Vehicular Fog Computing for Video Crowdsourcing: Applications, Feasibility, and Challenges

Chao Zhu, Giancarlo Pastor, Yu Xiao, Antti Ylajaaski
2018 IEEE Communications Magazine  
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 realtime analytic of crowdsourced dash camera video. Instead of forwarding all the
more » ... o 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. Firstly, we analyze the availability of vehicular fog nodes based on a real world traffic dataset. Secondly, 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 analytic of crowdsourced videos over vehicular networks.
doi:10.1109/mcom.2018.1800116 fatcat:rrrhyxih2rcizbcy6e5o4xpoj4