Recently, the Internet of Medical Things (IoMT) could offload healthcare services to 5G edge computing for low latency. However, some existing works assumed altruistic patients will sacrifice quality of service for the global optimum. For priority-aware and deadline-sensitive healthcare, this sufficient and simplified assumption will undermine the engagement enthusiasm, i.e., unfairness. To address this issue, we propose a long-term proportional fairness-driven 5G edge healthcare, i.e., FairHealth. First, we establish a long-term Nash bargaining game to model the service offloading, considering the stochastic demand and dynamic environment. We then design a Lyapunov-based proportional-fairness resource scheduling algorithm, which decouples the long-term fairness problem into single-slot subproblems, realizing a tradeoff between service stability and fairness. Moreover, we propose a block-coordinate descent method to iteratively solve nonconvex fair subproblems. Simulation results show that our scheme can improve 74.44% of the fairness index (i.e., Nash product), compared with the classic global time-optimal scheme.

中文翻译：

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FairHealth：医疗物联网中长期比例公平驱动的 5G 边缘医疗


最近，医疗物联网 (IoMT) 可以将医疗保健服务转移到 5G 边缘计算以实现低延迟。然而，一些现有的工作假设利他的患者会为了全局最优而牺牲服务质量。对于优先意识和截止日期敏感的医疗保健来说，这种充分且简化的假设将削弱参与热情，即不公平。为了解决这个问题，我们提出了一种长期比例公平驱动的5G边缘医疗保健，即FairHealth。首先，考虑随机需求和动态环境，我们建立长期纳什讨价还价博弈来模拟服务卸载。然后，我们设计了一种基于李亚普诺夫的比例公平资源调度算法，将长期公平问题解耦为单时隙子问题​​，实现了服务稳定性和公平性之间的权衡。此外，我们提出了一种块坐标下降方法来迭代解决非凸公平子问题。仿真结果表明，与经典的全局时间最优方案相比，我们的方案可以提高74.44%的公平性指数（即纳什乘积）。