Cost-Efficient Deployment Optimization for Multi-UAV-Assisted Vehicular Edge Computing Networks

doi:10.1109/JIOT.2024.3515107

Cost-Efficient Deployment Optimization for Multi-UAV-Assisted Vehicular Edge Computing Networks

2024-01-01

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Xie Shengli (Shengli Xie)

DOI：10.1109/JIOT.2024.3515107

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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.

Keyword:

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)

Cost-Efficient Deployment Optimization for Multi-UAV-Assisted Vehicular Edge Computing Networks

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