Abstract
Vehicular edge computing (VEC) has emerged as a promising paradigm to ensure the real-time task processing caused by the emerging 5G or high level intelligent assisted driving applications. The computing tasks can be processed via the edge services deployed as the roadside units (RSUs) or moving vehicles. However, the high dynamic topology of the vehicular communication system and the time-varying available computing resources in RSUs make a challenge of the efficient task offloading of vehicles. In this paper, we consider an efficient task offloading scheme for VEC networks based on trajectory prediction, we focus on the serving handover between the adjacent RSUs. The moving vehicles can cooperate with RSUs or the surrounding vehicles for task processing. To reduce the latency of task transmission between vehicles, we present a cooperative vehicle selection method based on trajectory prediction. Then, we propose an efficient task offloading scheme based on deep reinforcement learning (DRL), while the dynamically available computing and communication resources are considered jointly. The simulation results show that the proposed task offloading scheme has great advantages in improving the utility of vehicles.
Highlights
INTRODUCTIONW ITH the rapid development of 5G wireless communication and intelligent transportation technologies, various emerging applications (such as autonomous driving, augmented reality, face recognition, etc.) appear in recent years [1]–[3]
W ITH the rapid development of 5G wireless communication and intelligent transportation technologies, various emerging applications appear in recent years [1]–[3]
To coper with the service delay caused by the long transmission distance and disorder task offloading schemes, mobile edge computing (MEC) has emerged as a promising approach, the delay and computing sensitive tasks can be processed at the proximate wireless access edge nodes [5], [6]
Summary
W ITH the rapid development of 5G wireless communication and intelligent transportation technologies, various emerging applications (such as autonomous driving, augmented reality, face recognition, etc.) appear in recent years [1]–[3]. In the VEC networks, deploying edge servers on both sides of the road or utilizing the available computing resources of surrounding vehicles, the task processing delay can be reduced efficiently [7] [8]. We propose an optimal task offloading scheme based on the vehicle trajectory prediction, the highly dynamic vehicular topology, vehicular tasks offloading targets and switching judgments of MEC servers deployed as the adjacent RSUs are considered jointly. We propose an optimal task offloading scheme while the both the varying V2V and V2R communication links and the varying available computing resources in RSUs and surrounding vehicles are considered. We propose a comprehensive vehicular task offloading scheme in VEC networks based on trajectory prediction, focusing on serving handover between RSUs and selection of cooperative vehicles.
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