Adaptive Negotiation in Managing Wireless Sensor Networks

  • Thao P. Le
  • Timothy J. Norman
  • Wamberto Vasconcelos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)

Abstract

The allocation of resources to tasks in an efficient manner is a key problem in computer science. One important application domain for solutions to this class of problem is the allocation of sensor resources for environmental monitoring, surveillance, or similar sensing tasks. In real-world problem domains, the problem is compounded by the fact that the number of tasks and resources change over time, the number of available resources is limited and tasks compete for resources. Thus, it is necessary for a practical allocation mechanism to have the flexibility to cope with dynamic environments, and to ensure that unfair advantages are not given to a subset of the tasks (say, because they arrived first). Typical contemporary approaches use agents to manage individual resources, and the allocation problem is modelled as a coordination problem. In existing approaches, however, the successful allocation of resources to a new task is strongly dependent upon the allocation of resources to existing tasks. In this paper we propose a novel negotiation mechanism for exchanging resources to accommodate the arrival of new tasks, dynamically re-arranging the resource allocation. We have shown, via a set of experiments, that our approach offers significantly better results when compared with an agent-based approach without resource re-allocation through concurrent negotiation.

Keywords

Multiagent System Sensor Type Successful Task Sensor Agent Fully Polynomial Time Approximation Scheme 
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.
    Chevaleyre, Y., Dunne, P.E., Endriss, U., Lang, J., Lemaitre, M., Maudet, N., Padget, J., Phelps, S., Rodrguez-aguilar, J.A., Sousa, P.: Issues in multiagent resource allocation. Informatica 30 (2006)Google Scholar
  2. 2.
    Heinzelman, W.R., Kulik, J., Balakrishnan, H.: Adaptive protocols for information dissemination in wireless sensor networks. In: Proceedings of the ACM MobiCom 1999, Seattle, Washington, pp. 174–185 (1999)Google Scholar
  3. 3.
    Howard, A., Viguria, A.: Controlled reconfiguration of robotic mobile sensor networks using distributed allocation formalisms. In: Proc. of the NASA Science Technology Conference, NSTC 2007 (2007)Google Scholar
  4. 4.
    Jacyno, M., Bullock, S., Payne, T., Luck, M.: Understanding decentralised control of resource allocation in a minimal multi-agent system. In: AAMAS 2007: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. pp. 208–210 (2007)Google Scholar
  5. 5.
    Johnson, M.P., Rowaihy, H., Pizzocaro, D., Bar-Noy, A., Chalmers, S., La Porta, T., Preece, A.: Frugal Sensor Assignment. In: Nikoletseas, S.E., Chlebus, B.S., Johnson, D.B., Krishnamachari, B. (eds.) DCOSS 2008. LNCS, vol. 5067, pp. 219–236. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Krishna, V.: Auction Theory. Academic Press (2002)Google Scholar
  7. 7.
    Kulik, J., Heinzelman, W.: Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless Networks 8, 169–185 (2002)Google Scholar
  8. 8.
    Le, T.P., Norman, T.J., Vasconcelos, W.: Agent-based sensor-mission assignment for tasks sharing assets. In: Proceeding of the Third International Workshop on Agent Technology for Sensor Networks, Budapest, Hungary (May 2009)Google Scholar
  9. 9.
    Nguyen, T.D.: A heuristic model for concurrent bilateral negotiations in incomplete information settings. Ph.D. thesis, University of Southampton, Southampton, England (2005)Google Scholar
  10. 10.
    Nguyen, T.D., Jennings, N.R.: Coordinating multiple concurrent negotiations. In: Proceedings of the Third International Conference on Autonomous Agents and Multiagent Systems, New York, USA, pp. 1064–1071 (2004)Google Scholar
  11. 11.
    Preece, A., Pizzocaro, D., Borowiecki, K., de Mel, G., Gomez, M., Vasconcelos, M., Bar-Noy, A., Johnson, M.P., La Porta, T.L., Rowaihy, H., Pearson, G., Pham, T.: Reasoning and resource allocation for sensor-mission assignment in a coalition context. In: MILCOM 2008 (2008)Google Scholar
  12. 12.
    Salemi, B., Will, P., min Shen, W.: Distributed task negotiation in modular robots. Robotics Society of Japan, Special Issue (2003)Google Scholar
  13. 13.
    Sensoy, M., Le, T., Vasconcelos, W.W., Norman, T.J., Preece, A.D.: Resource determination and allocation in sensor networks: A hybrid approach. Computer Journal (2010) (to appear)Google Scholar
  14. 14.
    Shima, T., Rasmussen, S.J., Chandler, P.: UAV team decision and control using efficient collaborative estimation. In: Proceedings of the 2005 American Control Conference, vol. 6, pp. 4107–4112 (2005)Google Scholar
  15. 15.
    Sujit, P.B., Sinha, A., Ghose, D.: Multiple UAV task allocation using negotiation. In: AAMAS 2006: Proceedings of the Fifth International Conference on Autonomous Agents and Multiagent Systems, pp. 471–478 (2006)Google Scholar
  16. 16.
    Sung, S.C., Vlach, M.: Maximizing weighted number of just-in-time jobs on unrelated parallel machines. Journal of Scheduling 8(5), 453–460 (2005)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Thao P. Le
    • 1
  • Timothy J. Norman
    • 1
  • Wamberto Vasconcelos
    • 1
  1. 1.Department of Computer ScienceKing’s College, University of AberdeenUK

Personalised recommendations