Data-Centric Storage in Non-Uniform Sensor Networks

  • M. Albano
  • S. Chessa
  • F. Nidito
  • S. Pelagatti
Part of the Signals and Communication Technology book series (SCT)

Abstract

Dependable data storage in wireless sensor networks is becoming increasingly important, due to the lack of reliability of the individual sensors. Recently, data-centric storage (DCS) has been proposed to manage network-sensed data. DCS reconsiders ideas and techniques successfully proposed in peer-to-peer systems within the framework of wireless sensor networks. In particular it assumes that data are uniquely named and data storage and retrieval is achieved using names instead of sensor nodes addresses. In this chapter, we discuss the limitations of previous approaches, and in particular of geographic hash tables (GHT), and introduce DELiGHT, a protocol that provides fine QoS control by the user and ensures even data distribution, also in non-uniform sensor networks. The merits of DELiGHT have been evaluated through simulation in uniform and Gaussian-distributed sensor networks. The simulation results show that the protocol provides a better load balancing than the previous proposals and that the QoS is ensured without appreciable overhead.

Keywords

Sensor Network Hash Function Sink Node Distribute Hash Table Sensor Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Albano, M., Chessa, S., Nidito, F., Pelagatti, S.: “Geographic hash tables with QoS in non uniform sensor networks”, ACM Mobihoc 2006 (Poster Session), Firenze, Italy, 22–25 May 2006, p. 3.Google Scholar
  2. [2]
    Albano, M., Chessa, S., Nidito, F., Pelagatti, S.: “Q-NiGHT: adding QoS to data centric storage in non-uniform sensor networks”, Technical report, Dipartimento di Informatica, Università di Pisa, 2006.Google Scholar
  3. [3]
    Araujo, F., Rodrigues, L., Kaiser, J., Liu, C., Mitidieri, C.: “CHR: a distributed hash table for wireless ad hoc networks”, In: Proc. of the 25th IEEE ICDCSW’05, 2005.Google Scholar
  4. [4]
    Baronti, P., Pillai, P., Chook, V., Chessa, S., Gotta, A., Hu, Y.F.: “Wireless sensor networks: a survey on the state of the art and the 802.15.4 and ZigBee standards”, Computer Communications, 2007, doi: 10.1016/j.comcom.2006.12.020.CrossRefGoogle Scholar
  5. [5]
    Bian, F., Govindan, R., Schenker, S., Li, X.: “Using hierarchical location names for scalable routing and rendezvous in wireless sensor networks”, In: SenSys ’04, New York, 2004, pp. 305–306.Google Scholar
  6. [6]
    Bose, P., Morin, P., Stoimenovic, I., Urrutia, J.: “Routing with guaranteed delivery in ad hoc wireless networks”}, Wireless Netw., 7(6), 2001, 609–616. Also in DialM’99, Seattle, August 1999, pp. 48–55.CrossRefGoogle Scholar
  7. [7]
    Intanagonwiwat, C., Govindan, R., Estrin, D.: “Directed diffusion: a scalable and robust communication paradigm for sensor networks”, In: Proc. of MobiCom 2000, Boston, 2000, pp. 56–67.Google Scholar
  8. [8]
    Jaromczyk, J., Toussaint, G.: “Relative neighborhood graphs and their relatives”, Proc. IEEE, 80(9), 1992, 1502–1517.CrossRefGoogle Scholar
  9. [9]
    Maddenand, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: “The design of an acquisitional query processor for sensor networks”, In: Proc. of the 2003 SIGMOD Conference, San Diego, 2003, pp. 491–502.Google Scholar
  10. [10]
    Neumann, J.V.: “Various techniques used in connection with random digits”, In Taub, A.H., ed., John von Neumann, Collected Works, Volume 5, Pergamon Press, Oxford, 1951, 768–770.Google Scholar
  11. [11]
    Newsome, J., Song, D.: “GEM: Graph EMbedding for routing and data-centric storage in sensor networks without geographic information”, In: Proc. of the First International Conference on Embedded Networked Sensor Systems, Los Angeles, 2003, pp. 76–88.Google Scholar
  12. [12]
    Ratnasamy, S., Karp, B., Shenker, S., Estrin, D., Govindan, R., Yin, L., Yu, F. “Data-centric storage in sensornets with GHT, a geographic hash table”, Mob. Netw. Appl. (MONET), 8(4), 2003, 427–442.CrossRefGoogle Scholar
  13. [13]
    Wilson, E.B., Hilferty, M.M.: “The distribution of chi-square”, Proc. Natl. Acad. Sci., 17, 1931, 684–688.CrossRefGoogle Scholar
  14. [14]
    Yao, Y., Gehrke, J.: “The Cougar approach to in-network query processing in sensor networks”, SIGMOD Rec., 31(3), 2002, 9–18.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • M. Albano
    • 1
  • S. Chessa
    • 1
    • 2
  • F. Nidito
    • 1
  • S. Pelagatti
    • 1
  1. 1.Computer Science DepartmentUniversity of Pisa56127 PisaItaly
  2. 2.Istituto di Scienza e Tecnologie dell’Informazione, Area della Ricerca56100 PisaItaly

Personalised recommendations