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In this research, we conducted a field experiment to understand vehicle maintenance mechanisms of a connected car platform. Specifically, we investigated the feasibility of prognostics and health management under different driving circumstances, with varying vehicle models, vehicle conditions, drivers\u2019 propensity for speeding, and road conditions. We collected sensor data through a two-stage model of vehicle communication using an on-board diagnostics scanner and data transmission using wireless communication. We found that device defects can be predicted based on driving situations such as the driving mode, mechanical characteristics, and a driver\u2019s speeding propensity. <\/jats:p>","DOI":"10.1177\/1550147718755290","type":"journal-article","created":{"date-parts":[[2018,1,27]],"date-time":"2018-01-27T12:58:09Z","timestamp":1517057889000},"page":"155014771875529","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["An empirical study on real-time data analytics for connected cars: Sensor-based applications for smart cars"],"prefix":"10.1177","volume":"14","author":[{"given":"Jonghyuk","family":"Kim","sequence":"first","affiliation":[{"name":"Graduate School of Information, Yonsei University, Seoul, Republic of 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