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Abstract

The concept of energy efficiency has moved in and out of favor with the public over the years, but recently has gained renewed broad-based support. The confluence of economic, environmental and fairness concerns around offering the same quality of service by reducing energy has moved efficiency to the fore. In the context of networking and communications, there are different energy-efficiency issues in terms of quality of service, quality of experience, energy consumption, pricing etc. This chapter will focus primarily on the game theoretical formulations of energy-efficiency metrics, with applications to networking problems.We will first present a broadly inclusive notions of energy-efficiency and then explore a variety of ways to analyze the strategic behaviors of the players depending on the information and the dynamics of the system. Applications to power management with stochastic battery state modelling and network selection problems with energy-efficiency criteria are presented.

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References

  1. Bush, R., Mosteller, F.: Stochastic Models of Learning. John Wiliey & Son, New York (1955)

    Google Scholar 

  2. Robbins, H., Monro, S.: A Stochastic Approximation Method. Annals of Mathematical Statistics 22(3), 400–407 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  3. Kiefer, J., Wolfowitz, J.: Stochastic Estimation of the Maximum of a Regression Function. Annals of Mathematical Statistics 23(3), 462–466 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  4. Hayel, Y., Tembine, H., Altman, E., El-Azouzi, R.: Markov Decision Evolutionary Games with Individual Energy Management. In: Breton, M., Szajowski, K. (eds.) Annals of the International Society of Dynamic Games, Advances in Dynamic Games: Theory, Applications, and Numerical Methods, pp. 313–335 (2010)

    Google Scholar 

  5. Tembine, H.: Dynamic Robust Games in MIMO systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics (forthcoming 2011)

    Google Scholar 

  6. Tembine, H., Altman, E., ElAzouzi, R., Hayel, Y.: Evolutionary Games in Wireless Networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(3), 634–646 (2010)

    Article  Google Scholar 

  7. Tembine, H.: Distributed strategic learning for wireless engineers, notes, 250 pages, supelec (2010)

    Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Khan, M.A., Tembine, H. (2011). Energy-Efficiency Networking Games. In: Kim, J.H., Lee, M.J. (eds) Green IT: Technologies and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22179-8_11

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  • DOI: https://doi.org/10.1007/978-3-642-22179-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22178-1

  • Online ISBN: 978-3-642-22179-8

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