QL-AODV: Q-Learning-Enhanced Multi-Path Routing Protocol for 6G-Enabled Autonomous Aerial Vehicle Networks

Abdelhamied A. Ateya, Nguyen Duc Tu, Ammar Muthanna, Andrey Koucheryavy, Dmitry Kozyrev and János Sztrik ()
Additional contact information
Abdelhamied A. Ateya: EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Nguyen Duc Tu: Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia
Ammar Muthanna: Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia
Andrey Koucheryavy: Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia
Dmitry Kozyrev: Department of Probability Theory and Cybersecurity, Peoples’ Friendship University of Russia Named After Patrice Lumumba (RUDN University), 117198 Moscow, Russia
János Sztrik: Department of Informatics Systems and Networks, Faculty of Informatics, University of Debrecen, Egyetem ter 1, 4032 Debrecen, Hungary

Future Internet, 2025, vol. 17, issue 10, 1-23

Abstract: With the arrival of sixth-generation (6G) wireless systems comes radical potential for the deployment of autonomous aerial vehicle (AAV) swarms in mission-critical applications, ranging from disaster rescue to intelligent transportation. However, 6G-supporting AAV environments present challenges such as dynamic three-dimensional topologies, highly restrictive energy constraints, and extremely low latency demands, which substantially degrade the efficiency of conventional routing protocols. To this end, this work presents a Q-learning-enhanced ad hoc on-demand distance vector (QL-AODV). This intelligent routing protocol uses reinforcement learning within the AODV protocol to support adaptive, data-driven route selection in highly dynamic aerial networks. QL-AODV offers four novelties, including a multipath route set collection methodology that retains up to ten candidate routes for each destination using an extended route reply (RREP) waiting mechanism, a more detailed RREP message format with cumulative node buffer usage, enabling informed decision-making, a normalized 3D state space model recording hop count, average buffer occupancy, and peak buffer saturation, optimized to adhere to aerial network dynamics, and a light-weighted distributed Q-learning approach at the source node that uses an ε-greedy policy to balance exploration and exploitation. Large-scale simulations conducted with NS-3.34 for various node densities and mobility conditions confirm the better performance of QL-AODV compared to conventional AODV. In high-mobility environments, QL-AODV offers up to 9.8% improvement in packet delivery ratio and up to 12.1% increase in throughput, while remaining persistently scalable for various network sizes. The results prove that QL-AODV is a reliable, scalable, and intelligent routing method for next-generation AAV networks that will operate in intensive environments that are expected for 6G.

Keywords: 6G; Q-learning; autonomous aerial vehicle; swarm drones; ad hoc on-demand distance vector; routing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/10/473/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/10/473/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:10:p:473-:d:1773196

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().