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Backpressure-Aware Collaborative Learning Empowered Data Flow Backhaul for Aerial-Ground Integrated Networks
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Author(s): Liwei Ren (State Grid Zhejiang Electric Power Company, Ltd. Lishui Power Supply Company, Lishui, China), Yan Song (State Grid Zhejiang Electric Power Company, Ltd. Lishui Power Supply Company, Lishui, China), Shuhan Shi (State Grid Zhejiang Electric Power Company, Ltd. Lishui Power Supply Company, Lishui, China), Chang Jiang (State Grid Zhejiang Electric Power Company, Ltd. Lishui Power Supply Company, Lishui, China), Binjie Ying (State Grid Zhejiang Electric Power Company, Ltd. Lishui Power Supply Company, Lishui, China), Jinchao Fan (North China Electric Power University, China), Ziyi Zhou (North China Electric Power University, China), Yiming Chen (North China Electric Power University, China)and Bin Liao (North China Electric Power University, China)
Copyright: 2026
Volume: 17
Issue: 1
Pages: 20
Source title:
International Journal of Mobile Computing and Multimedia Communications (IJMCMC)
Editor(s)-in-Chief: Agustinus Waluyo (Monash University, Australia)
DOI: 10.4018/IJMCMC.406090
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Abstract
To address the critical challenge of ensuring reliable disaster data backhaul under frequent natural disasters and fragile ground communication infrastructures, a backpressure-aware collaborative learning empowered data flow backhaul framework for aerial-ground integrated networks (AGIN) is proposed. First, a data flow backhaul architecture is established by leveraging cooperative networking between unmanned aerial vehicles (UAVs) and ground facilities. Second, a QoS guarantee mechanism based on queue priorities dynamically coordinates resources to ensure low-latency backhaul for high-priority services. Thirdly, a data flow backhaul optimization approach based on backpressure-aware collaborative learning incorporates queue backlog differences into the reward function to prevent accumulation and adds a global conflict penalty to facilitate multi-UAV coordination and mitigate next-hop selection conflicts. Simulation results indicate that the proposed approach enables low-latency, energy-efficient, and high-throughput data flow backhaul under dynamic networks.
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