A Collaborative Computation and Offloading for Compute-Intensive and Latency-Sensitive Dependency-Aware Tasks in Dew-Enabled Vehicular Fog Computing: A Federated Deep Q-Learning Approach

Abstract

The demand for vehicular networks is prolifically emerging as it supports advancing in capabilities and qualities of vehicle services. However, this vehicular network cannot solely carry out latency-sensitive and compute-intensive tasks, as the slightest delay may cause any catastrophe. Therefore, fog computing can be a viable solution as an integration to address the aforementioned challenges. Moreover, it complements Cloud computing as it reduces the incurred latency and ingress traffic by shifting the computing resources to the edge of the network. This work investigated task offloading methods in Vehicular Fog Computing (VFC) networks and proposes a Federated learning-supported Deep Q-Learning-based (FedDQL) technique for optimal offloading of tasks in a collaborative computing paradigm. The proposed offloading method in the VFC network performs computations, communications, offloading, and resource utilization considering the latency and energy consumption. The trade-offs between latency and computing and communication constraints were considered in this scenario. The FedDQL scheme was validated for dependent task sets to analyze the efficacy of this method. Finally, the results of extensive simulations provide evidence that the proposed method outperforms others with an average improvement of 49%, 34.3%, 29.2%, 16.2% and 8.21%, respectively.