HQA: Hybrid Q-learning and AODV multi-path routing algorithm for Flying Ad-hoc Networks
Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attenti...
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| Vydané v: | Vehicular Communications Ročník 55; s. 100947 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.10.2025
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| ISSN: | 2214-2096 |
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| Abstract | Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attention, face two significant challenges: 1) insufficient stability of Q-learning leads to unreliable route selection in certain scenarios and higher packet loss rates; 2) in void regions with frequent topology changes and vast path exploration spaces, the slow convergence of Q-learning fails to adapt quickly to dynamic environmental changes, thereby reducing the packet delivery rate (PDR). This paper proposes a hybrid Q-learning/AODV (HQA) multi-path routing algorithm that integrates Q-learning and the AODV protocols to address these challenges. HQA includes a Bayesian stability evaluator for adaptive Q-learning/AODV switching and a dual-update reward mechanism that integrates reliable AODV paths into Q-learning training, enabling rapid void recovery and latency-optimized routing. Experimental results demonstrate HQA's superiority over baseline protocols: Compared to AODV, HQA reduces average end-to-end delay by 13.6–23.9% and improves PDR by 5.4–9.1% in non-void and void states, respectively. It outperforms QMR by 2.2–6.3% in PDR while achieving 25.6% and 53.2% higher average PDR than QMR and AODV across network densities. The hybrid design accelerates convergence by 40% versus standalone Q-learning through AODV-assisted rewards, maintaining scalability under dynamic topology changes. These findings indicate that the HQA algorithm can more rapidly adapt to the rapid changes in FANETs and better handle void regions, offering a promising solution for enhancing the performance and reliability of FANETs. |
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| AbstractList | Reliable and efficient data transmission between Unmanned Aerial Vehicle (UAV) nodes is critical for the control of UAV swarms and relies heavily on effective routing protocols in Flying Ad-hoc Networks (FANETs). However, Q-learning-based FANET routing protocols, which are gaining widespread attention, face two significant challenges: 1) insufficient stability of Q-learning leads to unreliable route selection in certain scenarios and higher packet loss rates; 2) in void regions with frequent topology changes and vast path exploration spaces, the slow convergence of Q-learning fails to adapt quickly to dynamic environmental changes, thereby reducing the packet delivery rate (PDR). This paper proposes a hybrid Q-learning/AODV (HQA) multi-path routing algorithm that integrates Q-learning and the AODV protocols to address these challenges. HQA includes a Bayesian stability evaluator for adaptive Q-learning/AODV switching and a dual-update reward mechanism that integrates reliable AODV paths into Q-learning training, enabling rapid void recovery and latency-optimized routing. Experimental results demonstrate HQA's superiority over baseline protocols: Compared to AODV, HQA reduces average end-to-end delay by 13.6–23.9% and improves PDR by 5.4–9.1% in non-void and void states, respectively. It outperforms QMR by 2.2–6.3% in PDR while achieving 25.6% and 53.2% higher average PDR than QMR and AODV across network densities. The hybrid design accelerates convergence by 40% versus standalone Q-learning through AODV-assisted rewards, maintaining scalability under dynamic topology changes. These findings indicate that the HQA algorithm can more rapidly adapt to the rapid changes in FANETs and better handle void regions, offering a promising solution for enhancing the performance and reliability of FANETs. |
| ArticleNumber | 100947 |
| Author | Sun, Chen Hou, Liang Yu, Suqi Shu, Jian |
| Author_xml | – sequence: 1 givenname: Chen orcidid: 0000-0002-6783-9631 surname: Sun fullname: Sun, Chen email: sunchen@nchu.edu.cn – sequence: 2 givenname: Liang surname: Hou fullname: Hou, Liang – sequence: 3 givenname: Suqi surname: Yu fullname: Yu, Suqi – sequence: 4 givenname: Jian surname: Shu fullname: Shu, Jian |
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| Cites_doi | 10.1016/j.comnet.2024.110514 10.1109/ACCESS.2023.3244067 10.1016/j.comnet.2021.108379 10.3390/electronics11071099 10.1109/JIOT.2022.3162849 10.1162/NECO_a_00059 10.3390/sym14091787 10.1016/j.comnet.2022.109382 10.1109/MCOM.2017.1700323 10.1007/s11276-023-03534-y 10.23919/JCC.2022.05.005 10.1016/j.jksuci.2024.102066 10.1109/TVT.2021.3074015 10.1016/j.comcom.2019.11.011 10.3390/app12073665 10.1016/j.protcy.2012.05.118 10.1016/j.jksuci.2022.03.021 10.1016/j.icte.2023.07.002 10.1016/j.net.2022.03.015 10.1109/TITS.2022.3145857 10.1016/j.applthermaleng.2024.124209 10.1109/TNSE.2021.3085514 10.1109/JIOT.2021.3089759 10.1016/j.jksuci.2023.101894 10.1631/FITEE.1900401 10.1016/j.jksuci.2023.101817 10.3390/su14158980 10.1016/j.cose.2024.103909 |
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| Copyright | 2025 Elsevier Inc. |
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| Keywords | Flying Ad-hoc Networks (FANETs) Q-learning Routing protocol Ad-hoc on-demand distance vector (AODV) |
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