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An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing

Academic Article
Publication Date:
2017
Short description:
An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing / Celesti, A., Galletta, A., Carnevale, L., Fazio, M., Lay-Ekuakille, A., Villari, M.. - In: IEEE SENSORS JOURNAL. - ISSN 1530-437X. - 8:12(2017), pp. 4795-4802. [10.1109/JSEN.2017.2777786]
abstract:
The sudden traffic slowdown especially in fast scrolling roads and highways characterised by a scarce visibility is one of the major causes of accidents among motorised vehicles. It can be caused by other accidents, work-in-progress on roads, excessive motorised vehicles especially at peak times and so on. Typically, fixed traffic sensors installed on roads that interact with drivers' mobile APP through the 4G network can mitigate such a problem, but unfortunately not all roads and highways are equipped with such sensors. In this paper, we discuss a possible alternative solution for addressing such an issue considering mobile traffic sensors directly installed in private and/or public transportation and other volunteer vehicles. In such a scenario a fast real-time processing of big traffic data is fundamental to prevent accidents. In particular, we discuss an IoT Cloud system for traffic monitoring and alert notification based on OpenGTS and MongoDB. Our IoT Cloud system, besides for private drivers, it is very useful for drivers of critical helpful service such as ambulances. Experiments prove that our system provides acceptable response times that allows drivers to receive alert message in useful time so as to avoid the risk of possible accidents.
Iris type:
1.1 Articolo in rivista
Keywords:
Accidents; Cloud computing; Mobile communication; Monitoring; Roads; Sensors; Vehicles; Accident prevention; Big Data; Cloud Computing; IoT; Smart Mobility; Traffic; Vehicles
List of contributors:
Celesti, A.; Galletta, A.; Carnevale, L.; Fazio, M.; Lay-Ekuakille, A.; Villari, M.
Handle:
https://iris.unirc.it/handle/20.500.12318/47102
Published in:
IEEE SENSORS JOURNAL
Journal
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URL

http://ieeexplore.ieee.org/document/8119786/
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