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, oneVFC , 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. oneVFC 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 oneVFC 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 oneVFC 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 oneVFC for IoV environments whenever the communication links among fog nodes are guaranteed by V2X technology.
Highlights
The number of vehicles used worldwide is expected to rise from one billion in 2010 to two billion in 2030
- Artificial Intelligence (AI)/deep learning (DL)-based model exploitation: object detection application based on Convolutional Neural Networks (CNN) model
The used CNN model is m-AlexNet model [35]. mAlexNet is a compact version of AlexNet [36] which is an early well-known DL model for object detection and object recognition purposes. mAlexNet has fewer convolutional layers and fewer parameters than AlexNet to trade off accuracy against computation cost
Summary
The number of vehicles used worldwide is expected to rise from one billion in 2010 to two billion in 2030. Internet of Vehicles (IoV), a network allowing data and information exchange among vehicles, things such as roadside infrastructure, humans, and the environment is becoming a reality thanks to Vehicle to Everything (V2X) technology which is based on two pillars being 5G-LTE and Dedicated Short-Range Communications (DSRC) [3]. Those intelligent vehicles and networks give rise. Advanced ITS services are Artificial Intelligence (AI)-based applications whose efficiency is enhanced by rich data collection in the IoV environment [4]. ITS data are collected from other sources, like ITS infrastructures such as loop detectors, infra-red sensors, ultrasonic sensors, and closed-circuit television (CCTV) cameras, travelers (who use web browsers, mobile apps, social networks)
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