Mobility Helps Data Delivery in Disruption Tolerant Networks

  • Kaoru Sezaki
  • Niwat Thepvilojanapong
  • Yoshito Tobe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4836)


Sensor networks using mobile robots have recently been proposed to deal with data communication in disruption tolerant networks (DTNs) where an instantaneous end-to-end path between a source and destination may not exist. In such network scenarios, a node should move to deliver data to the destination. In this paper, we study adaptive formations of mobile robots based on the knowledge of network topology held by each node. Different node formations are applied when nodes have neighboring, clustering, or perfect information of network. Node formations also depend on traffic patterns, e.g., single and multiples packets per event. We introduce a straight line formation called pipeline for delivering multiple packets continuously. The benefit of controlled mobility in DTNs is validated through the ns–2 simulation tool by comparing with the ideal cases.


routing mobile sensor networks disruption tolerant networks mobile robots evaluation simulation 


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  1. 1.
    Vahdat, A., Becker, D.: Epidemic routing for partially-connected ad hoc networks. Technical Report CS-2000-06, Duke University (July 2000)Google Scholar
  2. 2.
    Zhao, W., Ammar, M., Zegura, E.: A message ferrying approach for data delivery in sparse mobile ad hoc networks. In: Proceedings of MobiHoc, pp. 187–198 (May 2004)Google Scholar
  3. 3.
    Thepvilojanapong, N., Tobe, Y., Sezaki, K.: Impact of intentional mobility in sparse sensor networks. In: Proceedings of SenSys, pp. 286–287 (November 2005)Google Scholar
  4. 4.
    Karp, B., Kung, H.T.: GPSR: greedy perimeter stateless routing for wireless networks. In: Proceedings of MobiCom, Boston, MA, USA, pp. 243–254 (August 2000)Google Scholar
  5. 5.
    Beaufour, A., Leopold, M., Bonnet, P.: Smart-tag based data dissemination. In: Proceedings of WSNA, pp. 68–77 (September 2002)Google Scholar
  6. 6.
    Shah, R.C., et al.: Data mules: Modeling a three-tier architecture for sparse sensor networks. In: Proceedings of SNPA, pp. 30–41 (May 2003)Google Scholar
  7. 7.
    Jain, S., et al.: Exploiting mobility for energy efficient data collection in sensor networks. In: Proceedings of WiOpt (March 2004)Google Scholar
  8. 8.
    Goldenberg, D.K., Lin, J., Morse, A.S.: Towards mobility as a network control primitive. In: Proceedings of MobiHoc, pp. 163–174 (May 2004)Google Scholar
  9. 9.
    Small, T., Haas, Z.J.: The shared wireless infostation model: a new ad hoc networking paradigm (or where there is a whale, there is a way). In: Proceedings of MobiHoc, pp. 233–244 (June 2003)Google Scholar
  10. 10.
    Fall, K.: A delay-tolerant network architecture for challenged internets. In: Proceedings of SIGCOMM, Karlsruhe, Germany, pp. 27–34 (August 2003)Google Scholar
  11. 11.
    Jain, S., Fall, K., Patra, R.: Routing in a delay tolerant network. In: Proceedings of SIGCOMM, Portland, Oregon, USA, pp. 145–158 (August 2004)Google Scholar
  12. 12.
    Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: Proceedings of MobiCom, Boston, Massachusetts, USA, pp. 56–67 (August 2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kaoru Sezaki
    • 1
  • Niwat Thepvilojanapong
    • 2
  • Yoshito Tobe
    • 3
  1. 1.Center for Spatial Information Science, University of Tokyo, 4–6–1 Komaba Meguro Tokyo 153–8505Japan
  2. 2.Department of Information and Communication Engineering, University of Tokyo, 7–3–1 Hongo Bunkyo Tokyo 113–8654Japan
  3. 3.Department of Info. Systems and Multimedia Design, Tokyo Denki University, 2–2 Kanda-Nishikicho Chiyoda Tokyo 101–8457Japan

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