Predictive Data Analysis and Machine Learning for Telematics hub based on sensory data
Session
Information Systems and Security
Description
Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life, including the automobile industry. 'Connected Car' is a terminology often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, the status of various parts/lubricants of the car, and if the car needs urgent service or not. In this sense, the data collected from the devices and IoT sensors can be processed in order to analyze the driving habits and provide suggestions and optimizations for improving the economy and maintenance intervals of the vehicle. The processing of this relies on the fusion of heterogeneous multisource information and modeling intangible factors, which would facilitate predictive maintenance and energy saving.
Keywords:
Machine Learning, Telematics hub, IoT, sensors
Session Chair
Agon Mehmeti
Session Co-Chair
Blerton Abazi
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-437-96-7
Location
Lipjan, Kosovo
Start Date
31-10-2020 9:00 AM
End Date
31-10-2020 10:30 AM
DOI
10.33107/ubt-ic.2020.214
Recommended Citation
Istrefi, Dashmir and Zdravevski, Eftim, "Predictive Data Analysis and Machine Learning for Telematics hub based on sensory data" (2020). UBT International Conference. 71.
https://knowledgecenter.ubt-uni.net/conference/2020/all_events/71
Predictive Data Analysis and Machine Learning for Telematics hub based on sensory data
Lipjan, Kosovo
Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life, including the automobile industry. 'Connected Car' is a terminology often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, the status of various parts/lubricants of the car, and if the car needs urgent service or not. In this sense, the data collected from the devices and IoT sensors can be processed in order to analyze the driving habits and provide suggestions and optimizations for improving the economy and maintenance intervals of the vehicle. The processing of this relies on the fusion of heterogeneous multisource information and modeling intangible factors, which would facilitate predictive maintenance and energy saving.