Skip to main content

An Adaptive Distributed Resource Allocation Scheme for Sensor Networks

  • Conference paper
Mobile Ad-hoc and Sensor Networks (MSN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 4325))

Included in the following conference series:

Abstract

A major research challenge in the field of sensor networks is the distributed resource allocation problem, which concerns how the limited resources in a sensor network should be allocated or scheduled to minimize costs and maximize the network capability. In this paper, we propose the Adaptive Distributed Resource Allocation (ADRA) scheme, which specifies relatively simple local actions to be performed by individual sensor nodes in a sensor network for mode management. Each node adapts its operation over time in response to the status and feedback of its neighboring nodes. Desirable global behavior results from the local interactions between nodes.

We study the effectiveness of the ADRA scheme for a realistic application scenario; namely, the sensor mode management for an acoustic wireless sensor network to track vehicle movement. We evaluated the scheme via simulations, and also prototyped the acoustic wireless sensor network scenario using the Crossbow MICA2 motes. Our simulation and hardware implementation results indicate that the ADRA scheme provides a good tradeoff between performance objectives such as coverage area, power consumption, and network lifetime.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Ali, S., Kim, J., Siegel, H., Maciejewski, A., Yu, Y., Gundala, S., Gertphol, S., Prasanna, V.: Greedy heuristics for resource allocation in dynamic distributed real-time heterogeneous computing systems. In: Proc. of the 2002 Intl. Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA 2002), Las Vegas, NV, pp. 519–530 (2002)

    Google Scholar 

  2. Modi, P., Scerri, P., Shen, W.M., Tambe, M.: Distributed Resource Allocation: A Distributed Constraint Reasoning Approach. In: Distributed Sensor Networks: A Multiagent Perspective. Kluwer Academic Publishers, Dordrecht (2003)

    Google Scholar 

  3. Salido, M., Barber, F.: Distributed constraint satisfaction problems for resource allocation. In: Proc. of the AAMAS 2003 Workshop on Decentralized Resource Allocation, Melbourne, Australia (2003)

    Google Scholar 

  4. Mainland, G., Kang, L., Lahaie, S., Parkes, D., Welsh, M.: Using virtual markets to program global behavior in sensor networks. In: Proc. of the 11th ACM SIGOPS European Workshop, Leuven, Belgium (2004)

    Google Scholar 

  5. Wellman, M.: Market-Oriented Programming: Some Early Lessons. In: Market-Based Control: A Paradigm for Distributed Resource Allocation. World Scientific, Singapore (1996)

    Google Scholar 

  6. Ostwald, J., Lesser, V.: Combinatorial auctions for resource allocation in a distributed sensor network. Technical Report 04-72, Univ. of Massachusetts CS Department (2004)

    Google Scholar 

  7. Nisan, N.: Bidding and allocation in combinatorial auctions. In: Proc. of the 2nd ACM Conf. on Electronic Commerce, Minneapolis, MN, pp. 1–12 (2000)

    Google Scholar 

  8. Shehory, O., Kraus, S.: Methods for task allocation via agent coalition formation. Artificial Intelligence 101(1-2), 165–200 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  9. Davis, R., Smith, R.: Negotiation as a metaphor for distributed problem solving. Artificial Intelligence 20(1), 63–109 (1983)

    Article  Google Scholar 

  10. Mailler, R., Lesser, V., Horling, B.: Cooperative negotiation for soft real-time distributed resource allocation. In: Proc. of the 2nd Intl. Joint Conf. on Autonomous Agents and Multiagent Systems, Melbourne, Australia, pp. 576–583 (2003)

    Google Scholar 

  11. Frank, M., Bugacov, A., Chen, J., Dakin, G., Szekely, P., Neches, B.: The marbles manifesto: A definition and comparison of cooperative negotiation schemes for distributed resource allocation. In: Proc. of the 2001 AAAI Fall Symp. on Negotation Methods for Autonomous Cooperative Systems, North Falmouth, MA, pp. 36–45 (2001)

    Google Scholar 

  12. Stansfield, R.G.: Statistical theory of df fixing. Journal of the IEE (London), Part IIIA 94(15), 762–770 (1947)

    MathSciNet  Google Scholar 

  13. Repast 3.0 - recursive porous agent simulation toolkit, http://repast.sourceforge.net

  14. Mica2 user’s manual, http://www.xbow.com/support/support_pdf_files/mts-mda_series_users_manual.pdf

  15. Gay, D., et al.: The nesc language: A holistic approach to networked embedded systems. In: Proc. of the 2003 ACM SIGPLAN Conf on Programming Language Design and Implementation (PLDI 2003), San Diego, CA, pp. 1–11 (2003)

    Google Scholar 

  16. Hill, J., et al.: System architecture directions for networked sensors. In: Proc. of the 9th Intl Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2000), Cambridge, MA, pp. 93–104 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lim, H.B., Lam, V.T., Foo, M.C., Zeng, Y. (2006). An Adaptive Distributed Resource Allocation Scheme for Sensor Networks. In: Cao, J., Stojmenovic, I., Jia, X., Das, S.K. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2006. Lecture Notes in Computer Science, vol 4325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11943952_65

Download citation

  • DOI: https://doi.org/10.1007/11943952_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49932-9

  • Online ISBN: 978-3-540-49933-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics