Intelligent Task Offloading in Vehicular Networks: A Deep Reinforcement Learning Perspective
Namory Fofana, Asma Ben Letaïfa, Abderrezak Rachedi
Abstract
The proliferation of connected vehicles and the Internet of Things (IoT) has made access to high-quality services increasingly viable. However, the growing number of vehicular applications poses challenges for embedded systems that need to perform tasks efficiently despite network fluctuations. To solve this problem, we have developed a Vehicular Edge Computing (VEC) system with a task offloading algorithm adapted to the Internet of Vehicles (IoV). Our solution uses a four-stage Stackelberg game and a reinforcement learning model. The approach consists of analyzing the vehicle state in real time to determine computational requirements and cost functions, implementing communication methods and cost functions, and using multi-agent reinforcement learning to design an experience-based offloading strategy. Through simulations, our algorithm demonstrates a balanced optimization of time, expense, work vehicle utility and service vehicle utility, while improving the probability of task success under various constraints.