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| Title | Qos-Aware Energy-Efficient Multi-Uav Offloading Ratio And Trajectory Control Algorithm In Mobile-Edge Computing |
|---|---|
| ID_Doc | 43825 |
| Authors | Yin J.; Tang Z.; Lou J.; Guo J.; Cai H.; Wu X.; Wang T.; Jia W. |
| Year | 2024 |
| Published | IEEE Internet of Things Journal, 11, 24 |
| DOI | http://dx.doi.org/10.1109/JIOT.2024.3452111 |
| Abstract | Multiple unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) leverages UAVs equipped with computational resources as mobile-edge servers, providing flexibility and low-latency connections, especially beneficial in smart cities and the Internet of Things (IoT). Maximizing Quality of Services (QoS) while minimizing energy consumption necessitates developing a suitable offloading ratio and trajectory control algorithm for UAVs. However, existing research on UAV control algorithms overlooks significant challenges like the heterogeneity of user equipments (UEs) and offloading failures. Furthermore, there is a dearth of experimental validation in large-scale UAV-assisted MEC scenarios. To bridge these gaps, we introduce a QoS-aware energy-efficient multi-UAV offloading ratio and trajectory control algorithm (QEMUOT). Specifically, 1) a composite UE mobility model is proposed to enhance system heterogeneous modeling, encompassing models for high-speed, low-speed, and fixed UEs; 2) QEMUOT is devised using multiagent reinforcement learning algorithms to determine offloading ratio and trajectory control decisions. To tackle sparse reward space and offloading failures, we employ expert demonstrations for pretraining and enhance reward mechanisms; and 3) experimental simulations illustrate that our algorithm outperforms baseline algorithms in user QoS with reduced energy consumption and demonstrates superior scalability in scenarios with numerous UAVs and UEs. © 2024 IEEE. |
| Author Keywords | Heterogeneous mobility pattern; mobile-edge computing (MEC); multiagent deep reinforcement learning; unmanned aerial vehicle (UAV) |
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