Fuzzy Deep Q-learning Task Offloading in Delay Constrained Vehicular Fog Computing

In the age of the ever-growing number of tasks being generated from IoT devices, one of the most crucial problems with enhancing the Quality of Service in multi-access computing is the system's limited resources. To this end, Vehicular Fog Computing (VFC) has emerged as a potential solution that utilizes the idle resources of vehicles to reduce the load imposed on the edge servers. In this paper, we leverage the advantages of both deep reinforcement learning and Fuzzy logic to propose Fuzzy Deep Q-learning base Offloading scheme (FDQO), a real-time offloading scheme in delay constrained VFC. Our objective is to maximize the Quality of Experiences (QoE), which indicates how the task meets its delay constraint. The experiment results show that our proposed approach significantly outperforms the existing algorithms. Specifically, FDQO improves the average QoE by 37.72% compared to using only Deep Q-learning, 7.47% compared to using only Fuzzy logic, and 19% compared to the ∊ -greedy strategy for multi-armed bandits.