[DBE16] Hybrid Sensor Behavioural Anomalies Detection by Using Algorithm MTS

Conférence Internationale avec comité de lecture : IEEE International Conference on Advanced Logistics and Transport (IEEE ICALTâ€™2016), June 2016, pp.to appear, Krakow, Poland,

Auteurs: J. Douin , Y. Barzaj , S. Espie

Mots clés: Evaluation of Road safety Situation, Driver and Traveler Support Systems, Connectd cehicles of the future, Real Time Identications and Tracking, Personalized Driver and Traveler Support Systems, Future Mobility

Résumé: This paper propose a new technic to identify a hybrid solution for Data Acquisition, by using ad-hoc sensors on the vehicle, the sensors in the recent smartphones, MBED and CAN-BUS. The assumption is that Smartphone's sensors will reduce the complexity and the high cost of these instrumentations. The objective is obtaining acceptable measurement accuracy of the collected trajectories and enable for a large-scale deployment of the system's instrumentation, such as a helpful system in the domain of transport. To build a hybrid system, we depend on the properties of the used sensors in both the smartphones and the vehicles to identify several situations like a failure sensor, accident situation and Riderâ€™s behaviour. Data has been collected by smartphone Galaxy S2 sensors that is connected by MBED which saves these data into SD card and in the other hand the data collected from the motorcycle sensors. We introduced a detection method that based on the statistical correlation of sensor signals that are tested by using the concept MTS to distinguish faults from nonlinear or abnormal data signals.

BibTeX

@inproceedings {
	DBE16,
	title	=	"{Hybrid Sensor Behavioural Anomalies Detection by Using Algorithm MTS}",
	author	=	" J. Douin and Y. BARZAJ and S. ESPIE ",
	booktitle	=	"{IEEE International Conference on Advanced Logistics and Transport (IEEE ICALTâ€™2016)}",
	year	=	2016,
	month	=	"June",
	pages	=	"to appear",
	address	=	"Krakow, Poland",
}