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Conference Paper SLICE-based Trustworthiness Analysis System
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Authors
Kang-Woon Hong, Dong-Hwan Park
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1389-1390
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539536
Abstract
We introduce system architecture and its functional blocks for the trustworthiness analysis in which the IoT data mining algorithm is used in order to provide trust based services in the field of the connected car. Our system trains dangerous driving behavior(DDB) detection model from the OBD(On Board Diagnosis) data and predicts whether a driver drives dangerously. For this, the system was developed based on the Self Learnable IoT Common SW Engine(SLICE) enabling context awareness, inference and learning on the IoT node by itself. More specifically, while the existing approach predicts the dangerous driving depending on the velocity of the car, the proposed system finds the relevant OBD parameters except for the velocity and makes the decision tree for prediction.
KSP Keywords
Connected Car, Context awareness, Dangerous driving, Data mining(DM), Decision Tree(DT), Detection model, System architecture, analysis system, board diagnosis(OBD), data mining algorithms, driving behavior