In the recent years, smart sensing approach is creating a vibrant impact in shaping our future. The growth of technology can be incorporated with the rising events triggering a need for a better lifestyle. Recent technological advancement which has influenced a change in lifestyle, is the field of IoT (Internet of Things). The applications of IoT are vast and innumerable. One such application can be implied for the Automobile industry to improve the quality and safety of the vehicles. With the increase in number of accidents and the deteriorating performance of the cars, there is a need for a pragmatic real time monitoring system and a self-learning algorithm for smart predictions. We use the Raspberry Pi as our GPU running Machine Learning algorithm [K-Nearest neighbor (KNN) and Naïve Bayesian algorithm] predicting the vehicle condition and life prediction of Engine, Coolant, etc. We are planning to implement such a system which will be user friendly and user interactive. We propose two methods for data handling, 1] Develop Bluetooth low energy technology as the communication module for data transmission to the cloud database. 2] Alternatively, 4G Dongle can be used for transmitting data directly to the cloud and the mobile application from the Raspberry pi. The cutting edge feature of BLE is its low power consumption and can also be integrated to many sensors at any point in time (scalable technology). Our prototype is using the BLE Communication for OBD-II (On board Diagnostics) - Raspberry pi communication and Wi-Fi for cloud interfacing. We have integrated OBD-II in a FORD Manufactured car to extract the following data: Speed, air pressure, temperature, CO2 emission, GPS Coordinates, Fuel Level indicator sensor. The Raspberry Pi will run the Machine Learning (ML) algorithm and output the results and live prediction of vehicle condition to the mobile application.


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    Titel :

    IoT Cloud Based Real Time Automobile Monitoring System


    Beteiligte:


    Erscheinungsdatum :

    2018-09-01


    Format / Umfang :

    7342062 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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