A Mobile Agent Routing Protocol for Data Aggregation in Wireless Sensor Networks

  • Saeid Pourroostaei Ardakani
  • Julian Padget
  • Marina De Vos


Mobile agent data aggregation routing forwards mobile agents in wireless sensor network to collect and aggregate data. The key objective of data aggregation routing is to maximise the number of collected data samples at the same time as minimising network resource consumption and data collection delay. This paper proposes a mobile agent routing protocol, called zone-based mobile agent aggregation. This protocol utilises a bottom-up mobile agent migration scheme in which the mobile agents start their journeys from the centre of the event regions to the sink aiming to reduce the MA itinerary cost and delay and increase data aggregation routing accuracy. In addition, the proposed protocol reduces the impact of network architecture, event source distribution model and/or data heterogeneity on the performance of data aggregation routing.


Wireless sensor networks Mobile agents Data aggregation Itinerary planning 


  1. 1.
    X. Zhu and W. Zhang, A mobile agent-based clustering data fusion algorithm in wsn, International Journal of Electrical and Computer Engineering, Vol. 5, No. 5, pp. 277–280, 2010.Google Scholar
  2. 2.
    E. Fasolo, M. Rossi, J. Widmer and M. Zorzi, In-network aggregation techniques for wireless sensor networks: A survey, Wireless Communications, Vol. 14, No. 2, pp. 70–87, 2007.CrossRefGoogle Scholar
  3. 3.
    Y. Xu and H. Qi, Mobile agent migration modeling and design for target tracking in wireless sensor networks, Ad Hoc Networks, Vol. 6, No. 1, pp. 1–16, 2008.MathSciNetCrossRefGoogle Scholar
  4. 4.
    P. K. Biswas, H. Qi and Y. Xu, Mobile-agent-based collaborative sensor fusion, Information Fusion, Vol. 9, No. 3, pp. 399–411, 2008.CrossRefGoogle Scholar
  5. 5.
    I. E. Venetis, G. Pantziou, D. Gavalas and C. Konstantopoulos, “Benchmarking mobile agent itinerary planning algorithms for data aggregation on wsns,” The Sixth International Conf on Ubiquitous and Future Networks (ICUF), Shanghai, China, Vol. 8–11, No. July, pp. 105–110, 2014.Google Scholar
  6. 6.
    H. Qi and F. Wang, “Optimal itinerary analysis for mobile agents in ad hoc wireless sensor networks,” In the proceedings of the13th International Conference on Wireless Communication, Calgary, Alberta, Canada, July 9-11, vol. 1, no. 1, pp. 147–153, 2001.Google Scholar
  7. 7.
    M. Chen, V. Leung, S. Mao, T. Kwon, and M. Li, “Energy-efficient itinerary planning for mobile agents in wireless sensor networks,” IEEE International Conference on Communications (ICC), Dresden, Germany, June 14-18, vol. 1, no. 1, pp. 1–5, 2009.Google Scholar
  8. 8.
    D. Gavalas, A. Mpitziopoulos, G. Pantziou and C. Konstantopoulos, An approach for near-optimal distributed data fusion in wireless sensor networks, Wireless Networks, Vol. 16, pp. 1407–1425, 2010.CrossRefGoogle Scholar
  9. 9.
    C. Konstantopoulos, A. Mpitziopoulos, D. Gavalas and G. Pantziou, Effective determination of mobile agent itineraries for data aggregation on sensor networks, IEEE transactions on knowledge and data engineering, Vol. 22, No. 12, pp. 1679–1693, 2010.CrossRefGoogle Scholar
  10. 10.
    I. Solis and K. Obraczka, “The impact of timing in data aggregation for sensor networks,” IEEE International Conference on Communications (ICC), Paris, France, June, 20-24, vol. 6, pp. 3640–3645, 2004.Google Scholar
  11. 11.
    F. Ye, A. Chen, S. Lu, and L. Zhang, “A scalable solution to minimum cost forwarding in large sensor networks,” The 10th International Conference on Computer Communications and Networks, Scottsdale, Arizona, USA, October 15-17, vol. 5, no. 2, pp. 304–309, 2001.Google Scholar
  12. 12.
    S. P. Ardakani, J. Padget and M. D. Vos, Hrts: A hierarchical reactive time synchronization protocol for wireless sensor networks, Ad Hoc Networks, Vol. 129, pp. 47–62, 2014.CrossRefGoogle Scholar
  13. 13.
    C. E. Perkins and E. M. Royer, “Ad-hoc on-demand distance vector routing,” Second IEEE Workshop on Mobile Computer Systems and Applications (WMCSA ’99), New Orleans, Louisiana, USA, February 25-26, vol. 1, no. 1, pp. 90–100, 1999.Google Scholar
  14. 14.
    I. D. Chakeres and E. M. Belding-Royer, “The utility of hello messages for determining link connectivity,” 5th International Symposium on Wireless Personal Multimedia Communications (WPMC), Honolulu, Hawaii, October 27-30, vol. 2, pp. 504–508, 2002.Google Scholar
  15. 15.
    S. Vasudevan, B. DeCleene, N. Immerman, J. Kurose and D. Towsley, Leader election algorithms for wireless ad hoc networks, DARPA Information Survivability Conference and Exposition, Vol. 1, pp. 261–272, 2003.CrossRefGoogle Scholar
  16. 16.
    J. Xu, W. Liu, F. Lang, Y. Zhang and C. Wang, Distance measurement model based on rssi in wsn, Wireless Sensor Network, Vol. 2, No. 8, pp. 606–611, 2010.CrossRefGoogle Scholar
  17. 17.
    P. Uthansakul, M. E. Bialkowski, S. Durrani, K. Bialkowski, and A. Postula, “Effect of line of sight propagation on capacity of an indoor mimo system,” IEEE Antennas and Propagation Society International Symposium 2005, 3-8 July, Washington, DC, pp. 707–710, 2005.Google Scholar
  18. 18.
    X. Shen, Z. Wang, P. Jiang, R. Lin, and Y. Sun, “Connectivity and rssi based localization scheme for wireless sensor networks,” International Conference on Intelligent Computing (ICIC’05), Hefei, China, August 23-26, vol. 1, no. 2, pp. 578–587, 2005.Google Scholar
  19. 19.
    OMNET++, “Omnet++ simulator,” 2012,, Retrieved (March 2016).
  20. 20.
    A. Viklund, “Mixim code,” 2013,, Retrieved (December, 2015).
  21. 21.
    A. Kopke, M. Swigulski, K. Wessel, D. Willkomm, P. K. Haneveld, T. Parker, O. Visser, H. Lichte, and S. Valentin, “Simulating wireless and mobile networks in omnet++ the mixim vision,” the 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems (Simutools ’08), Marseille, France, March 3-7, vol. 1, no. 1, pp. 71–78, 2008.Google Scholar
  22. 22.
    Q. Wu, N. S. Rao, J. Barhen, S. S. Iyengar, V. K. Vaishnavi, H. Qi and K. Chakrabarty, On computing mobile agent routes for data fusion in distributed sensor networks, IEEE Transactions on Knowledge & Data Engineering, Vol. 16, No. 6, pp. 740–753, 2004.CrossRefGoogle Scholar
  23. 23.
    M. Chen, S. Gonzalez, Y. Zhang and V. C. Leung, Multi-agent itinerary planning for sensor networks, Quality of Service in Heterogeneous Networks, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 22, pp. 584–597, 2009.CrossRefGoogle Scholar
  24. 24.
    A. Boulis, S. Ganeriwal and M. B. Srivastava, Aggregation in sensor networks: an energyaccuracy trade-off, Ad Hoc Networks, Vol. 1, No. 1, pp. 317–331, 2003.CrossRefGoogle Scholar
  25. 25.
    M. Chen, T. Kwon, Y. Yuan, Y. Choi and V. C. M. Leung, Mobile agent-based directed diffusion in wireless sensor networks, EURASIP Journal on Advances in Signal Processing, Vol. 2007, No. 1, p. 219, 2007.Google Scholar
  26. 26.
    S. S. A. Basurra, “Collision guided routing for ad-hoc mobile wireless networks,” Ph.D. dissertation, Department of Computer Science, University of Bath, October 2012.Google Scholar
  27. 27.
    S. A. R. Zaidi, M. Hafeez, S. A. Khayam, D. Mclernon, M. Ghogho, and K. Kim, “On minimum cost coverage in wireless sensor networks,” The 43rd Annual Conference on Information Sciences and Systems (CISS 09), Johns Hopkins University, Baltimore, MD, March 18-20, vol. 2, no. 3, pp. 213–218, 2009.Google Scholar
  28. 28.
    M. A. Youssef, A. Youssef and M. F. Younis, Overlapping multihop clustering for wireless sensor networks, IEEE Transactions on parallel and distributed systems, Vol. 20, No. 12, pp. 1844–1856, 2009.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Computer ScienceAllameh Tabataba’i UniversityTehranIran
  2. 2.Computer ScienceUniversity of BathBathUK

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