Jin Yong, Qian Zhenjiang, Gong Shengrong, Yang Weiyong
IEEE/ACM Trans Comput Biol Bioinform. 2021 Jan-Feb;18(1):114-125. doi: 10.1109/TCBB.2020.3017041. Epub 2021 Feb 3.
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 PoSs, 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 PoSs by 15.53 and 22.30 percent, through the assistance of bus network and learning transferable features.
智能医疗已应用于疾病监测和远程医疗等多个领域。然而，在区域疾病监测中，设备部署、数据收集和可持续性保障方面存在一些挑战。首先，在未知区域进行动态疾病监测时，难以部署传感器并调整传感器网络。其次，传感器网络有限的生命周期可能导致监测数据丢失。因此，提供一个可持续且强大的区域疾病监测系统很重要。给定一组疾病监测区域（DsA）和疾病监测点（PoS），部署一些传感器来监测这些PoS，并且有一架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。我们首先在传感器生命周期和无人机能量的约束下，对无人机辅助的区域疾病监测问题进行建模，并提出一种近似算法来找到无人机的可行循环，以最小化无人机的飞行时间成本。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DsA中部署一个机器人而不是传感器。我们进一步对学习可迁移驱动的区域疾病监测问题进行建模，并提出一种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，通过公交网络和学习可迁移特征的辅助，所提出的算法可以分别将总时间成本降低39.71%和48.74%，平均等待时间降低42.00%和50.14%，并将PoS的平均访问率提高15.53%和22.30%。
