An Efficient Early Frame Breaking Strategy for RFID Tag Identification in Large-Scale Industrial Internet of Things

Zhiyong He,Hanguang Luo,Yi-Zhang Jiang

doi:10.1155/2021/4603629

Zhiyong He, Hanguang Luo + Show 1 more

Open Access

https://doi.org/10.1155/2021/4603629

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Abstract

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.

Highlights

RFID is a key enabler of the Internet of ings (IoT), playing a crucial role in connecting low-/nonpowered devices to IoT environments
To cope with the above challenges, we propose an efficient early frame breaking strategy based anticollision algorithm (EFB-ACA) with channel awareness. e proposed EFB-ACA divides the tag identification process into two phases: convergence phase and identification phase; and various collision ratios are applied to identification phase to improve the identification efficiency. e core contributions of this paper can be summarized as follows: (1) An efficient and channel-aware anticollision algorithm is proposed
It can derive the actual number of singleton slots and collision slots in each frame based on the successful transmission possibility of identified tags, which indicates that EFB-ACA can be utilized in a variety of channel environments

Summary

Introduction

RFID is a key enabler of the Internet of ings (IoT), playing a crucial role in connecting low-/nonpowered devices to IoT environments. In Aloha-based protocols, the reader uses tag quantity estimation and frame size adjustment strategies to identify tags. In existing Aloha-based anticollision algorithms, tag population size estimation is a critical issue because it can help the reader to achieve the maximum efficiency when the frame size is the same as the tag population size. To cope with the above challenges, we propose an efficient early frame breaking strategy based anticollision algorithm (EFB-ACA) with channel awareness. (1) An efficient and channel-aware anticollision algorithm is proposed It can derive the actual number of singleton slots and collision slots in each frame based on the successful transmission possibility of identified tags, which indicates that EFB-ACA can be utilized in a variety of channel environments. Our proposed approach makes full use of the early breaking strategy It can accelerate the convergence of frame sizes and improve the stability of anticollision algorithm. The reader can obtain the average successful probability Pd. e actual number of singleton slots can be estimated as

Number of actual collision slots in a frame

Frame sizes

Number of tags

Conclusion