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Symmetric Game for Distributed Estimation in Energy Harvesting Wireless Sensor Networks with Selfish Sensors

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9532))

Abstract

The power control problem for distributed estimation in energy-harvesting wireless sensor networks (EH-WSNs) poses a unique set of problems with the uncertain availability of resources. The fusion center (FC) usually is hard to predict the energy state of sensors that use ambient energy harvesting. Concerning the problem that existing decentralized power control schemes are not suitable in EH-WSHs, we propose a novel distributed symmetric game, which takes both energy harvesting and the characteristics of distributed estimation into account. And, each sensor makes decisions autonomously and is treated fairly. Then, some conclusions of Nash equilibriums (NEs) are introduced, and that can explain the trend observed in simulations. Finally, numerical results validate the effectiveness of the proposed symmetric game.

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References

  1. Akyildiz, I., Su, W., Sankarsubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2012)

    Article  Google Scholar 

  2. Alkhweldi, M.: Optimal observations transmission for distributed estimation under energy constraint. In: IEEE Symposium on Computational Intelligence for Communication Systems and Networks, pp. 1–6 (2014)

    Google Scholar 

  3. Guibas, L.J.: Sensing, tracking and reasoning with relations. IEEE Signal Process. Mag. 19, 73–85 (2002)

    Article  Google Scholar 

  4. Ribeiro, A., Giannakis, G.: Bandwidth-constrained distributed estimation for wireless sensor networks-Part I: gaussian case. IEEE Trans. Signal Process. 54, 1131–1143 (2006)

    Article  Google Scholar 

  5. Kumar, S., Zhao, F., Shepherd, D.: Collaborative signal and information processing in microsensor networks. IEEE Signal Process. Mag. 19, 13–14 (2002)

    Article  Google Scholar 

  6. Li, H., Fang, J.: Distributed adaptive quantization and estimation for wireless sensor networks. IEEE Signal Process. Lett. 14, 669–672 (2007)

    Article  Google Scholar 

  7. Liu, G., Xu, B., Chen, H.: Decentralized estimation over noisy channels in cluster-based wireless sensor networks. Int. J. Commun. Syst. 25, 1313–1329 (2012)

    Article  Google Scholar 

  8. Chen, H.: Performance-energy tradeoffs for decentralized estimation in a multihop sensor network. IEEE Sens. J. 10, 1304–1310 (2010)

    Article  Google Scholar 

  9. Xiao, J., Cui, S., Luo, Z., Goldsmith, A.: Power scheduling of universal decentralized estimation in sensor networks. IEEE Trans. Signal Process. 54, 413–422 (2006)

    Article  Google Scholar 

  10. Liu, G., Xu, B.: Energy-efficient scheduling of distributed estimation with convolutional coding and rate-compatible punctured convolutional coding. IET Commun. 5, 1650–1660 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  11. Tentzeris, M., Georgiadis, A., Roselli, L.: Energy harvesting and scavenging. Proc. IEEE 102, 1644–1648 (2014)

    Article  Google Scholar 

  12. Roseveare, N., Natarajan, B.: An alternative perspective on utility maximization in energy-harvesting wireless sensor networks. IEEE Trans. Veh. Technol. 63, 344–356 (2014)

    Article  Google Scholar 

  13. Hong Y.P.: Distributed estimation with analog forwarding in energy-harvesting wireless sensor networks. In: IEEE International Conference on Communication Systems, pp. 142–146 (2014)

    Google Scholar 

  14. Liu, H., Liu, G., Liu, Y., Mo, L., Chen, H.: Adaptive quantization for distributed estimation in energy-harvesting wireless sensor networks: a game-theoretic approach. Int. J. Distrib. Sens. Netw. 2014, 1–9 (2014)

    Google Scholar 

  15. Liu, G., Xu, B., Chen, H., Zhang, C., Xiang, J., Zhou, C.: Adaptive quantization for distributed estimation in cluster-based wireless sensor networks. AEU - Int. J. Electron. Commun. 68, 484–488 (2014)

    Article  Google Scholar 

  16. Nourian, M., Dey, S., Ahlen, A.: Distortion minimization in multi-sensor estimation with energy harvesting. IEEE J. Sel. Areas Commun. 33, 524–539 (2015). doi:10.1109/JSAC.2015.2391691

    Article  Google Scholar 

  17. Drew, F., Jean, T.: Game Theory. MIT Press, Cambridge (1991)

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation, China (61403089, 61162008, 61002012, 61471176), Natural Science Foundation of Guangdong Province, China (S2013010016297), Foundation for Distinguished Young Talents in Higher Education of Guangdong, China (2013LYM\(_{-}\)0068), Program for Guangzhou Municipal Colleges and Universities (1201431034), Guangdong Science & Technology Project (2013B0104) and Guangzhou Education Bureau Science and Technology Project (2012A082).

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Correspondence to Guiyun Liu .

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© 2015 Springer International Publishing Switzerland

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Liu, G., Yao, J., Chen, H., Zhang, H., Tang, D. (2015). Symmetric Game for Distributed Estimation in Energy Harvesting Wireless Sensor Networks with Selfish Sensors. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9532. Springer, Cham. https://doi.org/10.1007/978-3-319-27161-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-27161-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27160-6

  • Online ISBN: 978-3-319-27161-3

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