The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

中文翻译：

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移动边缘计算支持医疗物联网的 5G 健康监测：一种去中心化的博弈论方法

医疗物联网 (IoMT) 的迅速发展促进了无处不在的家庭健康监测网络。然而，患者的过度需求导致频谱资源不足和通信过载。支持移动边缘计算 (MEC) 的 5G 健康监测被认为是解决此类障碍的有利范例。在本文中，我们通过将 IoMT 划分为两个子网络，即内部无线体域网 (WBAN) 和外部 WBAN，为 IoMT 构建了一个具有成本效益的家庭健康监测系统。突出 IoMT 的特点，患者的成本取决于医疗危急程度、信息时代 (AoI) 和能源消耗。对于 WBAN 内，制定合作博弈来分配无线信道资源。而对于超宽带网络，考虑到个体的理性和潜在的自私，提出了一种去中心化的非合作博弈，以最小化 IoMT 中的系统成本。我们证明了所提出的算法可以达到纳什均衡。此外，从理论上推导出算法时间复杂度和受益于MEC的患者数量的上限。性能评估证明了我们提出的算法在全系统成本和受益于 MEC 的患者数量方面的有效性。