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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
Kimura, N., Latifi, S.: A survey on data compression in wireless sensor networks. Information Technology: Coding and Computing 2, 8–13 (2005)
Santos, J., Wetherall, D.: Increasing effective link bandwidth by suppressing replicated data. In: Proc. of USENIX ATEC, Berkeley, USA, pp. 18–18 (1998)
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)
Anand, A., Muthukrishnan, C., Akella, A., Ramjee, R.: Redundancy in network traffic: findings and implications. In: Proc. of the ACM SIGMETRICS (2009)
Bjorner, N., Blass, A., Gurevich, Y.: Content-dependent chunking for differential compression, the local maximum approach. Journal of Comp. and Sys. Sc. (2009)
Pucha, H., Andersen, D.G., Kaminsky, M.: Exploiting similarity for multi-source downloads using file handprints. In: Proc. of the 4th USENIX NSDI (2007)
Spring, N.T., Wetherall, D.: A protocol-independent technique for eliminating redundant network traffic. SIGCOMM Comp. Comm. Rev. 30(4), 87–95 (2000)
Rabin, M.: Fingerprinting by random polynomials. Technical report tr-15-81, Harvard University, Department of Computer Science (1981)
Karl, H., Willig, A.: Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons, Chichester (2005)
Westphal, C.: Layered IP header compression for IP-enabled sensor networks. In: Proc. of the IEEE ICC, vol. 8, pp. 3542–3547 (2006)
Schleimer, S., Wilkerson, D.S., Aiken, A.: Winnowing: local algorithms for document fingerprinting. In: Proc. of the ACM SIGMOD, pp. 76–85 (2003)
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)
Cormode, G., Hadjieleftheriou, M.: Finding frequent items in data streams. Proc. of the VLDB Endowment 1(2), 1530–1541 (2008)
Manerikar, N., Palpanas, T.: Frequent items in streaming data: An experimental evaluation of the state-of-the-art. Data & Kn. En. 68(4) (2009)
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)
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)
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)
Gupta, A., Akella, A., Seshan, S., Shenker, S., Wang, J.: Understanding and exploiting network traffic redundancy. Technical report (2007)
Kirsch, A., Mitzenmacher, M., Varghese, G.: Hash-based techniques for high-speed packet processing. Technical report (2008)
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)
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)
Arnold, R., Bell, T.: A corpus for the evaluation of lossless compression algorithms. In: Proc. of the 7th DCC, pp. 201–210 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abe, R., Honiden, S. (2010). Suppressing Redundancy in Wireless Sensor Network Traffic. In: Rajaraman, R., Moscibroda, T., Dunkels, A., Scaglione, A. (eds) Distributed Computing in Sensor Systems. DCOSS 2010. Lecture Notes in Computer Science, vol 6131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13651-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-13651-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13650-4
Online ISBN: 978-3-642-13651-1
eBook Packages: Computer ScienceComputer Science (R0)