TY - JOUR
T1 - Vehicular Fog Computing for Video Crowdsourcing
T2 - Applications, Feasibility, and Challenges
AU - Zhu, Chao
AU - Pastor, Giancarlo
AU - Xiao, Yu
AU - Ylajaaski, Antti
N1 - Publisher Copyright:
© 1979-2012 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85055315380&partnerID=8YFLogxK
U2 - 10.1109/MCOM.2018.1800116
DO - 10.1109/MCOM.2018.1800116
M3 - Article
AN - SCOPUS:85055315380
SN - 0163-6804
VL - 56
SP - 58
EP - 63
JO - IEEE Communications Magazine
JF - IEEE Communications Magazine
IS - 10
M1 - 8493119
ER -