TY - GEN
T1 - Machine Learning Based Transportation Modes Recognition Using Mobile Communication Quality
AU - Kawakami, Wataru
AU - Kanai, Kenii
AU - Wei, Bo
AU - Katto, Jiro
N1 - Funding Information:
However, mobile communication quality is essentially non-uniform and easily degraded according to the user’s situations, such as time, locations and even the user moving ___________________________ This work was supported by the R&D contract “Wired and Wireless Converged Radio Access Network for Massive IoT Traffic” with the Ministry of Internal Affairs and Communications, Japan, for radio resource enhancement, JSPS KAKENHI Grant Numbers 15H01684 and 17K12681. 978-1-5386-1737-3/18/$31.00 ©2018 IEEE behaviors (e.g., moving speed). In addition, mobile applications become richer, such as 4K video delivery and Virtual Reality(VR) /Augmented Reality (AR) applications, and require more wireless network resources.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - In order to recognize the transportation modes without any additional sensor devices, we propose a recognition method by using communication quality factors. In the proposed method, instead of Global Positioning System (GPS) and accelerometer sensors, we collect mobile TCP throughputs, Received Signal Strength Indicators (RSSIs), and cellular base station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming service. In accuracy evaluations, we conduct two different field experiments to collect the data in five typical transportation modes (static, walking, riding a bicycle, a bus and a train,) and then construct the classifiers by applying Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest (RF). Results conclude that these transportation modes can be recognized by using communication quality factors with high accuracy as well as the use of accelerometer sensors.
AB - In order to recognize the transportation modes without any additional sensor devices, we propose a recognition method by using communication quality factors. In the proposed method, instead of Global Positioning System (GPS) and accelerometer sensors, we collect mobile TCP throughputs, Received Signal Strength Indicators (RSSIs), and cellular base station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming service. In accuracy evaluations, we conduct two different field experiments to collect the data in five typical transportation modes (static, walking, riding a bicycle, a bus and a train,) and then construct the classifiers by applying Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Random Forest (RF). Results conclude that these transportation modes can be recognized by using communication quality factors with high accuracy as well as the use of accelerometer sensors.
KW - Quality of Service
KW - Transportation modes recognition
KW - communication quality
KW - machine learning
KW - mobile sensing
UR - http://www.scopus.com/inward/record.url?scp=85061451424&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061451424&partnerID=8YFLogxK
U2 - 10.1109/ICME.2018.8486560
DO - 10.1109/ICME.2018.8486560
M3 - Conference contribution
AN - SCOPUS:85061451424
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2018 IEEE International Conference on Multimedia and Expo, ICME 2018
PB - IEEE Computer Society
T2 - 2018 IEEE International Conference on Multimedia and Expo, ICME 2018
Y2 - 23 July 2018 through 27 July 2018
ER -