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.

中文翻译：

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大规模工业物联网中RFID标签识别的高效早期破框策略

随着标签数量的增加，一种有效的标签识别方法已成为工业物联网（IIoT）的迫切需求。但是，当初始帧大小与实际标签总数严重不匹配时，现有基于Aloha的防冲突方案的识别性能将受到限制。当将IIoT部署在易于出错的信道环境中时，性能将进一步下降。为了在容易出错的信道环境中优化RFID系统的识别性能，我们提出了一种基于信道识别的高效早期基于帧破坏策略的防冲突算法（EFB-ACA）。EFB-ACA将整个标签识别过程分为两个阶段：收敛阶段和识别阶段。收敛阶段的功能是使调整后的帧快速收敛到合适的大小。早期的分帧策略已嵌入到收敛阶段。数值结果表明，所提出的EFB-ACA算法在易错信道中的效率和稳定性优于其他方法。此外，EFB-ACA算法在无错误通道中也优于其他方法。