Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations
<|reference_start|>Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations: 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.<|reference_end|>
@article{naouri2024maximizing,
    title={Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations},
    author={Abdenacer Naouri, Huansheng Ning, Nabil Abdelkader Nouri, Amar
  Khelloufi, Abdelkarim Ben Sada, Salim Naouri, Attia Qammar and Sahraoui
  Dhelim},
    journal={arXiv preprint arXiv:2402.16052},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16052},
    primaryClass={cs.NI cs.AI eess.SP}
}