The recent COVID-19 pandemic has brought significant challenges to the traditional healthcare industry. A novel eHealth system is required to cope with infectious and chronic diseases. This article proposes an online eHealth system to monitor patients and provides edge computing services based on the Internet of Unmanned Aerial Vehicles (UAVs). In order to minimize the health monitoring latency and guarantee the resource utilization efficiency of UAVs, the Lyapunov optimization method is utilized to decompose the long-term optimization problem into a series of instantaneous optimization problems. Then the decomposed sub-problem is formulated as a minimum cost maximum flow problem by constructing a bipartite graph that maps medical analysis requests to target UAVs. Extensive experimental results demonstrate the effectiveness of our system. Finally, system analyses illustrate that the robustness and scalability of our eHealth system is favorable, and our system can provide agile edge computing services for dispersed patients.

中文翻译：

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基于无人机互联网的远程健康监测：在线电子医疗系统


最近的COVID-19大流行给传统医疗保健行业带来了重大挑战。需要一种新型的电子卫生系统来应对传染病和慢性病。本文提出了一种在线电子医疗系统来监控患者并提供基于无人机（UAV）互联网的边缘计算服务。为了最小化健康监测延迟并保证无人机的资源利用效率，利用Lyapunov优化方法将长期优化问题分解为一系列瞬时优化问题。然后，通过构建将医学分析请求映射到目标无人机的二分图，将分解的子问题公式化为最小成本最大流量问题。大量的实验结果证明了我们系统的有效性。最后，系统分析表明，我们的电子医疗系统具有良好的鲁棒性和可扩展性，我们的系统可以为分散的患者提供敏捷的边缘计算服务。