Wireless sensor networks (WSNs) play an important role in the Industrial Internet of Things (IIoT), and have been widely used in many industrial fields to gather data of monitoring area. However, due to the open nature of wireless channel and resource-constrained feature of sensor nodes, how to guarantee that the sensitive sensor data can only accessed by valid user becomes an key challenge in IIoT environment. Some user authentication protocols for WSNs have been proposed to address this issue. However, previous more or less have their own weaknesses, such as not providing user anonymity and other ideal functions, or being vulnerable to some attacks. To provide secure communication for IIoT, a user authentication protocol scheme with privacy protection for IIoT has been proposed. The security of the proposed scheme is proved under random oracle model, and other security discussions show that the proposed protocol is robust to various attacks. Furthermore, the comparison results with other related protocols and the simulation by NS-3 show that the proposed protocol is secure and efficient for IIoT.

The past year has witnessed the rapid development of applying the Transformer module to vision problems. While some researchers have demonstrated that Transformer-based models enjoy a favorable ability of fitting data, there are still growing number of evidences showing that these models suffer over-fitting especially when the training data is limited. This paper offers an empirical study by performing step-by-step operations to gradually transit a Transformer-based model to a convolution-based model. The results we obtain during the transition process deliver useful messages for improving visual recognition. Based on these observations, we propose a new architecture named Visformer, which is abbreviated from the ‘Vision-friendly Transformer’. With the same computational complexity, Visformer outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy, and the advantage becomes more significant when the model complexity is lower or the training set is smaller. The code is available at https://github.com/danczs/Visformer.