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On the use of RFID Middleware for real-time data stream processing

Published: 13 November 2023 Publication History

Abstract

RFID technology is a contemporary method of identifying objects and transmitting other associated information. It enables the real-time tracking of mobile objects through a network while enabling the communication of the object's status at each stage. Due to the volume of data that RFID devices produce, significant data collection, filtration, and consolidation are necessary for their intended purpose. A software layer that serves as a connection point between business applications and RFID reader known as RFID middleware is necessary to control this data and transfer it effectively to an information system. The "BTMiddleware1.0" described in this article is a new RFID middleware architecture. The "BTMiddleware1.0 architecture" described in this article is a brand-new RFID middleware architecture. This design enables real-time processing, efficient storage and management of large data streams by combining complex event processing (CEP) with a MongoDB database. Moreover, it presents data in JSON format, which improves interoperability.
Additional Keywords and Phrases: RFID, Middleware, IoT, Data-processing, Tracking-systems.

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Cited By

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  • (2024)Enhanced Malware Prediction and Containment Using Bayesian Neural NetworksIEEE Journal of Radio Frequency Identification10.1109/JRFID.2024.34108818(592-600)Online publication date: 2024

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          NISS '23: Proceedings of the 6th International Conference on Networking, Intelligent Systems & Security
          May 2023
          451 pages
          ISBN:9798400700194
          DOI:10.1145/3607720
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Published: 13 November 2023

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          • (2024)Enhanced Malware Prediction and Containment Using Bayesian Neural NetworksIEEE Journal of Radio Frequency Identification10.1109/JRFID.2024.34108818(592-600)Online publication date: 2024

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