SOLVING DYNAMIC TASK OFFLOADING IN VEHICULAR FOG COMPUTING USING PARTIALLY OBSERVABLE MARKOV DECISION PROCESS BASED DEEP DETERMINISTIC POLICY GRADIENT APPROACH

Vehicular Fog Computing (VFC) becomes the potential solution for mitigating the vehicular computation load. In graded Vehicular Fog Computing (VFC), vehicles function as mobile fog nodes at network edge, delivering consistent as well as minimum-latency services. Due to limited onboard computational resources, vehicles offload intensive tasks to nearby Roadside Units (RSU). To minimize a computational weight at RSUs, VFC is utilized for the computational-exhaustive tasks. Within this framework, vehicles serve as part of the infrastructure, facilitating communication, monitoring, and resource sharing among fog nodes. This makes efficient resource allocation a critical factor for overall system performance. Thus, this research proposes the Partially Observable Markov Decision Process-assisted Deep Deterministic Policy Gradient (POMDP-DDPG) approach for an effective task offloading in VFC. The proposed approach is a Reinforcement Learning (RL) that utilises the global data to identify a better connection between the RSU and the fog servers. The experimental results specify that the proposed POMDP-DDPG approach attains better results as compared to the existing approaches