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

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Abstract

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.

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Notes

  1. To find out the number of uniformly positioned (grid) nodes to fully cover a 2D area, factor 0.3125 should change to 0.5 in this equation. The original equation (with factor 0.3125) does not consider the uncovered area which is formed among each four sensor nodes that are placed in a \({2\times 2}\) grid. Owing to this, factor 0.5 should be used as one node is required to fill the uncovered area for each four nodes.

References

  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. 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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  19. OMNET++, “Omnet++ simulator,” 2012, http://www.omnetpp.org/, Retrieved (March 2016).

  20. A. Viklund, “Mixim code,” 2013, http://mixim.sourceforge.net/index.html, Retrieved (December, 2015).

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

  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.

  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.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science, Allameh Tabataba’i University, Tehran, Iran

    Saeid Pourroostaei Ardakani

  2. Computer Science, University of Bath, Bath, UK

    Julian Padget & Marina De Vos

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  1. Saeid Pourroostaei Ardakani
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  2. Julian Padget
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  3. Marina De Vos
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Correspondence to Saeid Pourroostaei Ardakani.

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Pourroostaei Ardakani, S., Padget, J. & De Vos, M. A Mobile Agent Routing Protocol for Data Aggregation in Wireless Sensor Networks. Int J Wireless Inf Networks 24, 27–41 (2017). https://doi.org/10.1007/s10776-016-0327-y

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  • DOI: https://doi.org/10.1007/s10776-016-0327-y

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