Skip to main content

Adaptive Probing and Communication in Sensor Networks

  • Conference paper
Ad-Hoc, Mobile, and Wireless Networks (ADHOC-NOW 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3158))

Included in the following conference series:

Abstract

Sensor networks consist of multiple low-cost, autonomous, ad-hoc sensors, that periodically probe and react to the environment and communicate with other sensors or devices. A primary concern in the operation of sensor networks is the limited energy capacity per sensor. As a result, a common challenge is in setting the probing frequency, so as to compromise between the cost of frequent probing and the inaccuracy resulting from infrequent probing.

We present adaptive probing algorithms that enable sensors to make effective selections of their next probing time, based on prior probes. We also present adaptive communication techniques, which allow reduced communication between sensors, and hence significant energy savings, without sacrificing accuracy. The presented algorithms were implemented in Motes sensors and are shown to be effective by testing them on real data.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Edward, G., Box, P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Prentice Hall PTR, Englewood Cliffs (1994)

    Google Scholar 

  2. Habitat monitoring on great duck island, http://www.greatduckisland.net/

  3. Goel, S., Imielinski, T.: Prediction-based monitoring in sensor networks: Taking lessons from mpeg. ACM Computer Communication Review 31(5) (2001)

    Google Scholar 

  4. Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice Hall, Upper Saddle River (1996)

    Google Scholar 

  5. Hellerstein, J.M., Hong, W., Madden, S., Stanek, K.: Beyond average: Toward sophisticated sensing with queries. In: 2nd International Workshop on Information Processing in Sensor Networks (IPSN 2003) (March 2003)

    Google Scholar 

  6. Kalpakis, K., Puttagunta, V., Namjoshi, P.: Accuracy vs. lifetime: Linear sketches for approximate aggregate range queries in sensor networks. available as umbc cs tr-04-04 (February 11, 2004)

    Google Scholar 

  7. Liu, J., Liu, J., Reich, J., Cheung, P., Zhao, F.: Distributed group management for track initiation and maintenance in target localization applications. In: 2nd International Workshop on Information Processing in Sensor Networks (2003)

    Google Scholar 

  8. Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: a tiny aggregation service for ad-hoc sensor networks. The Magazine Of Usenix And Sage 28(2), 8 (2003)

    Google Scholar 

  9. Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J.: Wireless sensor networks for habitat monitoring. In: ACM International Workshop on Wireless Sensor Networks and Applications (WSNA 2002), Atlanta, GA (September 2002)

    Google Scholar 

  10. Makridakis, S., Wheelwright, S., Hyndman, R.J.: Forecasting: Methods and Applications. John Wiley & Sons, Chichester (1998)

    Google Scholar 

  11. Marbini, D., Sacks, L.E.: Adaptive sampling mechanisms in sensor networks. In: London Communications Symposium (2003)

    Google Scholar 

  12. Berkley mica motes, http://www.xbow.com/Products/Wireless_Sensor_Networks.htm

  13. Papadimitriou, S., Brockwell, A., Faloutsos, C.: Adaptive, hands-off stream mining. In: 29th International Conference on Very Large Data Bases VLDB (2003)

    Google Scholar 

  14. Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.K.: Tina: a scheme for temporal coherency-aware in-network aggregation. In: 3rd ACM international workshop on Data engineering for wireless and mobile access, pp. 69–76 (2003)

    Google Scholar 

  15. Tinyos operating system, http://webs.cs.berkeley.edu/tos/

  16. Yi, B.-K., Sidiropoulos, N.D., Johnson, T., Jagadish, H.V., Faloutsos, C., Biliris, A.: Online data mining for co-evolving time sequences. In: 16th International Conference on Data Engineering, p. 13. IEEE Computer Society, Los Alamitos (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ragoler, I., Matias, Y., Aviram, N. (2004). Adaptive Probing and Communication in Sensor Networks. In: Nikolaidis, I., Barbeau, M., Kranakis, E. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2004. Lecture Notes in Computer Science, vol 3158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28634-9_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-28634-9_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22543-0

  • Online ISBN: 978-3-540-28634-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics