Accurate analysis of traffic flow (TF) data is crucial for the vehicular applications. Conventional deep learning models require task-specific training and are susceptible to high-frequency disturbances, degrading the feature representation capability. To overcome these limitations, this paper proposes a Token-based SelfSupervised Network (TSSN) that can learn TF features in both tokenization and task-agnostic manners. It provides a properly bootstrapped pre-training model for various downstream tasks. In support of the edge computing and vehicular cloud computing, the pooled computational resources facilitate real-time inferences of downstream models. In TSSN, TF data are segmented into tokens. A pretext task, named as Masked Token Prediction (MTP), is then developed to allow TSSN to understand the underlying correlations of TF by predicting randomly masked tokens. By utilizing MTP, TSSN is able to extract the high-level intrinsic semantics of TF, and provide general-purpose token embeddings, leading to improved overall performance and enhanced ability to adapt to different tasks. By substituting the last fully-connected layers with a group of untrained new layers and fine-tuning using small-scale task-specific data, TSSN can be utilized for a variety of downstream tasks in vehicular applications. Simulation results indicate that the TSSN enhances overall performance in comparison to state-of-the-art models.

With the rapid development of big data analytics and the Internet of Things (IoT) technology, online learning becomes more and more prevalent. To improve the quality of online learning and help teachers better understand the students’ learning state, this paper proposes a Facial Emotion Recognition (FER) Enabled Education Aids IoT System, named FEAIS. By deploying a well-trained FER model on the remote server, FEAIS can provide instant feedback on students’ learning state by collecting their facial pictures through IoT devices like cameras or vision sensors and making recognization on their emotion. Utilizing this information, teachers can adjust their teaching progress to ensure that everyone could catch up. The experimental results demonstrate that the FEAIS can accurately recognize students’ emotion and give teachers precise feedback automatically, even if the images captured by IoT devices are occluded or deformed.