In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supported by the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the network's lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, We propose a novel model centred around dynamic UAV-based fog deployment that optimizes the system's adaptability and operational efficacy within the afflicted areas. First, we decomposed the problem into two subproblems. Connectivity and coverage subproblem, and network lifespan optimization subproblem. We shape our UAV fog deployment problem as a uni-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. While the network lifespan optimization subproblem is efficiently solved via a one-dimensional swapping method. Following that, We introduce a novel optimization strategy for UAV fog node placement in dynamic networks during evacuation scenarios, with a primary focus on ensuring robust connectivity and maximal coverage for mobile users, while extending the network's lifespan. Finally, we introduce Adaptive Whale Optimization Algorithm (WOA) for fog node deployment in a dynamic network. Its agility, rapid convergence, and low computational demands make it an ideal fit for high-pressure environments.

翻译：在灾害场景和高风险救援行动中，将无人机整合为雾节点已成为关键。这种整合确保了受影响人群与支持物联网的重要健康监测设备之间的顺畅连接。在此类环境中整合无人机本质上具有挑战性，其主要目标是在通过节能策略延长网络寿命以服务最多受影响个体时，最大化网络连通性和覆盖范围。本文提出了一种基于动态无人机雾部署的新型模型，旨在优化系统在受灾区域内的适应性和运行效能。首先，我们将问题分解为两个子问题：连通性与覆盖子问题，以及网络寿命优化子问题。我们将无人机雾部署问题建模为单目标优化，并设计了一种专门针对救援任务中部署的无人机雾节点的专用算法。网络寿命优化子问题则通过一维交换方法高效求解。随后，我们提出了一种针对疏散情景下动态网络中无人机雾节点放置的新型优化策略，主要目标是在扩展网络寿命的同时，确保移动用户的鲁棒连通性和最大覆盖范围。最后，我们引入了自适应鲸鱼优化算法用于动态网络中的雾节点部署。其敏捷性、快速收敛性和低计算需求使其成为高压环境的理想选择。