and uncontrolled mobility of a sink roaming through the network and having the data delivered to it in a multihop fashion. The two different network architectures, with single-hop versus multihop data routing, are described in details and compared. Our comparison concerns key metrics of interest for WSNs, which include network lifetime and sensor-to-sink data latency. Besides providing evidence of the advantages of using mobility, the chapter also provides insights on different techniques for protocol design and performance evaluation and comparison.
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10.9 EXERCISES 1. Review questions. (a) Why is it less convenient to use resource-constrained mobile sensor nodes rather than resource-rich mobile elements like relays and sinks (b) Give an example that clearly motivates the use of mobility in WSNs. (c) What metric is most affected by the use of mobile relays rather than mobile sinks (d) For what kind of application it is clearly more advisable to use the data MULE approach instead of a multihop solution with mobile sink (e) List ve metrics of interest that should be evaluated when dealing with mobile WSNs. (f) Describe pros and cons of developing a mathematical formulation of a problem for WSNs like the one described in Section 10.5.2. (g) What are the major differences, in terms of mobility, when comparing GMRE, RM, and PM (h) If the MULEs move according to the random waypoint mobility model, where are they more likely to be found What does this imply in terms of network performance (That is, which metric is the most affected in this case ) 2. Consider the MILP model presented in Section 10.5.2. What are the problems that would occur if constraints (10.8) are removed Why is this removal a problem 3. Consider the MILP model presented in Section 10.5.2. Imagine that instead of knowing the sink sites, several different routes for the sink are available. Rewrite the model in such a way that the output is the sequence of routes that maximize network lifetime. 4. Consider again the MILP model presented in Section 10.5.2. Rewrite the model so that the sink can pass for the same site at most ve times. 5. Consider the pattern of energy consumption depicted in Figure 10.6 (which refers to the basic scenario described in Section 10.6.1 with 8 8 sink sites). The darker the area, the higher the energy consumption. Given this pattern, describe where an heuristic will more likely send the sink (i.e., to which sites). Justify your answer. 6. What is the main difference between the data MULE approach and the PM scheme described in Section 10.5.3
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Figure 10.6. Average nodal energy consumption when the sink sojourns at different sites. (a) Corner site. (b) Perimeter site. (c) Central site.
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ACKNOWLEDGMENTS This work was supported in part by the European FP6 027227 IP Project E-Sense (Capturing Ambient Intelligence for Mobile Communications through Wireless Sensor Networks) and by NSF grant CNS-0738720.
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BIBLIOGRAPHY
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1. S. Basagni, M. Conti, S. Giordano, and I. Stojmenovic, editors. Mobile Ad Hoc Networking. IEEE Press and John Wiley & Sons, Piscataway, NJ, and Hoboken, NJ, 2004. 2. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: A survey. Computer Networks, vol. 38(4):393 422, 2002. 3. E. H. Callaway, Jr. Wireless Sensor Networks: Architectures and Protocols. Auerbach Publications, Boca Raton, FL, 2003. 4. I. F. Akyildiz, D. Pompili, and T. Melodia. Underwater acoustic sensor networks: Research challenges. Elsevier s Journal of Ad Hoc Networks, 3(3):257 279, 2005. 5. G. Kantor, S. Singh, R. Peterson, D. Rus, A. Das, V. Kumar, G. Pereira, and J. Spletzer. Distributed search and rescue with robot and sensor team. In Proceedings of the 4th International Conference on Field and Service Robotics, FSR 2003, Lake Yamanaka, Japan, July 2003, pp. 327 332. 6. K. Kotay, R. Peterson, and D. Rus. Experiments with robots and sensor networks for mapping and navigation. In Proceedings of the International Conference on Field and Service Robotics, FSR 2005, Port Douglas, Australia, July 29 31, 2005. 7. R. Brooks, P. Ramanathan, and A. Sayeed. Distributed target classi cation and tracking in sensor networks. Proceedings of the IEEE, 91(8):1163 1171, 2003. 8. G. Virone, A. Wood, L. Selavo, Q. Cao, L. Fang, T. Doan, Z. He, R. Stoleru, S. Lin, and J. A. Stankovic. An assisted living oriented information system based on a residential wireless sensor network. In Proceedings of the 1st Distributed Diagnosis and Home Healthcare (D2H2) Conference, Arlington, VA, April 2 4, 2006, pp. 95 100.
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