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物联网环境中 RFID 目标识别系统的可用性。

Availability of an RFID Object-Identification System in IoT Environments.

机构信息

Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

Applied Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

出版信息

Sensors (Basel). 2021 Sep 16;21(18):6220. doi: 10.3390/s21186220.

Abstract

Through the latest technological and conceptual developments, the centralized cloud-computing approach has moved to structures such as edge, fog, and the Internet of Things (IoT), approaching end users. As mobile network operators (MNOs) implement the new 5G standards, enterprise computing function shifts to the edge. In parallel to interconnection topics, there is the issue of global impact over the environment. The idea is to develop IoT devices to eliminate the greenhouse effect of current applications. Radio-frequency identification (RFID) is the technology that has this potential, and it can be used in applications ranging from identifying a person to granting access in a building. Past studies have focused on how to improve RFID communication or to achieve maximal throughput. However, for many applications, system latency and availability are critical aspects. This paper examines, through stochastic Petri nets (SPNs), the availability, dependability, and latency of an object-identification system that uses RFID tags. Through the performed analysis, the optimal balance between latency and throughput was identified. Analyzing multiple communication scenarios revealed the availability of such a system when deployed at the edge layer.

摘要

通过最新的技术和概念发展,集中式云计算方法已经转移到边缘、雾和物联网(IoT)等接近最终用户的结构。随着移动网络运营商(MNO)实施新的 5G 标准,企业计算功能转移到边缘。除了互联话题,还有全球环境影响的问题。其理念是开发物联网设备以消除当前应用的温室效应。射频识别(RFID)是具有这种潜力的技术,它可以应用于从识别人员到授予建筑物访问权限等各种应用。过去的研究主要集中在如何提高 RFID 通信或实现最大吞吐量上。然而,对于许多应用来说,系统延迟和可用性是关键方面。本文通过随机 Petri 网(SPN)来检查使用 RFID 标签的对象识别系统的可用性、可靠性和延迟。通过执行的分析,确定了延迟和吞吐量之间的最佳平衡。分析多个通信场景揭示了在边缘层部署时该系统的可用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b3b/8472853/508ba6db2c72/sensors-21-06220-g001.jpg

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