@inproceedings{472a5035e22c4868b36484d48d57579f,
title = "An Efficient Connected Swarm Deployment via Deep Learning",
abstract = "In this paper, an unmanned aerial vehicles (UAVs) deployment framework based on machine learning is studied. It aims to maximize the sum of the weights of the ground users covered by UAVs while UAVs forming a connected communication graph. We focus on the case where the number of UAVs is not necessarily enough to cover all ground users. We develop an UAV Deployment Deep Neural network (UD-DNNet) as a UAV's deployment deep network method. Simulation results demonstrate that UDDNNet can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and outperform the state-of-the-art traditional algorithms.",
author = "Kiril Danilchenko and Michael Segal",
note = "Publisher Copyright: {\textcopyright} 2021 Polish Information Processing Society.; 16th Conference on Computer Science and Intelligence Systems, FedCSIS 2021 ; Conference date: 02-09-2021 Through 05-09-2021",
year = "2021",
month = sep,
day = "2",
doi = "https://doi.org/10.15439/2021F001",
language = "American English",
series = "Proceedings of the 16th Conference on Computer Science and Intelligence Systems, FedCSIS 2021",
pages = "1--7",
editor = "Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik Slezak",
booktitle = "Proceedings of the 16th Conference on Computer Science and Intelligence Systems, FedCSIS 2021",
}