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Cost-Efficient Deployment Optimization for Multi-UAV-Assisted Vehicular Edge Computing Networks
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DOI：10.1109/JIOT.2024.3515107.png)
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Abstract
En 中文
Taking into account the flexible deployment and Line-of-Sight (LoS) communication links of uncrewed aerial vehicles (UAVs), this article proposes a multi-UAV-assisted vehicular edge computing networks (VECNs) architecture to provide instantaneous computation support at multiple congestion road segments. Given that the computation resources of a single UAV are insufficient, and offloading tasks directly to the cloud computing center (CCC) in intelligent transportation systems (ITSs) introduces significant latency, multiple UAVs with precached service or content caching data are deployed optimally for the vehicle users. In order to address the tradeoff between system costs and service efficiency, we propose a novel cost-efficient layered optimization scheme, in which the number and deployment positions of UAVs are jointly optimized. According to the varying vehicular network environments and the dynamic requirements of vehicle users, we design a hierarchical reinforcement learning algorithm, combining double deep Q network (DDQN) and multiagent deep deterministic policy gradient (MADDPG), the former is used to optimize the number of UAVs, and the deployment of UAVs are optimized via the MADDPG. Simulation results demonstrate the effectiveness of the proposed scheme in lowering total task completed latency and increasing the system profits. The service efficiency in dealing with the vehicle users' requirements also be improved.
Keywords:
Autonomous aerial vehicles
Optimization
Vehicle dynamics
Costs
Roads
Heuristic algorithms
Collaboration
Trajectory
Edge computing
Computational efficiency
Deployment
hierarchical reinforcement learning (HRL)
multiagent reinforcement learning
uncrewed aerial vehicle (UAV)
vehicular edge computing networks (VECNs)
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W