Abstract

In disaster scenarios, establishing reliable communication infrastructure is critical, and unmanned aerial vehicle (UAV) swarms offer a promising solution as temporary base stations. This study models communication demand in disaster-affected areas by applying Gaussian kernels to building data, forming a spatial demand distribution. Signal strength is estimated using the normalized inverse Free Space Path Loss (FSPL) to account for realistic attenuation. To guide UAV placement, we extract high-demand regions from the demand distribution using a gradient-based thresholding method. Based on this information, we develop a greedy algorithm to iteratively position UAVs for optimal coverage in areas with the greatest communication need. Simulations of the Joplin tornado scenario demonstrate the algorithm's effectiveness in aligning signal strength with demand across various parameter settings. Sensitivity analysis reveals that the intensity percentile significantly influences UAV spatial distribution, while minimum distance and altitude have moderate and minimal impacts, respectively. These findings underscore the algorithm's robustness and adaptability, confirming its potential to enhance communication in disaster scenarios and support real-world emergency response efforts.

Department(s)

Mechanical and Aerospace Engineering

Publication Status

Full Access

Keywords and Phrases

Algorithm Performance; Convex Optimization; Frequency Division Multiple Access; Greedy Algorithm; Mid Air Collision; Satellite Imagery; Satellites; Tornadoes; Unmanned Aerial Vehicle; Wireless Communications

International Standard Book Number (ISBN)

978-162410738-2

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 American Institute of Aeronautics and Astrnautics, All rights reserved.

Publication Date

01 Jan 2025

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