Department of Science and Technology, Nanjing Medical University, Nanjing, Jiangsu, China.
School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, Jiangsu, China.
J Healthc Eng. 2021 Apr 9;2021:5552350. doi: 10.1155/2021/5552350. eCollection 2021.
It is important to monitor the early screening of chronic diseases, predict the risk, and provide the comprehensive management of chronic diseases for the elderly. However, it is difficult to provide the robust and real-time emergency service for elderly chronic disease because of the complex social network and diversity of elderly chronic disease service. To address these issues, we design a new drone assisted robust emergency service system. We formulate the Drone assisted Management (DM) problem to minimize the total time cost of drone subject to all elderly chronic disease services which can be guaranteed exactly once by the drone under its energy constraint. Then, we propose the DRS algorithm to solve the DM problem. To provide the robust and real-time service, we further formulate the Charging driven Drone assisted Management (CDM) problem and present the CDRS algorithm to solve the CDM problem. Through the theoretical analysis and numerical simulation experiments, we demonstrate that DRS and CDRS can decrease the total time cost by 37.61% and increase the QoE by 112.80% through the designed system, respectively.
监测老年人慢性病的早期筛查、预测风险并为其提供慢性病的综合管理非常重要。然而，由于老年人慢性病服务的社会网络复杂且多样性，为老年人慢性病提供强大且实时的紧急服务具有一定难度。针对这些问题，我们设计了一种新的无人机辅助稳健应急服务系统。我们制定了无人机辅助管理（DM）问题，以在满足所有老年人慢性病服务需求的前提下，最小化无人机的总时间成本，同时无人机在其能量约束下能够保证恰好一次服务。然后，我们提出了 DRS 算法来解决 DM 问题。为了提供稳健且实时的服务，我们进一步制定了充电驱动的无人机辅助管理（CDM）问题，并提出了 CDRS 算法来解决 CDM 问题。通过理论分析和数值模拟实验，我们证明了 DRS 和 CDRS 可以通过设计的系统分别将总时间成本降低 37.61%和将 QoE 提高 112.80%。
