Advertisement

Demand aware transmission power cost optimization based on game theory and distributed learning algorithm for wireless body area network

  • Yutao Zhang
  • Yongxing JiaEmail author
  • Xiaobo Zhang
Article
  • 9 Downloads

Abstract

This paper studied the issue of communication transmission power cost optimization for the personal body base station in wireless body area network (WBAN). With the limited energy capacity, the energy cost for the data exchange between the personal body base station and the medical surveillance network should be controlled. At the same time, the users’ demand should also be guaranteed. The transmission AI choosing distributed optimization model is established, by taking the transmission power consumption as the optimization goal. In this model, the user’s data transmission requirements, wireless environment, location of the AI, and other users ‘choices are comprehensively analyzed. To achieve the optimal users’ AI choosing result for the transmission power cost optimization, the AI choosing game model for transmission power cost of WBAN is constructed, and the game is proved to be an accurate potential game. A transmission power cost optimization AI choosing distributed decision-making algorithm is designed, and the convergence of the algorithm is proved. Experiment analysis verifies the theoretical analysis of the proposed game model and learning algorithm, and show that the proposed algorithm can effectively optimize the AI choosing results of the WBAN to reduce the energy cost.

Keywords

Medical surveillance network Wireless body area network Power cost Potential game Demand Aware 

Notes

References

  1. 1.
    Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., & Wang, J. (2014). Cognitive internet of things: a new paradigm beyond connection. IEEE Internet of Things Journal, 1(2), 129–143.CrossRefGoogle Scholar
  2. 2.
    Ju, C. H., & Shao, Q. (2015). Energy efficiency oriented access point selection for cognitive sensors in internet of things. International Journal of Distributed Sensor Networks, 11, 619546.Google Scholar
  3. 3.
    Nicola, Magnavita. (2018). Medical surveillance, continuous health promotion and a participatory intervention in a small company. International Journal of Environmental Research & Public Health, 15(4), 62.  https://doi.org/10.3390/ijerph15040662.Google Scholar
  4. 4.
    Gao, G., Hu, B., Wang, S., & Yang, C. (2018). Wearable circular ring slot antenna with EBG structure for wireless body area network. IEEE Antennas and Wireless Propagation Letters, 17(3), 434–437.CrossRefGoogle Scholar
  5. 5.
    Yuce, M. R. (2010). Implementation of wireless body area networks for healthcare systems. Body Users & Actuators A Physical, 162(1), 116–129.CrossRefGoogle Scholar
  6. 6.
    Samanta, A., & Misra, S. (2018). Energy-efficient and distributed network management cost minimization in opportunistic wireless body area networks. IEEE Transactions on Mobile Computing, 99, 1.Google Scholar
  7. 7.
    Lee, J., & Kim, S. (2018). Emergency-prioritized asymmetric protocol for improving QoS of energy-constraint wearable device in wireless body area networks. Applied Sciences, 8(1), 92.CrossRefGoogle Scholar
  8. 8.
    Zhao, Z., Huang, S., & Cai, J. (2018). An analytical framework for IEEE 802.15.6 based wireless body area networks with instantaneous delay constraints and shadowing interruptions. IEEE Transactions on Vehicular Technology, 67(7), 6355–6369.CrossRefGoogle Scholar
  9. 9.
    Shen, J., Gui, Z., Ji, S., Shen, J., & Tan, H. (2018). Cloud-aided lightweight certificateless authentication protocol with anonymity for wireless body area networks. Journal of Network & Computer Applications, 106, 117–123.CrossRefGoogle Scholar
  10. 10.
    Samanta, Amit, & Misra, Sudip. (2017). EReM: energy-efficient resource management in body area networks with fault tolerance. IEEE GLOBECOM.  https://doi.org/10.1109/GLOCOM.2017.8255012.Google Scholar
  11. 11.
    Sangwan, A., & Bhattacharya, P. P. (2018). Delay tolerant energy efficient protocol for inter-BAN communication in mobile body area networks. Int J Adv Sci Eng Inf Technol, 8(3), 938–948.  https://doi.org/10.18517/ijaseit.8.3.4502.CrossRefGoogle Scholar
  12. 12.
    Wu, T. Y., Li, G. H., Huang, S. W., et al. (2012). A GA-based mobile RFID localization scheme for internet of things. Personal and Ubiquitous Computing, 16(3), 245–258.CrossRefGoogle Scholar
  13. 13.
    Monderer, D., & Shapley, L. (1996). Potential games. Games and Economic Behavior, 14, 124–143.MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Marden, J., Arslan, G., & Shamma, J. (2009). Cooperative control and potential games. IEEE Trans Syst Man Cybern B, 39(6), 1393–1407.CrossRefGoogle Scholar
  15. 15.
    K. Apt and T. Radzik,”Stable partitions in coalitional games,” arXiv:cs/0605132v1 [cs.GT], May 2006.
  16. 16.
    Zhong, W., Xu, Y., & Tianfield, H. (2011). Game-theoretic opportunistic spectrum sharing strategy selection for cognitive MIMO multiple access channels. IEEE Transactions on Signal Processing, 59(6), 2745–2759.MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Young, H. P. (1998). Individual strategy and social structure. Princeton, NJ: Princeton Univ. Press.Google Scholar
  18. 18.
    Raychaudhuri, D., & Mandayam, N. B. (2012). Frontiers of wireless and mobile communications. Proceedings of the IEEE, 100(4), 824–840.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Economics and ManagementNanjing University of Science and TechnologyNanjingChina
  2. 2.Post-Doctoral Research CenterNanjing General HospitalNanjingChina
  3. 3.Army Engineering University of PLANanjingChina

Personalised recommendations