Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety
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Computer Architecture Department, UPC BarcelonaTech, Esteve Terradas, Castelldefels 7-08860, Spain
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Electrical Engineering Department, UPC BarcelonaTech, Esteve Terradas, Castelldefels 7-08860, Spain
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Laboratorio Nacional de Informática Avanzada (LANIA), Monterrey IT Cluster, Technological Research and Innovation Park (PIIT), Km. 10 Autopista al Aeropuerto Internacional Mariano Escobedo, Monterrey-Nuevo León 66629, México
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Sensors 2016, 16(1), 107; https://doi.org/10.3390/s16010107
Received: 4 November 2015
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Revised: 4 January 2016
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Accepted: 12 January 2016
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Published: 15 January 2016
(This article belongs to the Special Issue Sensors in New Road Vehicles)
The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.
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MDPI and ACS Style
Reyes-Muñoz, A.; Domingo, M.C.; López-Trinidad, M.A.; Delgado, J.L. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety. Sensors 2016, 16, 107. https://doi.org/10.3390/s16010107
AMA Style
Reyes-Muñoz A, Domingo MC, López-Trinidad MA, Delgado JL. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety. Sensors. 2016; 16(1):107. https://doi.org/10.3390/s16010107
Chicago/Turabian StyleReyes-Muñoz, Angelica, Mari C. Domingo, Marco A. López-Trinidad, and José L. Delgado. 2016. "Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety" Sensors 16, no. 1: 107. https://doi.org/10.3390/s16010107
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