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Differentiated service strategies for ad-hoc wireless sensor networks in harsh communication environments

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Abstract

In some wireless sensor network applications, sensor nodes will be deployed in harsh communication environments. In such environments, the deployment may not be adequately controlled, and nodes may have to communicate with a single destination node. For nodes to alert the destination on critical data that has been sensed, in addition to the harsh communication environment, contention resulting from both the deployment and network density must be appropriately overcome. In this paper, we create theoretical models for the behavior of Timeout-MAC (T-MAC) protocol, and evaluate five possible solutions, each designed to be easy to implement on a device by simply tuning T-MAC parameters, so as to overcome these environment-specific issues and effectively alert the destination to critical data. Our results indicate that slight changes to the behavior of the network can improve the awareness of the destination to critical regions in the environment, and that these changes have different levels of effectiveness at different network densities.

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References

  1. Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., & Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM international workshop on wireless sensor networks and applications, September 28, 2002, Atlanta, GA, USA. doi:10.1145/570738.570751.

  2. Siuli Roy, A. D., & Bandyopadhyay, S. (2008). Agro-sense: Precision agriculture using sensor-based wireless mess networks. Innovations in NGN: Future network and services, 2008. K-INGN 2008. First ITU-T Kaleidoscope Academic Conference. doi:10.1109/KINGN.2008.4542291.

  3. Hefeeda, M., & Bagheri, M. (2007). Wireless sensor networks for early detection of forest fires. In Proceedings of international workshop on mobile ad hoc and sensor systems for global and homeland security (MASS-GHS’07), in conjunction with IEEE MASS’07 (pp. 2376–2380).

  4. Yan, T., He, T., & Stankovic, J. A. (2003). Differentiated surveillance for sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems, November 05–07, 2003, Los Angeles, CA, USA. doi:10.1145/958491.958498.

  5. Jiang, S. (1998). Wireless communications and a priority access protocol for multiple mobile terminals in factory automation. IEEE Transactions on Robotics and Automation, 14(1), 137–143.

    Article  Google Scholar 

  6. Pantelopoulos, A., & Bourbakis, N. G. (2010). A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(1), 1–12.

    Article  Google Scholar 

  7. Alcaraz, C., & Lopez, J. (2010). A security analysis for wireless sensor mesh networks in highly critical systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(4), 419–428.

    Article  Google Scholar 

  8. Pottie, G. J., & Kaiser, W. J. (2000). Wireless integrated network sensors. Communications of the ACM, 43(5), 51–58.

    Article  Google Scholar 

  9. Goudarzi, P. (2009). Rate allocation with minimized packet-loss in multi-hop wireless ad hoc networks. Transactions D: Computer Science & Engineering and Electrical Engineering, Sharif University of Technology, 16(1), 65–73.

    MATH  Google Scholar 

  10. Buttyán, L., & Hubaux, J.-P. (2002). Stimulating cooperation in self-organizing mobile ad hoc networks. ACM Journal for Mobile Networks (MONET), Special Issue on Mobile Ad Hoc Networks, 8(5), 579–592.

    Google Scholar 

  11. Pedram, M. (1996). Power minimization in IC design: principles and applications. ACM Transactions on Design Automation of Electronic Systems (TODAES), 1(1), 3–56.

    Article  Google Scholar 

  12. Sudarsan, S., Subramanian, V., Yoshigoe, K., Ramaswamy, S., Seker, R., & Lenin, R. B. (2009). Impact of duty cycle variation on WSNs. In Proceedings of the 3rd international conference on new technologies, mobility and security (pp. 369–373).

  13. van Dam, T., & Langendoen, K. (2003). An adaptive energy-efficient MAC protocol for wireless sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems, November 05–07, 2003, Los Angeles, CA, USA. doi:10.1145/958491.958512.

  14. Polastre, J., Hill, J., & Culler, D. (2004). Versatile low power media access for wireless sensor networks. In Proceedings of the 2nd international conference on embedded networked sensor systems, November 03–05, 2004, Baltimore, MD, USA. doi:10.1145/1031495.1031508.

  15. Ansari, J., Riihijarvi, J., Mahonen, P., & Haapola, J. (2007). Implementation and performance evaluation of nanoMAC: A low-power MAC solution for high density wireless sensor networks. International Journal of Sensors, 2(5), 341–349.

    Article  Google Scholar 

  16. Rhee, I., Warrier, A., Aia, M., Min, J., & Sichitiu, M. L. (2008). Z-MAC: A hybrid MAC for wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 16(3), 511–524.

    Article  Google Scholar 

  17. Koubaa, A., Alves, M., Nefzi, B., & Song, Y.-Q. (2006). Improving the IEEE 802.15.4 Slotted CSMA/CA MAC for time-critical events in wireless sensor networks. In Proceedings workshop of real-time networks (RTN 2006), satellite workshop to (ECRTS 2006).

  18. Demirkol, I., Ersoy, C., & Alagöz, F. (2005). MAC protocols for wireless sensor networks: A survey. IEEE Communications Magazine, 44(4), 115–121.

    Article  Google Scholar 

  19. Ye, W., Heidemann, J., & Estrin, D. (2004). Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 11(3), 493–506.

    Article  Google Scholar 

  20. Guo, S., Gu, Y., Jiang, B., & He, T. (2009). Opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links. In Proceedings of the 15th annual international conference on mobile computing and networking (pp. 133–144).

  21. Li, Y. J., Chen, C. S., Song, Y.-Q., & Wang, Z. (2007). Real-time QoS support in wireless sensor networks: a survey. In Proceedings of the 7th IFAC international conference on fieldbuses & networks in industrial & embedded systems (FeT’07), Toulouse, France.

  22. Wu, Z. (2007). Energy-efficient nodes-cooperation transmission in unreliable wireless sensor networks. Wireless Communications, Networking and Mobile Computing. doi:10.1109/WICOM.2007.637.

  23. Woo, A., Tong, T., & Culler, D. (2003). Taming the underlying challenges of reliable multihop routing in sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (pp. 14–27).

  24. Boulis, A. (2009). Castalia—A simulator for wireless sensor networks and body area networks, version 2.3 user’s manual. http://castalia.npc.nicta.com.au/pdfs/Castalia%20-%20User%20Manual.pdf. Accessed 12 July 2010.

  25. National Information and Communications Technology Australia. (2004). Castalia Home. http://castalia.npc.nicta.com.au/. Accessed 12 July 2010.

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Acknowledgments

The authors would like to thank anonymous reviewers for detailed comments and suggestions on improving this work. This work is based in part, upon research supported by the National Science Foundation (Grant Nos. CNS-0619069, CNS-0855248, EPS-0701890, and OISE 0729792). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Jesse D. Thomason

  2. Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR, 72204, USA

    Kenji Yoshigoe

  3. Department of Mathematics, University of Central Arkansas, Conway, AR, 72035, USA

    R. B. Lenin

  4. College of Engineering, Missouri University of Science and Technology, Rolla, MO, 65409, USA

    James M. Bridges

  5. Industrial Software Systems, ABB Corporate Research, Bangalore, 560048, India

    Srini Ramaswamy

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  1. Jesse D. Thomason
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  2. Kenji Yoshigoe
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  3. R. B. Lenin
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  4. James M. Bridges
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  5. Srini Ramaswamy
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Correspondence to Kenji Yoshigoe.

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Thomason, J.D., Yoshigoe, K., Lenin, R.B. et al. Differentiated service strategies for ad-hoc wireless sensor networks in harsh communication environments. Wireless Netw 18, 551–564 (2012). https://doi.org/10.1007/s11276-012-0418-3

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  • DOI: https://doi.org/10.1007/s11276-012-0418-3

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