Internet of Things (IoT) and unmanned aerial vehicles (UAVs) together can significantly enhance the performance of disaster management systems. UAVs can collect massive heterogeneous data from disaster-affected areas using fifth-generation (5G)/beyond 5G networks and this data can be analyzed to get the information required by the first responders such as marking the boundary of the affected area, identifying the infrastructure damaged and the roads blocked, and the health situation of people living in that area. This paper presents an overview of different platforms (UAVs-based, IoT-based, and IoT, coupled with UAVs) for disaster management. We propose an energy-efficient task scheduling scheme for data collection by UAVs from the ground IoT network. The focus is to optimize the path taken by the UAVs to minimize energy consumption. We also analyze the vital signs data collected by UAVs for people in disaster-affected areas and apply the decision tree classification algorithm to determine their health risk status. The risk status will enable the first responders to decide the areas which need the most immediate help Simulation results compare the effectiveness of our proposed scheduling scheme with the traditional approach used for data collection. Also, we present the results of our predicted risk status compared with the risk status calculated through the National Early Warning Score 2 (NEWS2) method.

物联网（IoT）和无人机（UAV）一起可以显着提高灾难管理系统的性能。无人机可以使用第五代（5G）/超越5G网络从受灾地区收集大量异构数据，并且可以对这些数据进行分析以获取急救人员所需的信息，例如标记受灾地区的边界，确定基础设施损坏，道路阻塞，以及该地区居民的健康状况。本文概述了用于灾难管理的不同平台（基于UAV，基于IoT和IoT，以及UAV）的概述。我们提出了一种高效的任务调度方案，用于无人机从地面物联网网络收集数据。重点是优化无人机所采用的路径，以最大程度地减少能耗。我们还分析了无人机收集的受灾地区人员的生命体征数据，并应用决策树分类算法来确定其健康风险状况。风险状态将使第一响应者能够确定需要最紧急帮助的区域。模拟结果将我们建议的计划方案的有效性与用于数据收集的传统方法进行了比较。此外，我们还提供了预测风险状态的结果与通过国家早期预警得分2（NEWS2）方法计算的风险状态相比较的结果。风险状态将使第一响应者能够确定需要最紧急帮助的区域。模拟结果将我们建议的计划方案的有效性与用于数据收集的传统方法进行了比较。此外，我们还提供了预测风险状态的结果与通过国家早期预警得分2（NEWS2）方法计算的风险状态相比较的结果。风险状态将使第一响应者能够确定需要最紧急帮助的区域。模拟结果将我们建议的计划方案的有效性与用于数据收集的传统方法进行了比较。此外，我们还提供了预测风险状态的结果与通过国家早期预警得分2（NEWS2）方法计算的风险状态相比较的结果。