상세정보
학술대회
SLICE-based Trustworthiness Analysis System
- 발행일
- 201810
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1389-1390
- 협약과제
- 18HH2900, 사물 스스로 학습이 가능한 IoT 공통 SW 엔진 개발, 박동환
- 초록
- 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 제안 키워드
- Connected Car, Context awareness, Dangerous driving, Data mining(DM), Decision Tree(DT), Detection model, On-board diagnostics(OBD), System architecture, analysis system, data mining algorithms, driving behavior