An Efficient Connected Swarm Deployment via Deep Learning

Kiril Danilchenko, Michael Segal

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalAnnals of Computer Science and Information Systems
StatePublished - 2 Sep 2021
Event16th Conference on Computer Science and Intelligence Systems, FedCSIS 2021 - Virtual, Sofia, Bulgaria
Duration: 2 Sep 20215 Sep 2021

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization


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