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In our days, the cyber world is developing due to the revolution of smart cities and machine learning technologies. The internet of Things constitutes the essential background of cyber technology. As a case study, the Internet of Vehicles is one of the leading applications which is developed quickly. Studies are focused on resolving issues related to real-time problems and privacy leakage. Uploading data from the cloud during the data collection step is the origin of delay issues. This process decreases the level of privacy. The objective of the present paper is to ensure a high level of privacy and accelerated data collection. During this study, we propose an advanced Internet of Vehicle architecture to conduct the data collection step. An occlusion detection application based on a deep learning technique is performed to evaluate the IoV architecture. Training data at Distributed Intelligent layer ensures not only the privacy of data but also reduces the delay.

Smart Internet of Vehicles Architecture based on Deep Learning for Occlusion Detection