TY - JOUR
T1 - Car e-Talk
T2 - An IoT-Enabled Cloud-Assisted Smart Fleet Maintenance System
AU - Hussain, Shariq
AU - Mahmud, Umar
AU - Yang, Shunkun
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affect fleet productivity and performance. In this article, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle's health. Data from sensors are received on the driver's smartphone through a microcontroller and, after processing, useful information is displayed on the driver's mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.
AB - Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affect fleet productivity and performance. In this article, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle's health. Data from sensors are received on the driver's smartphone through a microcontroller and, after processing, useful information is displayed on the driver's mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.
KW - Bayesian learning
KW - Internet of Things (IoT)
KW - cloud-assisted system
KW - context awareness
KW - fleet management
KW - multisensor data fusion
KW - predictive maintenance
KW - vehicle fleets
KW - vehicle health management
UR - https://www.scopus.com/pages/publications/85107506804
U2 - 10.1109/JIOT.2020.2986342
DO - 10.1109/JIOT.2020.2986342
M3 - 文章
AN - SCOPUS:85107506804
SN - 2327-4662
VL - 8
SP - 9484
EP - 9494
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 12
M1 - 9060939
ER -