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Suppressing Redundancy in Wireless Sensor Network Traffic

  • Rey Abe
  • Shinichi Honiden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6131)

Abstract

Redundancy suppression is a network traffic compression technique that, by caching recurring transmission contents at receiving nodes, avoids repeatedly sending duplicate data. Existing implementations require abundant memory both to analyze recent traffic for redundancy and to maintain the cache. Wireless sensor nodes at the same time cannot provide such resources due to hardware constraints. The diversity of protocols and traffic patterns in sensor networks furthermore makes the frequencies and proportions of redundancy in traffic unpredictable. The common practice of narrowing down search parameters based on characteristics of representative packet traces when dissecting data for redundancy thus becomes inappropriate. Such difficulties made us devise a novel protocol that conducts a probabilistic traffic analysis to identify and cache only the subset of redundant transfers that yields most traffic savings. We verified this approach to perform close enough to a solution built on exhaustive analysis and unconstrained caching to be practicable.

Keywords

Sensor Network Sensor Node Wireless Sensor Network Computational Overhead Wireless Sensor Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Prabh, K.S., Abdelzaher, T.F.: Energy-conserving data cache placement in sensor networks. ACM Transactions on Sensor Networks (TOSN) 1(2), 178–203 (2005)CrossRefGoogle Scholar
  2. 2.
    Kimura, N., Latifi, S.: A survey on data compression in wireless sensor networks. Information Technology: Coding and Computing 2, 8–13 (2005)Google Scholar
  3. 3.
    Santos, J., Wetherall, D.: Increasing effective link bandwidth by suppressing replicated data. In: Proc. of USENIX ATEC, Berkeley, USA, pp. 18–18 (1998)Google Scholar
  4. 4.
    Anand, A., Gupta, A., Akella, A., Seshan, S., Shenker, S.: Packet caches on routers: the implications of universal redundant traffic elimination. SIGCOMM Comp. Comm. Rev. 38(4), 219–230 (2008)CrossRefGoogle Scholar
  5. 5.
    Anand, A., Muthukrishnan, C., Akella, A., Ramjee, R.: Redundancy in network traffic: findings and implications. In: Proc. of the ACM SIGMETRICS (2009)Google Scholar
  6. 6.
    Bjorner, N., Blass, A., Gurevich, Y.: Content-dependent chunking for differential compression, the local maximum approach. Journal of Comp. and Sys. Sc. (2009)Google Scholar
  7. 7.
    Pucha, H., Andersen, D.G., Kaminsky, M.: Exploiting similarity for multi-source downloads using file handprints. In: Proc. of the 4th USENIX NSDI (2007)Google Scholar
  8. 8.
    Spring, N.T., Wetherall, D.: A protocol-independent technique for eliminating redundant network traffic. SIGCOMM Comp. Comm. Rev. 30(4), 87–95 (2000)CrossRefGoogle Scholar
  9. 9.
    Rabin, M.: Fingerprinting by random polynomials. Technical report tr-15-81, Harvard University, Department of Computer Science (1981)Google Scholar
  10. 10.
    Karl, H., Willig, A.: Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons, Chichester (2005)CrossRefGoogle Scholar
  11. 11.
    Westphal, C.: Layered IP header compression for IP-enabled sensor networks. In: Proc. of the IEEE ICC, vol. 8, pp. 3542–3547 (2006)Google Scholar
  12. 12.
    Schleimer, S., Wilkerson, D.S., Aiken, A.: Winnowing: local algorithms for document fingerprinting. In: Proc. of the ACM SIGMOD, pp. 76–85 (2003)Google Scholar
  13. 13.
    Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 693–703. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Cormode, G., Hadjieleftheriou, M.: Finding frequent items in data streams. Proc. of the VLDB Endowment 1(2), 1530–1541 (2008)Google Scholar
  15. 15.
    Manerikar, N., Palpanas, T.: Frequent items in streaming data: An experimental evaluation of the state-of-the-art. Data & Kn. En. 68(4) (2009)Google Scholar
  16. 16.
    Jin, C., Qian, W., Sha, C., Yu, J.X., Zhou, A.: Dynamically maintaining frequent items over a data stream. In: Proc. of the 12th ACM CIKM, pp. 287–294 (2003)Google Scholar
  17. 17.
    Aguilar-Saborit, J., Trancoso, P., Muntes-Mulero, V., Larriba-Pey, J.L.: Dynamic adaptive data structures for monitoring data streams. Data & Kn. En. 66 (2008)Google Scholar
  18. 18.
    Santini, S., Roemer, K.: An adaptive strategy for quality-based data reduction in wireless sensor networks. In: Proc. of the 3rd INSS, pp. 29–36 (2006)Google Scholar
  19. 19.
    Gupta, A., Akella, A., Seshan, S., Shenker, S., Wang, J.: Understanding and exploiting network traffic redundancy. Technical report (2007)Google Scholar
  20. 20.
    Kirsch, A., Mitzenmacher, M., Varghese, G.: Hash-based techniques for high-speed packet processing. Technical report (2008)Google Scholar
  21. 21.
    Barrenetxea, G., Ingelrest, F., Schaefer, G., Vetterli, M., Couach, O., Parlange, M.: Sensorscope: Out-of-the-box environmental monitoring. In: Proc. of the 7th IEEE IPSN, Washington, DC, USA, pp. 332–343 (2008)Google Scholar
  22. 22.
    Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L.S., Rubenstein, D.: Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with zebranet. In: Proc. of the 10th ASPLOS-X, New York, USA, pp. 96–107 (2002)Google Scholar
  23. 23.
    Arnold, R., Bell, T.: A corpus for the evaluation of lossless compression algorithms. In: Proc. of the 7th DCC, pp. 201–210 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rey Abe
    • 1
  • Shinichi Honiden
    • 1
    • 2
  1. 1.University of TokyoTokyoJapan
  2. 2.National Institute of InformaticsTokyoJapan

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