An offline grouping proof shows that multiple Radio Frequency Identification tags are scanned simultaneously, regardless of a reader device's network connectivity. However, the previous offline grouping proof schemes are unsuitable for passive tags, and do not handle errors that cause performance degradation, such as transmission loss or synchronization failure of shared values between tags and a back-end database. In this paper, we propose an efficient offline grouping proof scheme using multiple types of tags. It makes use of an agent tag which does computing operations instead of the passive tags to reduce the computational cost of them, thus it eventually shortens the time for generating a grouping proof. We show that the number of modules and operations of the passive tags in our scheme are less than half of the previous schemes while it achieves the enhanced security and scalability.

Damage to bridge expansion joints arises from a variety of causes such as increasingly deteriorated bridges, abnormal temperatures, and increased traffic. To detect anomalies in the expansion joints, this study proposes an Artificial Intelligence (AI)-model-based diagnosis method of analyzing the vibration of the bridge bearing that supports the upper structure of a bridge. The proposed system establishes big data with the measured displacement of a bridge bearing and makes an AI-based prediction about the risk of bridge expansion joints. Replacing a bridge bearing makes it possible to manage the bridge displacement before and after construction and helps improve safety inspections and diagnosis methods. It is necessary to prepare a bridge with anomalies for the AI model training. For this reason, a bridge with a bridge bearing was simulated. In addition, a vehicle suitable for the bridge was simulated. The displacement data in normal and abnormal situations were collected, cleaned, and applied to the AI analysis model. The system was found to have over 90% accuracy of prediction about expansion joint faulting and damage.

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