oneVFC—A Vehicular Fog Computation Platform for Artificial Intelligence in Internet of Vehicles
Kieu-Ha Phung, Hieu Minh Tran, Thang Trung Nguyen, Hung Dao, Vinh Tran-Quang, Thu-Huong Truong, An Braeken, Kris Steenhaut
Abstract
We are witnessing the evolution from Internet of Things (IoT) to Internet of Vehicles (IoV). Internet connected vehicles can sense, communicate, analyze and make decisions. Rich vehicle-related data collection allows to apply artificial intelligence (AI) such as machine learning and deep learning (DL) to develop advanced services in Intelligent Transportation Systems (ITS). However, AI/DL-based ITS applications require intensive computation, both for model training and deployment. The exploitation of the huge computational power obtained through aggregation of resources present in individual vehicles and ITS infrastructure brings an efficient solution. In this work, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oneVFC</i> , a tangible vehicular fog computing (VFC) platform based on oneM2M is proposed. It benefits from the oneM2M standard to facilitate interoperability as well as hierarchical resource organization. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oneVFC</i> manages the distributed resources, orchestrates information flows and computing tasks on vehicle fog nodes and feeds back results to the application users. On a lab scale model consisting of Raspberry Pi modules and laptops, we demonstrate how <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oneVFC</i> manages the AI-driven applications running on various machines and how it succeeds in significantly reducing application processing time, especially in cases with high workload or with requests arriving at high pace. We also show how <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oneVFC</i> facilitates the deployment of AI model training in Federated Learning (FL), an advanced privacy preserving and communication saving training approach. Our experiments deployed in an outdoor environment with mobile fog nodes participating in the computation jobs confirm the feasibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oneVFC</i> for IoV environments whenever the communication links among fog nodes are guaranteed by V2X technology.