Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil.
Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal 59078-970, Rio Grande do Norte, Brazil.
Sensors (Basel). 2018 Dec 14;18(12):4426. doi: 10.3390/s18124426.
The Industrial Internet of Things (IIoT) is often presented as a concept that is significantly changing industry, yet continuous improvements in the identification and automation of objects are still required. Such improvements are related to communication speed, security, and reliability, critical attributes for industrial environments. In this context, the radio-frequency identification (RFID) systems present some issues related to frame collision when there are several tags transmitting data. The dynamic framed-slotted ALOHA (DFSA) is a widely used algorithm to solve collision problems in RFID systems. DFSA dynamically adjusts the frame length based on estimations of the number of labels that have competed for slots in the previous frame. Thus, the accuracy of the estimator is directly related to the label identification performance. In the literature, there are several estimators proposed to improve labels identification accuracy. However, they are not efficient when considering a large tag population, requiring a considerable amount of computational resources to perform the identification. In this context, this work proposes an estimator, which can efficiently identify a large number of labels without requiring additional computational resources. Through a set of simulations, the results demonstrate that the proposed estimator has a nearly ideal channel usage efficiency of 36.1%, which is the maximum efficiency of the DFSA protocol.
工业物联网（IIoT）常被描述为一个正在显著改变工业的概念，但仍需要不断改进对象的识别和自动化。这些改进与通信速度、安全性和可靠性有关，这些都是工业环境的关键属性。在这种情况下，射频识别（RFID）系统在多个标签传输数据时会出现一些与帧冲突相关的问题。动态帧时隙 ALOHA（DFSA）是一种广泛用于解决 RFID 系统中碰撞问题的算法。DFSA 根据前一帧中竞争时隙的标签数量的估计值动态调整帧长度。因此，估计器的准确性直接关系到标签识别性能。在文献中，已经提出了几种估计器来提高标签识别的准确性。然而，当考虑到大量标签时，它们的效率不高，需要大量的计算资源来执行识别。在这种情况下，这项工作提出了一种估计器，可以在不增加额外计算资源的情况下，有效地识别大量标签。通过一系列模拟，结果表明，所提出的估计器具有接近理想的 36.1%信道使用效率，这是 DFSA 协议的最大效率。
