TY - GEN
T1 - Sensor calibration for floor detection by D2D communications
AU - Chiang, Ting Hui
AU - Chen, Ling Jyh
AU - Tseng, Yu Chee
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Recent advances in technology have led to the rapid development of the Internet of Things (IoT) and the use of smartphones. The phenomenon has been widely influenced by the use of smart sensors, the accuracy of which is critical to the quality of service provided. Hence, it is vital that sensors are calibrated at both the device and software levels. Some applications even rely on environment-dependent sensing results. Floor detection by barometers is one example, where the pressure of a floor actually depends on the base reference pressure. This observation motivates us to study the floor detection problem by using barometers with device-to-device (D2D) communications. In this paper, we propose the Floor Calibration Protocol (FCP), which enables a mobile device to be calibrated with other mobile devices and anchor devices by considering both temporal and spatial factors. We conducted trace-based simulations to evaluate the proposed scheme and compared its performance with that of state-of-the-art approaches to validate the efficacy of our protocol.
AB - Recent advances in technology have led to the rapid development of the Internet of Things (IoT) and the use of smartphones. The phenomenon has been widely influenced by the use of smart sensors, the accuracy of which is critical to the quality of service provided. Hence, it is vital that sensors are calibrated at both the device and software levels. Some applications even rely on environment-dependent sensing results. Floor detection by barometers is one example, where the pressure of a floor actually depends on the base reference pressure. This observation motivates us to study the floor detection problem by using barometers with device-to-device (D2D) communications. In this paper, we propose the Floor Calibration Protocol (FCP), which enables a mobile device to be calibrated with other mobile devices and anchor devices by considering both temporal and spatial factors. We conducted trace-based simulations to evaluate the proposed scheme and compared its performance with that of state-of-the-art approaches to validate the efficacy of our protocol.
KW - Floor detection
KW - Indoor localization
KW - Internet of Things (IoT)
KW - Sensor calibration
UR - http://www.scopus.com/inward/record.url?scp=85045235080&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045235080&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2017.8288155
DO - 10.1109/VTCFall.2017.8288155
M3 - Conference contribution
AN - SCOPUS:85045235080
T3 - IEEE Vehicular Technology Conference
SP - 1
EP - 6
BT - 2017 IEEE 86th Vehicular Technology Conference, VTC Fall 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 86th IEEE Vehicular Technology Conference, VTC Fall 2017
Y2 - 24 September 2017 through 27 September 2017
ER -