Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks

  • Abhishek Ghose
  • Jens Grossklags
  • John Chuang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2574)


Wireless sensor networks will be used in a wide range of challenging applications where numerous sensor nodes are linked to monitor and report distributed event occurrences. In contrast to traditional communication networks, the single major resource constraint in sensor networks is power, due to the limited battery life of sensor devices. It has been shown that data-centric methodologies can be used to solve this problem efficiently. In data-centric storage, a recently proposed data dissemination framework, all event data is stored by type at designated nodes in the network and can later be retrieved by distributed mobile access points in the network. In this paper we propose Resilient Data-Centric Storage (R-DCS) as a method to achieve scalability and resilience by replicating data at strategic locations in the sensor network. Through analytical results and simulations, we show that this scheme leads to significant energy savings in reasonably large-sized networks and scales well with increasing node-density and query rate. We also show that R-DCS realizes graceful performance degradation in the presence of clustered as well as isolated node failures, hence making the sensornet data robust.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    W. Adjie-Winoto, E. Schwartz, H. Balakrishnan, and J. Lilley. The Design and Implementation of an Intentional Naming System. In Proceedings of ACM SOSP-99, pages 186–201, Dec. 1999.Google Scholar
  2. 2.
    B. H. Bloom. Space/Time Trade-offs in Hash Coding with Allowable Errors. Communications of the ACM, 13(7):422–426, July 1970.zbMATHCrossRefGoogle Scholar
  3. 3.
    L. Fan, P. Cao, J. Almeida, and A. Broder. Summary Cache: A Scalable Widearea Web Cache Sharing Protocol. In Proceedings of ACM SIGCOMM’98, pages 254–265, Sept. 1998.Google Scholar
  4. 4.
    J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, and D. Ganesan. Building Efficient Wireless Sensor Networks with Low-Level Naming. In Proceedings of ACM SOSP-01, 35(5):146–159, Oct. 2001.Google Scholar
  5. 5.
    C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed difuusion: A Scalable and Robust Communication Paradigm for Sensor Networks. In Proceedings of ACM MOBICOM-00, pages 56–67, Aug. 2000.Google Scholar
  6. 6.
    B. Karp and H. T. Kung. GPSR: Greedy Perimeter Stateless Routing for Wireless Networks. In Proceedings of ACM MOBICOM-00, pages 243–254, Aug. 2000.Google Scholar
  7. 7.
    S. Ratnasamy, B. Karp, Y. Li, F. Yu, R. Govindan, S. Shenker and D. Estrin. GHT: A Geographic Hash Table for Data-Centric Storage. In Proceedings of ACM WSNA-02, Oct. 2002.Google Scholar
  8. 8.
    S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker. A Scalable Content-Addressable Network. In Proceedings of ACM SIGCOMM 2001, 31(4):161–172, Aug. 2001.Google Scholar
  9. 9.
    S. Shenker, S. Ratnasamy, B. Karp, R. Govindan, and D. Estrin. Data-Centric Storage in Sensornets. In Proceedings of ACM HotNets-I, Oct. 2002.Google Scholar
  10. 10.
    I. Stoica, R. Morris, D. Karger, F. Kaashoek, and H. Balakrishnan. Chord: A Scalable Peer-To-Peer Lookup Service for Internet Applications. In Proceedings of ACM SIGCOMM 2001, 31(4):149–160, Aug. 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Abhishek Ghose
    • 1
  • Jens Grossklags
    • 1
  • John Chuang
    • 1
  1. 1.University of California at BerkeleyUSA

Personalised recommendations