## 2 Related Works
Moreover, the authors proposed formulating the task-offloading problem as a mixed-integer nonlinear programming (MINLP) optimization problem that takes into account parameters such as UAV association, task offloading, transmission power, resource allocation, and UAV location. The simulation results demonstrated the effectiveness of the proposed model. [PERSON] et al. [21] evaluated the Whale Optimization Algorithm (WOA) for UAV fog node deployment in critical rescue operations and compared it against other optimization techniques, such as PSO, Harris Hawks Optimization (HHO), the Sine Cosine Algorithm (SCA), the Slime Mold Algorithm (SMA), and accelerated particle swarm optimization (APSO). The experimental results demonstrated that the WOA algorithm outperformed other algorithms in terms of connectivity, coverage, stability, and efficiency. In [22], [PERSON] et al. introduced the Hybrid Multi-Strategy Artificial Rabbits Optimization (HARO) algorithm to enhance UAV path planning in complex environments such as urban and mountainous areas. Conventional algorithms often face challenges like high computational costs and the tendency to become trapped in local optima. HARO overcomes these limitations by employing a dual exploration switching strategy and a population migration memory mechanism to balance exploration and exploitation. Additionally, it incorporates spherical and cylindrical obstacle models for realistic simulations and utilizes a key-point retention strategy to eliminate redundant waypoints, thereby reducing flight costs. The experimental results demonstrated that HARO improves search performance, enabling more efficient and stable UAV navigation. Table 1 presents a comparative analysis of various studies on IoD fog computing based on various criteria, including the nature of fog nodes, objectives, addressed problems, optimization techniques, and application domains, which are described as follows:  
* Nature of fog nodes: This criterion represents the task-offloading strategy of the proposed IoD fog computing platform, which may involve stationary fog nodes, UAV-based fog nodes, or a combination of both. * Objectives: Each study focuses on different objectives, such as minimizing real-time latency, optimizing energy consumption, improving system utility, and enhancing service availability. * Addressed problems: These include task offloading, resource allocation, UAV deployment, connectivity, and coverage, among others. * Optimization techniques: Various optimization methods can be employed, including particle swarm optimization (PSO), mixed-integer linear programming (MILP), game-theoretic approaches, convex optimization, genetic algorithms (GAs), dynamic programming, convolutional neural networks (CNNs), mixed-integer nonlinear programming (MINLP), and stochastic routing networks (SRNs). * Application domains: Some studies focus on general fog computing functions such as processing, storage, and networking, while others target specific applications like post-disaster rescue, disaster management, object recognition, and critical rescue operations. The table shows that existing studies rely on either stationary fog nodes or UAV-based fog nodes. However, our proposed approach uniquely integrates stationary and mobile fog nodes, leveraging their combined advantages. Moreover, the table highlights that real-time latency optimization and task offloading are the most critical challenges and objectives in IoD fog computing. Therefore, our work focuses on addressing these challenges. Furthermore, in our work, we employ a GA instead of the metaheuristic approaches used in other IoD fog computing studies, as prior research has demonstrated the superior effectiveness of GAs. Adopting a hybrid fog computing architecture enhances processing, storage, and energy capacity, making it well suited for GA-based optimization. \begin{table}
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