Advertisement

Heuristics for Mobile Object Tracking Problem in Wireless Sensor Networks

  • Li Liu
  • Hao Li
  • Junling Wang
  • Lian Li
  • Caihong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5598)

Abstract

The network lifetime and application requirement are two fundamental, yet conflicting, design objectives in wireless sensor networks for tracking mobile objects. The application requirement is often correlated to the delay time within which the application can send its sensing data back to the users in tracking networks. In this paper we study the network lifetime maximization problem and the delay time minimization problem together. To make both problems tractable, we have the assumption that each sensor node keeps working since it turns on. And we formulate the network lifetime maximization problem as maximizing the number of sensor nodes who don’t turn on, and the delay time minimization problem as minimizing the routing path length, after achieving the required tracking tasks. Since we prove the problems are NP-complete and APX-complete, we propose three heuristic algorithms to solve them. And we present several experiments to show the advantages and disadvantages referring to the network lifetime and the delay time among these three algorithms on three models, random graphs, grids and hypercubes.

Keywords

Sensor Node Wireless Sensor Network Source Node Network Lifetime Sink Node 
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. [Bern and Plassmann]
    Bern, M., Plassmann, P.: The steiner problem with edge lengths 1 and 2. Information Processing Letters 32, 171–176 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  2. [INFOCOM07]
    Chen, Y., Fleury, E.: A Distributed Policy Scheduling for Wireless Sensor Networks. In: INFOCOM 2007. IEEE, Anchorage (2007)Google Scholar
  3. [SECON07]
    Floreen, P., Kaski, P., Suomela, J.: A distributed approximation scheme for sleep sceduling in sensor networks. In: SECON 2007, San Diego, CA (2007)Google Scholar
  4. [ICMSAS04]
    He, T., Krishnamurthy, S., Stankovic, J.A., Abdelzaher, T., Luo, L., Stoleru, R., Yan, T., Gu, L., Hui, J., Krogh, B.: Energy-efficient surveillance system using wireless sensor networks. In: International Conference On Mobile Systems, Applications And Services, Boston, MA, USA, pp. 270–283 (2004)Google Scholar
  5. [IRTETAS06]
    He, T., Vicaire, P., Yan, T., Luo, L., Gu, L., Zhou, G., Stoleru, R., Qing, C., Stankovic, J.A., Abdelzaher, T.: Achieving Real-Time Target Tracking UsingWireless Sensor Networks. In: Proceedings of the 12th IEEE on Real-Time and Embedded Technology and Applications Symposium, April 2006, pp. 37–48 (2006)Google Scholar
  6. [MobiHoc06]
    Keshavarzian, A., Lee, H., Venkatraman, L.: Wakeup Scheduling in Wireless Sensor Networks. In: MobiHoc 2006, Florence, Italy, May 22-25 (2006)Google Scholar
  7. [Sinha and Chandrakasan]
    Sinha, A., Chandrakasan, A.: Dynamic Power Management in Sensor Networks. IEEE Design & Test of Computers 18(2), 62–74 (2001)CrossRefGoogle Scholar
  8. [Mobihoc2000]
    Subramanian, L., Katz, R.H.: An architecture for building self-configurable systems. In: MOBIHOC 2000, Boston, USA, pp. 63–73 (2000)Google Scholar
  9. [SIGCSE06]
    Tran, S., Yang, T.: Evaluations of target tracking in wireless sensor networks. In: Proceedings of the 37th SIGCSE technical symposium on Computer science education, Houston, Texas, USA, pp. 97–101 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Li Liu
    • 1
    • 2
  • Hao Li
    • 2
  • Junling Wang
    • 1
  • Lian Li
    • 1
  • Caihong Li
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhou, GansuP. R. China
  2. 2.LRI, Univ Paris-sud and CNRSOrsayFrance

Personalised recommendations