Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it is difficult to deploy sensors and adjust the sensor network in unknown region for dynamic disease surveillance. Second, the limited life-cycle of sensor network may cause the loss of surveillance data. Thus, it is important to provide a sustainable and robust regional disease surveillance system. Given a set of Disease surveillance Area (DsA) s and Point of disease Surveillance (PoS) s, some sensors are deployed to monitor these PoS s, and a drone collect data from the sensors as well as charge the sensors to extend their life-cycles. The drone replenish its energy by relying on the bus network. We first formulate the drone assisted regional disease surveillance problem under the constraints of life-cycle of sensors and energy of drone, and propose an approximation algorithm to find a feasible cycle of drone to minimize the traveling time cost of drone. To satisfy the diversity requirements and dynamic scalability of regional disease surveillance, we deploy one robot in each DsA instead of sensors. We further formulate the learning transferable driven regional disease surveillance problem, and propose a joint schedule algorithm of drone and robots. The results of both theoretical analysis and extensive simulations show that the proposed algorithms can reduce the total time cost by 39.71 and 48.74 percent, average waiting time by 42.00 and 50.14 percent, and increase the average accessing ratio of PoS s by 15.53 and 22.30 percent, through the assistance of bus network and learning transferable features.

中文翻译：

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学习可迁移驱动和无人机辅助智能医疗的可持续和稳健的区域疾病监测

智慧医疗已经在疾病监测、远程医疗等多个领域得到应用，但在区域疾病监测中，设备部署、数据采集和可染色性保证等方面存在一定的挑战。首先，难以在未知区域部署传感器和调整传感器网络以进行动态疾病监测。其次，传感器网络有限的生命周期可能导致监控数据的丢失。因此，提供一个可持续和强大的区域疾病监测系统非常重要。给定一组疾病监测区 (DsA) 沙 疾病点监测 (PoS) s，部署了一些传感器来监控这些 权益证明 s，无人机从传感器收集数据并为传感器充电以延长其生命周期。无人机依靠公交网络补充能量。我们首先在传感器的生命周期和无人机能量的约束下制定无人机辅助区域疾病监测问题，并提出一种近似算法来寻找无人机的可行周期，以最小化无人机的旅行时间成本。为了满足区域疾病监测的多样性要求和动态可扩展性，我们在每个区域部署了一个机器人脱氧核糖核酸而不是传感器。我们进一步制定了学习可迁移驱动的区域疾病监测问题，并提出了无人机和机器人的联合调度算法。理论分析和大量仿真结果表明，所提算法的总时间成本分别降低了 39.71% 和 48.74%，平均等待时间分别降低了 42.00% 和 50.14%，提高了平均访问率。权益证明 通过总线网络和学习可迁移特征的帮助，s 分别为 15.53% 和 22.30%。