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.

在灾难场景和紧急救援行动中，将无人机作为雾节点集成已变得至关重要。该模型通过优化系统的适应性和操作效能，以动态无人机雾部署为核心，解决了连接性和覆盖性子问题，并通过一维交换方法有效解决了网络寿命优化问题。同时使用适应性鲸鱼优化算法 (WOA) 进行动态网络中的雾节点部署，以确保移动用户的稳定连接和最大覆盖范围，同时延长网络寿命。