Projects per year
Abstract
With the growing adoption of dash cameras, we are seeing great potential for innovations by analyzing the video collected from vehicles. On the other hand, transmitting and analyzing a large amount of video, especially high-resolution video in real time, requires a lot of communications and computing resources. In this work, we investigate the feasibility and challenges of applying vehicular fog computing for real- time analytics of crowdsourced dash camera video. Instead of forwarding all the video to the cloud, we propose to turn commercial fleets (e.g., buses and taxis) into vehicular fog nodes, and to utilize these nodes to gather and process the video from the vehicles within communication ranges. We assess the feasibility of our proposal in two steps. First, we analyze the availability of vehicular fog nodes based on a real-world traffic dataset. Second, we explore the serviceability of vehicular fog nodes by evaluating the networking performance of fog-enabled video crowdsourcing over two mainstream access technologies, DSRC and LTE. Based on our findings, we also summarize the challenges to largescale real-time analytics of crowdsourced videos over vehicular networks.
Original language | English |
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Article number | 8493119 |
Pages (from-to) | 58-63 |
Number of pages | 6 |
Journal | IEEE Communications Magazine |
Volume | 56 |
Issue number | 10 |
DOIs | |
Publication status | Published - 1 Oct 2018 |
MoE publication type | A1 Journal article-refereed |
Fingerprint Dive into the research topics of 'Vehicular Fog Computing for Video Crowdsourcing: Applications, Feasibility, and Challenges'. Together they form a unique fingerprint.
Projects
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5G meets II/Ylä-Jääski
Ylä-Jääski, A., Gazi Karam Illahi, G., Hanhirova, J. & Laattala, M.
01/01/2017 → 31/12/2019
Project: Domestic funds and foundations
Press / Media
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Task Allocation and Resource Scheduling in Vehicular Fog Computing
Quan Zhou, Yu Xiao, Chao Zhu & Jin Tao
04/12/2020
1 item of Media coverage
Press/Media: Media appearance