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Related Experiment Video

Updated: Nov 21, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network.

Jordi Mongay Batalla1, Constandinos X Mavromoustakis2, George Mastorakis3

  • 1Department of Telecommunications, Warsaw University of Technology, 00-661 Warsaw, Poland.

Sensors (Basel, Switzerland)
|January 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning algorithm for selecting optimal data forwarding paths in unmanned aerial vehicles (UAVs) communicating via mobile networks. The two-phase approach ensures reliable connectivity for diverse UAV applications.

Keywords:
UAVmachine learningoptimizationrouting and forwardingtwo-phase selection algorithms

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Aerospace Engineering

Background:

  • Unmanned Aerial Vehicles (UAVs) require robust communication links for control and data transmission.
  • Commercial mobile networks offer a viable, cost-effective solution for UAV connectivity.
  • Ensuring reliable data forwarding is critical for UAV mission success, especially in complex scenarios like drone swarms.

Purpose of the Study:

  • To propose a novel two-phase algorithm for multi-criteria packet forwarding selection in UAVs.
  • To enhance UAV communication reliability by leveraging commercial mobile networks.
  • To develop a machine learning-based approach for predicting link behavior during flight.

Main Methods:

  • A two-phase algorithm was designed for independent yet constrained data forwarding link selection.
  • Machine Learning (ML) models were trained to predict mobile network link performance.
  • Implementation involved utilizing three different mobile network operators for redundancy.

Main Results:

  • The proposed algorithm effectively manages multi-criteria packet forwarding selection.
  • Trained ML models accurately predict link connectivity behavior throughout UAV flight.
  • The system demonstrates validated performance in ensuring reliable UAV communication.

Conclusions:

  • The developed two-phase algorithm offers an effective solution for UAV packet forwarding.
  • Machine learning significantly enhances the prediction of mobile network link stability for UAVs.
  • This approach provides a flexible and reliable connectivity framework for various UAV applications, including swarms.