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- W3174136208 abstract "Intelligent sensing plays an important part in making our use of vehicles safe and problem-free. On average, a person spends over 35 hours in traffic jams each year. This valuable time could be saved by intelligent routing and real-time traffic alerts. Transport is a necessity of life, both in our everyday lives and at work. Navigation apps are now enabling users to access real-time alerts and alternatives. However, with the increase in the number of Internet-of-Vehicle-Things (IoVT), a large amount of data is produced within a short period of time. The huge data produced by the IoVT could be used to obtain greater perspective and to make dramatically smarter decisions. With this data, there is always a risk to security, trust, and privacy (STP). A standardized protocol is needed to preserve privacy and maintain the security of data. This paper addressed several STP issues in an intelligent transportation system. In addition, a deep learning model is proposed to process data generated by the IoVT." @default.
- W3174136208 created "2021-07-05" @default.
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- W3174136208 date "2022-11-01" @default.
- W3174136208 modified "2023-10-18" @default.
- W3174136208 title "Security, Trust, and Privacy for the Internet of Vehicles: A Deep Learning Approach" @default.
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- W3174136208 doi "https://doi.org/10.1109/mce.2021.3089880" @default.
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