Advertisement

Telecommunication Systems

, Volume 38, Issue 3–4, pp 161–174 | Cite as

Software agent-based directed diffusion in wireless sensor network

  • Elhadi ShakshukiEmail author
  • Haroon Malik
  • Mieso K. Denko
Article

Abstract

In an environment where node density is massive, placement is heterogeneous and redundant sensory traffic is produced; limited network resources such as bandwidth and energy are hastily consumed by individual sensor nodes. Equipped with only a limited battery power supply, this minimizes the lifetime of these sensor nodes. At the network layer, many researchers have tackled this issue by proposing several energy efficient routing schemes. All these schemes tend to save energy by elevating redundant data traffic via in-network processing and choosing empirically good and shortest routing paths for transfer of sensory data to a central location (sink) for further, application-specific processing. Seldom has an attempt been made to reduce network traffic by moving the application-specific code to the source nodes. We unmitigated our efforts to augment the node lifetime within a sensor network by introducing mobile agents. These mobile agents can be used to greatly reduce communication costs, especially over low bandwidth links, by moving the processing function to the data rather than bringing the data to a central processor. Toward this end, we propose an agent-based directed diffusion approach to increase sensor node efficiency and we present the experimental results.

Keywords

Wireless sensor networks Data dissemination Software agents Mobile agents 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–116. CrossRefGoogle Scholar
  3. 3.
    Chen, M., Kwon, T., Yuan, Y., Choi, Y., & Leung, V. C. M. (2007). Mobile agent-based directed diffusion in wireless sensor networks. EURASIP Journal on Advances in Signal Processing, 2007. Article ID 36871, 13 pages. Google Scholar
  4. 4.
    Culler, D., Estrin, D., & Srivastava, M. (2004). Overview of sensor networks. IEEE Computer, 37(8), 41–49. Google Scholar
  5. 5.
    Dasgupta, P., Narasimhan, N., Moser, L. E., & Melliar-Smith, P. M. (1999). MAgNET: mobile agents for networked electronic trading. IEEE Transactions on Knowledge and Data Engineering, 11(4), 509–525. CrossRefGoogle Scholar
  6. 6.
    Elson, J., & Estrin, D. (2001). Random, ephemeral transaction identifiers in dynamic sensor networks. In Proceedings of the international conference on distributed computing systems (pp. 159–168). Phoenix, United States. Google Scholar
  7. 7.
    Hairong, Q., Xu, Y., & Wang, X. (2003). Mobile-agent-based collaborative signal and information processing in sensor networks. IEEE, 91(8), 1172–1183. CrossRefGoogle Scholar
  8. 8.
    He, M., Rogers, A., Luo, X., & Jennings, N. R. (2006). Designing a successful trading agent for supply chain management. In Proceedings of the 5th international conference on autonomous agents and multi-agent systems (pp. 1159–1166). Hakodate, Japan. Google Scholar
  9. 9.
    Heinzelman, W. R., Kulik, J., & Balakrishnan, H. (1999). Adaptive protocols for information dissemination in wireless sensor networks. In Proceedings of the 5th annual ACM/IEEE international conference on mobile computing and networking (pp. 174–185). Google Scholar
  10. 10.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the IEEE Hawaii international conference on system sciences. Google Scholar
  11. 11.
    Hill, J., & Culler, D. (2002). Mica: a wireless platform for deeply embedded networks. IEEE Micro, 22(6), 12–24. CrossRefGoogle Scholar
  12. 12.
    Hill, J., Szewczyk, R., Woo, A., Hollar, S., Culler, D. E., & Pister, K. S. J. (2000). System architecture directions for networked sensors. Architectural Support for Programming Languages and Operating Systems (ASPLOS), (4), 93–104. Google Scholar
  13. 13.
    Hofmann, M. O., McGovern, A., & Whitebread, K. R. (1998). Mobile agents on the digital battlefield. In Proceedings of the second international conference on autonomous agents (Agents’98) (pp. 219–225). Minneapolis/St. Paul, United States. Google Scholar
  14. 14.
    Hong, X., Gerla, M., Wang, H., & Clare, L. (2002). Load balanced, energy-aware communications for mars sensor networks. In Proceedings of IEEE Aerospace 2002. Google Scholar
  15. 15.
    Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: a scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th annual ACM/IEEE international conference on mobile computing and networking (MOBICOM’00) (pp. 56–67). Boston, United States. Google Scholar
  16. 16.
    Krishnamachari, B., Estrin, D., & Wicker, S. (2002). Modelling data-centric routing in wireless sensor networks. In Proceedings of IEEE INFOCOM’02. Google Scholar
  17. 17.
    Knoll, Meinkoehn, J. (1994). Data fusion using large multi-agent networks: an analysis of network structure and performance. In Proceedings of the international conference on multisensor fusion and integration for intelligent systems (MFI) (pp. 113–120). Las Vegas, United States. Google Scholar
  18. 18.
    Liu, J., & Jin, X. (2004). Agent-based, energy efficient routing in sensor networks. In Proceedings of the third international joint conference on autonomous agents and multiagent systems (AAMAS’04) (Vol. 1, pp. 472–479). Google Scholar
  19. 19.
    Marcel, B., Reiner, K., & Clemens, M. (2005). A modular platform for sophisticated real-time wireless sensor networking. TR 399, http://www5.informatik.uni-wuerzburg.de/publications/techreports/.
  20. 20.
    Mhatre, V., Rosenberg, C., Kofman, D., Azumdar, R., & Shroff, N. (2005). A minimum cost heterogeneous sensor network with a lifetime constraint. IEEE Transactions on Mobile Computing (TMC), 4(1), 4–15. CrossRefGoogle Scholar
  21. 21.
    Pister, K., Kahn, J., & Boser, B. http://robotics.eecs.berkeley.edu/~pister/SmartDust/.
  22. 22.
    Satyanarayanan, M. (2003). Of smart dust and brilliant rocks. IEEE Pervasive Computing, 2(4), 2–3. Google Scholar
  23. 23.
    Schurgers, C., & Srivastava, M. (2001). Energy efficient routing in wireless sensor networks. In MILCOM proceedings on communications for network-centric operations: creating the information force (pp. 357–361). Vienna, VA, 2001. Google Scholar
  24. 24.
    Shah, R. C., & Rabaey, J. M. (2002). Energy aware routing for low energy ad hoc sensor networks. In Proceedings of the IEEE wireless communications and networking conference (WCNC ’02). Google Scholar
  25. 25.
    Shakshuki, E., Ghenniwa, H., & Kamel, M. (2003). Agent-based system architecture for dynamic and open environments. International Journal of Information Technology and Decision Making, 2(1), 105–133. CrossRefGoogle Scholar
  26. 26.
    Sohrabi, K., Gao, J., Ailawadhi, V., & Pottie, G. J. (2000). Protocols for self-organization of a wireless sensor network. IEEE Personal Communications, 7(5), 16–27. CrossRefGoogle Scholar
  27. 27.
    Stojmenovic, I. (2006). Localized network layer protocols in sensor networks based on optimizing cost over progress ratio. IEEE Network, 20(1), 21–27. CrossRefGoogle Scholar
  28. 28.
    Thomas, H. (2006). An FDL’ed textbook on sensor networks (GNU FDL). Google Scholar
  29. 29.
    Trigoni, N., Yao, Y., Demers, A., Gehrke, J., & Rajara, R. (2004). Wave scheduling: energy-efficient data dissemination for sensor networks. In Proceedings of the 1st international workshop on data management for sensor networks (DMSN). Conjunction with the international conference on very large data bases (VLDB) (pp. 48–57). Toronto, Canada. Google Scholar
  30. 30.
    Wu, Q., Rao, N. S. V., Barhen, J., Iyengar, S. S., Vaishnavi, V. K., Qi, H., & Chakrabarty, K. (2004). On computing mobile agent routes for data fusion in distributed sensor networks. IEEE Transactions on Knowledge and Data Engineering, 16(6), 740–753. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Elhadi Shakshuki
    • 1
    Email author
  • Haroon Malik
    • 1
  • Mieso K. Denko
    • 2
  1. 1.Jodrey School of Computer ScienceAcadia UniversityWolfvilleCanada
  2. 2.Dept. of Computing and Info. ScienceUniversity of GuelphGuelphCanada

Personalised recommendations