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Information gathering in adversarial systems: lines and cycles

Published:07 June 2003Publication History

ABSTRACT

In this paper we consider the problem of routing packets to a single destination in a dynamically changing network, where both the network and the packet injections are under adversarial control. Routing packets to a single destination is also known as information gathering. Information gathering is an important communication primitive for sensor networks. Since sensor networks have a wide range of civilian and military applications, they have recently attracted a great deal of research attention. Several communication protocols have already been suggested for sensor networks, but not much theoretical work has been done so far in this area. Information gathering is an important primitive to allow an observer to collect information from the sensors. Because sensors usually do not move, they form a static topology of possible communication links, but since sensors may frequently be in sleep mode or their communication may be disrupted by interference or obstacles, communication links may be up and down in an unpredictable way. In this paper, we consider sensor networks forming lines or cycles of unreliable edges. Already these seemingly simple topologies are difficult to handle by online algorithms, and the best previously known algorithms require by a factor of θ(n) more buffer size to achieve the same throughput as optimal routing algorithms, where n is the size of the network. We improve this factor to O(log n) and prove a matching lower bound that holds for all online algorithms.

References

  1. Factoid project. Available at http://www.research.digital.com/wrl/projects/Factoid.Google ScholarGoogle Scholar
  2. PicoRadio project. Available at http://bwrc.eecs.berkeley.edu/Research/Pico Radio/Default.htm.Google ScholarGoogle Scholar
  3. Ultra Low Power Wireless Sensor project. Available at http://www-mtl.mit.edu/~jimg/project top.html.Google ScholarGoogle Scholar
  4. WINS project. Available at http://www.janet.ucla.edu/WINS.Google ScholarGoogle Scholar
  5. W. Aiello, E. Kushilevitz, R. Ostrovsky, and A. Rosén. Adaptive packet routing for bursty adversarial traffic. In Proc. of the 30th ACM Symp. on Theory of Computing (STOC), pages 359--368, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Andrews, B. Awerbuch, A. Fernández, J. Kleinberg, T. Leighton, and Z. Liu. Universal stability results for greedy contention-resolution protocols. In Proc. of the 37th IEEE Symp. on Foundations of Computer Science (FOCS), pages 380--389, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. E. Anshelevich, D. Kempe, and J. Kleinberg. Stability of load balancing algorithms in dynamic adversarial systems. In Proc. of the 34th ACM Symp. on Theory of Computing (STOC), 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. B. Awerbuch, P. Berenbrink, A. Brinkmann, and C. Scheideler. Simple online strategies for adversarial systems. In Proc. of the 42nd IEEE Symp. on Foundations of Computer Science (FOCS), 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. B. Awerbuch and F. Leighton. A simple local-control approximation algorithm for multicommodity flow. In Proc. of the 34th IEEE Symp. on Foundations of Computer Science (FOCS), pages 459--468, 1993.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. B. Awerbuch and F. Leighton. Improved approximation algorithms for the multi-commodity flow problem and local competitive routing in dynamic networks. In Proc. of the 26th ACM Symp. on Theory of Computing (STOC), pages 487--496, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. B. Awerbuch, Y. Mansour, and N. Shavit. End-to-end communication with polynomial overhead. In Proc. of the 30th IEEE Symp. on Foundations of Computer Science (FOCS), pages 358--363, 1989.Google ScholarGoogle Scholar
  12. A. Borodin, J. Kleinberg, P. Raghavan, M. Sudan, and D. P. Williamson. Adversarial queueing theory. In Proc. of the 28th ACM Symp. on Theory of Computing (STOC), pages 376--385, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. D. Gamarnik. Stability of adversarial queues via fluid models. In Proc. of the 29th IEEE Symp. on Foundations of Computer Science (FOCS), pages 60--70, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. D. Gamarnik. Stability of adaptive and non-adaptive packet routing policies in adversarial queueing networks. In Proc. of the 31st ACM Symp. on Theory of Computing (STOC), pages 206--214, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Goel. Stability of networks and protocols in the adversarial queueing model for packet routing. In Proc. of the 10th ACM/SIAM Symp. on Discrete Algorithms (SODA), pages 911--912, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. W. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-efficient communication protocols for wireless microsensor networks. In Proc. of Hawaiian International Conference on Systems Science, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proc. of the 6th ACM/IEEE Mobicom Conference, pages 56--67, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. K. Kalpakis, K. Dasgupta, and P. Namjoshi. Maximum lifetime data gathering and aggregation in wireless sensor networks. Technical report TR CS-02-12, Computer Science and Electrical Engineering Department, University of Maryland Baltimore County, August 2002.Google ScholarGoogle Scholar
  19. R. Katz, J. Kahn, and K. Pister. Emerging challenges: Mobile networking for "Smart Dust". Journal of Communications and Networks, September 2000.Google ScholarGoogle Scholar
  20. J. Kulik, W. Rabiner, and H. Balakrishnan. Adaptive protocols for information dissemination in wireless sensor networks. In Proc. of the 5th ACM/IEEE Mobicom Conference, pages 174--185, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. S. Lindsey and C. Raghavendra. PEGASIS: Power Efficient GAthering in Sensor Information Systems. In Proc. of IEEE Aerospace Conference, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  22. G. Pottie and W. Kaiser. Wireless integrated network sensors. Communications of the ACM, 43(5):51--58, May 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. C. Scheideler and B. Vöcking. From static to dynamic routing: efficient transformations of store-and-forward protocols. In Proc. of the 31st ACM Symp. on Theory of Computing (STOC), pages 215--224, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. S. Singh, M. Woo, and C. Raghavendra. Power-aware routing in mobile ad-hoc networks. In MOBICOM '98, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. K. Sohrabi, J. Gao, V. Ailawadhi, and G. Pottie. Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, pages 16--27, October 2000.Google ScholarGoogle ScholarCross RefCross Ref
  26. P. Tsaparas. Stability in adversarial queueing theory. Master's thesis, Dept. of Computer Science, University of Toronto, 1997.Google ScholarGoogle Scholar

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          cover image ACM Conferences
          SPAA '03: Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
          June 2003
          374 pages
          ISBN:1581136617
          DOI:10.1145/777412

          Copyright © 2003 ACM

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          Publication History

          • Published: 7 June 2003

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          SPAA '03 Paper Acceptance Rate38of106submissions,36%Overall Acceptance Rate447of1,461submissions,31%

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