TY - GEN
T1 - Evaluating mobile signal and location predictability along public transportation routes
AU - Abou-Zeid, Hatem
AU - Hassanein, Hossam S.
AU - Tanveer, Zohaib
AU - Abuali, Najah
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/17
Y1 - 2015/6/17
N2 - Emerging mobility-aware content delivery approaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is to forecast the user locations and associated link capacity, and then proactively counter service fluctuations in advance. For instance, a user that is heading towards low coverage can be prioritized and have video content prebuffered. While the reported gains are encouraging, the results are primarily based on assumptions of perfect prediction. Investigating the predictability of mobility and future signal variations is therefore imperative to evaluate the practical viability of such predictive content delivery paradigms. To this end, this paper presents a large-scale measurement study of 33 repeated trips along a 23.4 km bus route covering urban and sub-urban areas in Kingston, Canada. We provide a thorough analysis of the collected traces to investigate the effects of geographical area, time, forecasting window, and contextual factors such as signal lights and bus stops. The collected dataset can also be used in several other ways to further investigate and drive research in predictive vehicular content delivery.
AB - Emerging mobility-aware content delivery approaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is to forecast the user locations and associated link capacity, and then proactively counter service fluctuations in advance. For instance, a user that is heading towards low coverage can be prioritized and have video content prebuffered. While the reported gains are encouraging, the results are primarily based on assumptions of perfect prediction. Investigating the predictability of mobility and future signal variations is therefore imperative to evaluate the practical viability of such predictive content delivery paradigms. To this end, this paper presents a large-scale measurement study of 33 repeated trips along a 23.4 km bus route covering urban and sub-urban areas in Kingston, Canada. We provide a thorough analysis of the collected traces to investigate the effects of geographical area, time, forecasting window, and contextual factors such as signal lights and bus stops. The collected dataset can also be used in several other ways to further investigate and drive research in predictive vehicular content delivery.
UR - http://www.scopus.com/inward/record.url?scp=84938705125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938705125&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2015.7127639
DO - 10.1109/WCNC.2015.7127639
M3 - Conference contribution
AN - SCOPUS:84938705125
T3 - 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015
SP - 1195
EP - 1200
BT - 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015
Y2 - 9 March 2015 through 12 March 2015
ER -