Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Bayesian Statistical Modeling of System Energy Saving Effectiveness for MAC Protocols of Wireless Sensor Networks: The Case of Non-Informative Prior Knowledge

  • Kim, Myong-Hee (Dept. of Information System, Pukyong National University) ;
  • Park, Man-Gon (Dept. of IT Convergence and Application Engineering, Pukyong National University)
  • Received : 2010.05.27
  • Accepted : 2010.07.07
  • Published : 2010.06.30
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Abstract

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

Keywords

References

  1. V. Bharghavan, A. Demers, S. Shenker, and L. Zhang, "MACAW: A Media Access Protocol for Wireless LAN's," Proceedings of the ACM SIGCOMM Conference on Communications Architectures, Protocols and Applications, London, UK, pp. 212-225, Sep. 1994.
  2. I. Demirkol, C. Ersoy, and F. Alag"oz, "MAC Protocols for Wireless Sensor Networks: A Survey," IEEE Communications Magazine, Vol.44, No.4, pp. 115-121, 2006.
  3. G. P. Halkes, Tijs van Dam, and K. G. Langendoen, "Comparing Energy-Saving MAC Protocols for Wireless Sensor Networks," Mobile Networks and Applications, Vol. 10, Issue 5, Kluwer Academic Publishers, Hingham, MA, USA, pp.783-791, 2005. https://doi.org/10.1007/s11036-005-3371-x
  4. Tijs van Dam and Koen Langendoen, "An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks," Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, Los Angeles, USA, pp. 171-180, 2003.
  5. J. Polastre and D. Culler, "B-MAC: An Adaptive CSMA Layer for Low-Power Operation," Technical Report CS294-F03/BMAC, UC Berkeley, Dec. 2003.
  6. Sagduyu and Y.E. Ephremides, A., "The Problem of Medium Access Control in Wireless Sensor Networks," IEEE Wireless Communications, Vol.1, No.6, pp. 44-53, Dec.2004.
  7. Adam Dunkels, Fredrik Osterlind, Nicolas Tsiftes, and Zhitao He, "Software-based Sensor Node Energy Estimation," Proceedings of 5th International Conference on Embedded Networked Sensor Systems, Sydney, Australia, pp.409-410, 2007.
  8. M. Stemm and R. H. Katz, "Measuring and Reducing Energy Consumption of Network Interfaces in Hand-held Devices," IEICE Transactions on Communications, E80-B, pp.1125-1131, 1997.
  9. Olivier Pourret, Patrick Nairn, and Bruce Marcot, Bayesian Networks: A Practical Guide to Applications, John Wiley & Sons Ltd, Chichester, West Sussex, England, May 2008.
  10. M. Neil, N. E. Fenton, and M. Tailor, "Using Bayesian Networks to Model Expected and Unexpected Operational Losses," An International Journal of Risk Analysis, Vol.25, No.4, pp. 963-972, 2005. https://doi.org/10.1111/j.1539-6924.2005.00641.x
  11. Murphy, K. P., "Dynamic Bayesian Networks: Representation, Inference and Learning," Ph.D. thesis, UC Berkeley, Computer Science Division, 2002.
  12. Fred Stann and John Heidemann, "BARD: Bayesian-Assisted Resource Discovery in Sensor Networks," Proceedings of the. 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2005), Miami, USA, Vol. 2, pp.866-877, March 2005.
  13. Harry F. Martz and Ray A Waller, "Bayesian Reliability Analysis of Complex Series/Parallel Systems of Binomial Subsystems and Components," Technometrics, Vol.32, No.4, pp.407-416, 1990. https://doi.org/10.2307/1270120
  14. Gutierrez, J.A., Naeve, M., Callaway, E., Bourgeois, M, Mitter, and V., Heile, B., "IEEE 802.15.4: A Developing Standard for LowPower Low-Cost Wireless Personal Area Networks," IEEE Network, Vol.15, No.5, pp.12-19, Sep, -Oct. 2001. https://doi.org/10.1109/65.953229
  15. IEEE 802.15 $WPAN^TM$ Task Group 4 (TG4), http://www.ieee802.org/15/pub/TG4.html
  16. Gerald H. Sandler, System Reliability Engineering, Prentice-Hall, Englewood Cliffs, 1963.
  17. Judea Pearl, "Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning," Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA, USA, PP. 329-334, Aug. 1985.
  18. M. H. Kim and M. G. Park, "Bayesian Statistical Modeling of System Energy Saving Effectiveness for MAC Protocols of Wireless Sensor Networks," Studies in Computational Intelligence, Vol. 209, Springer Berlin, pp. 233-245, 2009. https://doi.org/10.1007/978-3-642-01203-7_20
  19. Howard Raiffa and Robert Schlaifer, Applied Statistical Decision Theory, (First Edition, Harvard University Press, Cambridge, MA, USA, 1961), Paperback edition, John Wiley & Sons, Jun. 2000.
  20. Harold Jeffreys, Theory of Probability (Third Edition), Clarendon Press, Oxford, pp.24-25, 1961, Reprinted in 1998.
  21. Ronald A Fisher, "On the Mathematical Foundations of Theoretical Statistics," Philosophical Transactions of the Royal Society, Series A. Vol.222, pp.308-358, 1922.