# Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations

arXiv: https://arxiv.org/abs/2402.16052 · HTML: https://mlsanity.com/paper/2402.16052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review](https://mlsanity.com/paper/2201.09130/) (2022)
- [UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2008.00218/) (2020)
- [Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis](https://mlsanity.com/paper/1509.01655/) (2015)
- [Planning UAV Activities for Efficient User Coverage in Disaster Areas](https://mlsanity.com/paper/1811.12450/) (2018)
- [Blockchain Application on the Internet of Vehicles (IoV)](https://mlsanity.com/paper/2205.03832/) (2022)
- [Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of Elevated Body Temperature](https://mlsanity.com/paper/2308.07193/) (2023)
- [Efficient Fog Node Placement using Nature-Inspired Metaheuristic for IoT Applications](https://mlsanity.com/paper/2302.05948/) (2023)
- [Context-Aware Service Recommendation System for the Social Internet of Things](https://mlsanity.com/paper/2308.08499/) (2023)
- [IP-UNet: Intensity Projection UNet Architecture for 3D Medical Volume Segmentation](https://mlsanity.com/paper/2308.12761/) (2023)
- [VBCA: A Virtual Forces Clustering Algorithm for Autonomous Aerial Drone Systems](https://mlsanity.com/paper/1607.05048/) (2016)
- [Coverage Maximization for a Poisson Field of Drone Cells](https://mlsanity.com/paper/1708.06598/) (2017)
- [Energy Harvesting in Unmanned Aerial Vehicle Networks with 3D Antenna Radiation Patterns](https://mlsanity.com/paper/2007.08108/) (2020)

## Abstract (arXiv)

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.