Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETs
Routing in flying ad hoc networks (FANETs) is hindered by high mobility, trajectory-induced topology dynamics, and energy constraints. Conventional topology-based or position-based protocols often fail due to stale link information and limited neighbor awareness. This paper proposes a trajectory-inf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/7/489 |
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| author | Mingwei Wu Bo Jiang Siji Chen Hong Xu Tao Pang Mingke Gao Fei Xia |
| author_facet | Mingwei Wu Bo Jiang Siji Chen Hong Xu Tao Pang Mingke Gao Fei Xia |
| author_sort | Mingwei Wu |
| collection | DOAJ |
| description | Routing in flying ad hoc networks (FANETs) is hindered by high mobility, trajectory-induced topology dynamics, and energy constraints. Conventional topology-based or position-based protocols often fail due to stale link information and limited neighbor awareness. This paper proposes a trajectory-informed routing protocol enhanced by Q-learning: Traj-Q-GPSR, tailored for mission-oriented UAV swarm networks. By leveraging mission-planned flight trajectories, the protocol builds time-aware two-hop neighbor tables, enabling routing decisions based on both current connectivity and predicted link availability. This spatiotemporal information is integrated into a reinforcement learning framework that dynamically optimizes next-hop selection based on link stability, queue length, and node mobility patterns. To further enhance adaptability, the learning parameters are adjusted in real time according to network dynamics. Additionally, a delay-aware queuing model is introduced to forecast optimal transmission timing, thereby reducing buffering overhead and mitigating redundant retransmissions. Extensive ns-3 simulations across diverse mobility, density, and CBR connections demonstrate that the proposed protocol consistently outperforms GPSR, achieving up to 23% lower packet loss, over 80% reduction in average end-to-end delay, and improvements of up to 37% and 52% in throughput and routing efficiency, respectively. |
| format | Article |
| id | doaj-art-0b0f1b93dd6049efa36d05af1cdba17f |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-0b0f1b93dd6049efa36d05af1cdba17f2025-08-20T03:58:27ZengMDPI AGDrones2504-446X2025-07-019748910.3390/drones9070489Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETsMingwei Wu0Bo Jiang1Siji Chen2Hong Xu3Tao Pang4Mingke Gao5Fei Xia6East-China Research Institute of Computer Technology, Shanghai 201800, ChinaEast-China Research Institute of Computer Technology, Shanghai 201800, ChinaEast-China Research Institute of Computer Technology, Shanghai 201800, ChinaEast-China Research Institute of Computer Technology, Shanghai 201800, ChinaEast-China Research Institute of Computer Technology, Shanghai 201800, ChinaEast-China Research Institute of Computer Technology, Shanghai 201800, ChinaEast-China Research Institute of Computer Technology, Shanghai 201800, ChinaRouting in flying ad hoc networks (FANETs) is hindered by high mobility, trajectory-induced topology dynamics, and energy constraints. Conventional topology-based or position-based protocols often fail due to stale link information and limited neighbor awareness. This paper proposes a trajectory-informed routing protocol enhanced by Q-learning: Traj-Q-GPSR, tailored for mission-oriented UAV swarm networks. By leveraging mission-planned flight trajectories, the protocol builds time-aware two-hop neighbor tables, enabling routing decisions based on both current connectivity and predicted link availability. This spatiotemporal information is integrated into a reinforcement learning framework that dynamically optimizes next-hop selection based on link stability, queue length, and node mobility patterns. To further enhance adaptability, the learning parameters are adjusted in real time according to network dynamics. Additionally, a delay-aware queuing model is introduced to forecast optimal transmission timing, thereby reducing buffering overhead and mitigating redundant retransmissions. Extensive ns-3 simulations across diverse mobility, density, and CBR connections demonstrate that the proposed protocol consistently outperforms GPSR, achieving up to 23% lower packet loss, over 80% reduction in average end-to-end delay, and improvements of up to 37% and 52% in throughput and routing efficiency, respectively.https://www.mdpi.com/2504-446X/9/7/489FANETrouting protocoltrajectory knowledgeQ-learning |
| spellingShingle | Mingwei Wu Bo Jiang Siji Chen Hong Xu Tao Pang Mingke Gao Fei Xia Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETs Drones FANET routing protocol trajectory knowledge Q-learning |
| title | Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETs |
| title_full | Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETs |
| title_fullStr | Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETs |
| title_full_unstemmed | Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETs |
| title_short | Traj-Q-GPSR: A Trajectory-Informed and Q-Learning Enhanced GPSR Protocol for Mission-Oriented FANETs |
| title_sort | traj q gpsr a trajectory informed and q learning enhanced gpsr protocol for mission oriented fanets |
| topic | FANET routing protocol trajectory knowledge Q-learning |
| url | https://www.mdpi.com/2504-446X/9/7/489 |
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