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Related Experiment Video

Updated: Dec 6, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Q-LBR: Q-learning Based Load Balancing Routing for UAV-assisted VANET.

Bong-Soo Roh1,2, Myoung-Hun Han1, Jae-Hyun Ham1

  • 1Agency for Defense Development, Daejeon 34186, Korea.

Sensors (Basel, Switzerland)
|October 8, 2020
PubMed
Summary

This study introduces Q-learning based load balancing routing (Q-LBR) for vehicular ad hoc networks. Q-LBR effectively manages network load and improves performance, addressing traffic congestion in dynamic environments.

Keywords:
MANETQ learningUAV relayVANETload balancingrouting

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Vehicular ad hoc networks (VANETs) face challenges with increasing traffic and dynamic environments.
  • Existing unmanned aerial vehicle (UAV)-assisted routing protocols often neglect load balancing, leading to potential bottlenecks.
  • UAV relay nodes (URNs) offer extended coverage but can become congestion points.

Purpose of the Study:

  • To develop an effective load balancing routing algorithm for UAV-assisted VANETs.
  • To address traffic congestion and improve network performance in complex, dynamic scenarios.
  • To enhance the efficiency and reliability of data transmission in future vehicular networks.

Main Methods:

  • Proposed Q-learning based load balancing routing (Q-LBR).
  • Implemented a low-overhead technique for estimating network load using queue status from ground nodes via URN.
  • Utilized a Q-learning scheme with a reward control function for rapid convergence.

Main Results:

  • Q-LBR demonstrated significant improvements in packet delivery ratio (over 8%).
  • Network utilization was enhanced by more than 28% compared to existing protocols.
  • Latency was reduced by over 30% through effective load management.

Conclusions:

  • Q-LBR effectively balances network load in UAV-assisted VANETs.
  • The proposed method enhances key performance metrics like packet delivery ratio, network utilization, and latency.
  • Q-LBR offers a viable solution for managing traffic congestion and improving network efficiency.