Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Data Compression in Sensor Networks

  • Amol Deshpande
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_96

Synonyms

Correlated data collection; Data suppression; Distributed source coding

Definition

Data compression issues arise in a sensor network when designing protocols for efficiently collecting all data observed by the sensor nodes at an Internet-connected base station. More formally, let Xi denote an attribute being observed by a node in the sensor network – Xi may be an environmental property being sensed by the node (e.g., temperature), or it may be the result of an operation on the sensed values (e.g., in an anomaly detection application, the sensor node may continuously evaluate a filter such as “temperature > 100” on the observed values). The goal is to design an energy-efficient protocol to periodically collect the observed values of all such attributes (denoted X1,…,Xn) at the base station, at a frequency specified by the user. In many cases, a bounded-error approximation might be acceptable, i.e., the reported values may only be required to be within ± of the observed...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Adler M. Collecting correlated information from a sensor network. In: Proceedings of the 16th Annual ACM-SIAM Symposium on Discrete Algorithms; 2005.Google Scholar
  2. 2.
    Chu D, Deshpande A, Hellerstein J, Hong W. Approximate data collection in sensor networks using probabilistic models. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.Google Scholar
  3. 3.
    Pattem S, Krishnamachari B, Govindan R. The impact of spatial correlation on routing with compression in wireless sensor networks. In: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks; 2004.Google Scholar
  4. 4.
    Pradhan S, Ramchandran K. Distributed source coding using syndromes (DISCUS): design and construction. IEEE Trans Inform Theory. 2003;49(3)CrossRefMathSciNetzbMATHGoogle Scholar
  5. 5.
    Silberstein A, Puggioni G, Gelfand A, Munagala K, Yang J. Making sense of suppressions and failures in sensor data: a Bayesian approach. In: Proceedings of the 33rd International Conference on Very Large Data Bases; 2007.Google Scholar
  6. 6.
    Slepian D, Wolf J. Noiseless coding of correlated information sources. IEEE Trans Inf Theory. 1973;19(4).CrossRefMathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of MarylandCollege ParkUSA

Section editors and affiliations

  • Le Gruenwald
    • 1
  1. 1.School of Computer ScienceUniversity of OklahomaNormanUSA