Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare

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.