Skip to main content
SpringerLink
Log in
Book cover

International Conference on Ad-Hoc Networks and Wireless

ADHOC-NOW 2017: Ad-hoc, Mobile, and Wireless Networks pp 255–261Cite as

Optimising Wireless Sensor Network Link Quality Through Power Control with Non-convex Utilities Using Game Theory

  • Conference paper
  • First Online:

  • 1042 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10517))

Abstract

The asymmetric and dynamic properties of the wireless channel required the derivation of a plethora of link quality metrics that assisted in the enhancement of wireless link reliability. Expected Transmission Count is a robust link quality metric used in most of the state-of-the-art works, due to its consideration of bidirectional links. Transmission power plays a key role to mitigate interference, which affects link quality. Power control problems, often exhibit non-convex behaviour and can not be solved using traditional methods. In this paper, we optimise transmission power taking onboard the Expected Transmission Count metric. We construct our game-theoretic model with pricing and show that it can reach Pareto-dominant equilibrium. Finally, we create a learning algorithm using logit dynamics and show that it guarantees probabilistic convergence of the joint action to the potential function maximisers.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Spyrou, E.D., Yang, S., Mitrakos, D.K.: Discrete strategy game-theoretic topology control in wireless sensor networks. In: Proceedings of the 6th International Conference on Sensor Networks, SENSORNETS, vol. 1, pp. 27–38 (2017)

    Google Scholar 

  2. De Couto, D.S., Aguayo, D., Bicket, J., Morris, R.: A high-throughput path metric for multi-hop wireless routing. Wirel. Netw. 11(4), 419–434 (2005)

    Article  Google Scholar 

  3. Draves, R., Padhye, J., Zill, B.: Comparison of routing metrics for static multi-hop wireless networks. ACM SIGCOMM Comput. Commun. Rev. 34(4), 133–144 (2004)

    Article  Google Scholar 

  4. Luo, Z.-Q., Zhang, S.: Dynamic spectrum management: complexity and duality. IEEE J. Sel. Top. Signal Process. 2(1), 57–73 (2008)

    Article  Google Scholar 

  5. Huang, J., Berry, R.A., Honig, M.L.: Distributed interference compensation for wireless networks. IEEE J. Sel. Areas Commun. 24(5), 1074–1084 (2006)

    Article  Google Scholar 

  6. Hande, P., Rangan, S., Chiang, M., Wu, X.: Distributed uplink power control for optimal sir assignment in cellular data networks. Netw. IEEE/ACM Trans. 16(6), 1420–1433 (2008)

    Article  Google Scholar 

  7. Alpcan, T., Başar, T., Srikant, R., Altman, E.: CDMA uplink power control as a noncooperative game. Wirel. Netw. 8(6), 659–670 (2002)

    Article  MATH  Google Scholar 

  8. Spyrou, E.D., Mitrakos, D.K.: Approximating nash equilibrium uniqueness of power control in practical WSNs, arXiv preprint arXiv:1512.05141 (2015)

  9. Monderer, D., Shapley, L.S.: Potential games. Games Econ. Behav. 14(1), 124–143 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  10. Qian, L.P., Zhang, Y.J., Chiang, M.: Globally optimal distributed power control for nonconcave utility maximization. In: Global Telecommunications Conference (GLOBECOM 2010), pp. 1–6. IEEE (2010)

    Google Scholar 

  11. Yang, L., Sagduyu, Y.E., Zhang, J., Li, J.H.: Distributed power control for ad-hoc communications via stochastic nonconvex utility optimization. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–5. IEEE (2011)

    Google Scholar 

  12. Zhou, S., Wu, X., Ying, L.: Distributed power control and coding-modulation adaptation in wireless networks using annealed gibbs sampling. In: INFOCOM, 2012 Proceedings IEEE, pp. 3016–3020. IEEE (2012)

    Google Scholar 

  13. Ru, G., Li, H., Tran, T., Lin, W., Liu, L., Wu, H.: Distributed optimal power control for multicarrier cognitive systems. In: Global Communications Conference (GLOBECOM), pp. 1132–1137. IEEE (2012)

    Google Scholar 

  14. Blume, L.E.: The statistical mechanics of strategic interaction. Games Econ. Behav. 5(3), 387–424 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  15. Lasaulce, S., Tembine, H.: Game Theory and Learning for Wireless Networks: Fundamentals and Applications. Academic Press, Waltham (2011)

    Google Scholar 

  16. Tatarenko, T.: Log-linear learning: convergence in discrete and continuous strategy potential games. In: 2014 IEEE 53rd Annual Conference on Decision and Control (CDC), pp. 426–432. IEEE (2014)

    Google Scholar 

  17. Dobrushin, R.L.: Central limit theorem for nonstationary markov chains. i. Theory Probab. Appl. 1(1), 65–80 (1956)

    Article  MathSciNet  Google Scholar 

  18. Isaacson, D.L., Madsen, R.W.: Markov Chains. Theory and Applications. Wiley, New York (1976)

    MATH  Google Scholar 

  19. Dorea, C.C., Cruz, J.A.R.: Approximation results for non-homogeneous Markov chains and some applications. Sankhyā: Indian J. Stat. 243–252 (2004)

    Google Scholar 

  20. Hajnal, J., Bartlett, M.: Weak ergodicity in non-homogeneous Markov chains. In: Mathematical Proceedings of the Cambridge Philosophical Society, vol. 54, no. 02, pp. 233–246. Cambridge Univ Press (1958)

    Google Scholar 

  21. Roberts, G.O., Rosenthal, J.S., et al.: General state space Markov chains and mcmc algorithms. Probab. Surv. 1, 20–71 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  22. Dobrushin, R.: Central limit theorem for nonstationary Markov chains. ii. Theory Probab. Appl. 1(4), 329–383 (1956)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Egnatia Odos, University Campus, 54124, Thessaloniki, Greece

    Evangelos D. Spyrou & Dimitrios K. Mitrakos

Authors
  1. Evangelos D. Spyrou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitrios K. Mitrakos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Evangelos D. Spyrou .

Editor information

Editors and Affiliations

  1. University of Messina, Messina, Italy

    Antonio Puliafito

  2. University of Messina, Messina, Italy

    Dario Bruneo

  3. University of Messina, Messina, Italy

    Salvatore Distefano

  4. Kazan Federal University, Kazan, Russia

    Francesco Longo

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Spyrou, E.D., Mitrakos, D.K. (2017). Optimising Wireless Sensor Network Link Quality Through Power Control with Non-convex Utilities Using Game Theory. In: Puliafito, A., Bruneo, D., Distefano, S., Longo, F. (eds) Ad-hoc, Mobile, and Wireless Networks. ADHOC-NOW 2017. Lecture Notes in Computer Science(), vol 10517. Springer, Cham. https://doi.org/10.1007/978-3-319-67910-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67910-5_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67909-9

  • Online ISBN: 978-3-319-67910-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation