Abstract
Edge healthcare system is recognized as an acceptable paradigm for resolving this problem. The IoMT is divided into two sub-networks - intraWBANs and beyond-WBANs - based on the physical bonds of WBANs. Given the features of the healthcare systems, medical emergency, AoI and power depreciation are the prices of MUs. Intra-WBANs, a cooperative game shapes the wireless channel resource allocation problem. The Nash negotiation solution is used to get the unique optimum point in Pareto. MUs are regarded reasonable and perhaps egoistic in non-WBANs. Another non-cooperative activity is therefore developed to reduce overall system costs. The assessments of the performance of the system-wide cost and of the number of MUs gaining from edge computer systems are done to illustrate the success of our solution. Finally, for further effort, numerous barriers to research and open questions are highlighted.
Reference27 articles.
1. Alam, H. Malik, M. I. Khan, T. Pardy, A. Kuusik, and Y. Le Moullec, ‘‘A survey on the roles of communication technologies in IoT-based personalized healthcare applications,’’ IEEE Access, vol. 6, pp. 36611– 36631, 2018.
2. Astrin, IEEE Standard for Local and Metropolitan Area Networks Part 15.6: Wireless Body Area Networks, IEEE Standard 802.15. 6, 2012.
3. Chen, W. Li, Y. Hao, Y. Qian, and I. Humar, ‘‘Edge cognitive computing based smart healthcare system,’’ Future Gener. Comput. Syst., vol. 86, pp. 403–411, Sep. 2018.
4. Dai, I. Spasic, B. Meyer, S. Chapman, and F. Andres, ‘‘Machine learning on mobile: An on-device inference app for skin cancer detection,’’ in Proc. 4th Int. Conf. Fog Mobile Edge Comput. (FMEC), Jun. 2019, pp. 301–305.
5. Feng et al., “Optimal Haptic Communications over Nanonetworks for E-Health Systems,” IEEE Trans. Industrial Informatics, vol. 15, no. 5, 2019, pp. 3016–27.