Development of Analytical Offloading for Innovative Internet of Vehicles Based on Mobile Edge Computing

The current task offloading technique needs to be performed more effectively. Onboard terminals cannot execute efficient computation due to the explosive expansion of data flow, the quick increase in vehicle population, and the growing scarcity of spectrum resources. As a result, this study suggests a task-offloading technique based on reinforcement learning computing for the Internet of Vehicles edge computing architecture. The system framework for the Internet of Vehicles has been initially developed. Although the control centre gathers all vehicle information, the roadside unit collects vehicle data from the neighborhood and sends it to a mobile edge computing server for processing. Then, to guarantee that job dispatching in the Internet of Vehicles is logical, the computation model, communications approach, interfering approach, and concerns about confidentiality are established. This research examines the best way to analyze and design a computation offloading approach for a multiuser smart Internet of Vehicles (IoV) based on mobile edge computing (MEC). We present an analytical offloading strategy for various MEC networks, covering one-to-one, one-to-two, and two-to-one situations, as it is challenging to determine an analytical offloading proportion for a generic MEC-based IoV network. The suggested analytic offload strategy may match the brute force (BF) approach with the best performance of the Deep Deterministic Policy Gradient (DDPG). For the analytical offloading design for a general MEC-based IoV, the analytical results in this study can be a valuable source of information.