With the increase in the number of tags, an efficient approach of tag identification is becoming an urgent need in Industrial Internet of Things (IIoT). However, the identification performance of existing Aloha-based anticollision schemes is limited when the initial frame size is seriously mismatched with the actual tag population size. The performance will degrade further when IIoT is deployed in the error-prone channel environment. To optimize the identification performance of RFID system in an error-prone channel environment, we propose an efficient early frame breaking strategy based anticollision algorithm (EFB-ACA) with channel awareness. The EFB-ACA divides the whole tag identification process into two phases: convergence phase and identification phase. The function of convergence phase is to make the adjusted frame quickly converge to an appropriate size. The early frame breaking strategy is embedded in the convergence phase. Numerical results show that the proposed EFB-ACA algorithm outperforms the other methods on efficiency and stability in the error-prone channel. In addition, EFB-ACA algorithm also outperforms the other methods in the error-free channel.

Radio Frequency Identification (RFID) technology has been used in numerous applications, e.g., supply chain management and inventory control. This paper focuses on the practically important problem of the rapid estimation of the number of tags in large-scale RFID systems with multiple readers and multicategory RFID tags. RFID readers are often static and have to be deployed strategically after careful planning to cover the entire monitoring area, but reader-to-reader collision (R2Rc) remains a problem. R2Rc decreases the reliability of the estimation of the tag population size, because it results in the failure of communication between the reader and tags. In this paper, we propose a coloring graph-based estimation scheme (CGE), which is the first estimation framework designed for multireader and multicategory RFID systems to determine the distribution of tags in different categories. CGE allows for the use of any estimation protocol to determine the number of tags, prevents R2Rc, and results in higher time efficiency and less power-consumption than the classic scheduling method DCS.