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In this work, we integrate digital twin technology with RFID localization to achieve real-time monitoring of physical items in a large-scale complex environment, such as warehouses and retail stores. To map the item-level realities into a digital environment, we proposed a sensor fusion technique that merges a 3D map created by RGB-D and tracking cameras with real-time RFID tag location estimation derived from our novel Bayesian filter approach. Unlike mainstream localization methods, which rely on phase or RSSI measurements, our proposed method leverages a fixed RF transmission power model. This approach extends localization capabilities to all existing RFID devices, offering a significant advancement over conventional techniques. As a result, the proposed method transforms any RFID device into a digital twin scanner with the support of RGB-D cameras. To evaluate the performance of the proposed method, we prototype the system with commercial off-the-shelf (COTS) equipment in two representative retail scenarios. The overall performance of the system is demonstrated in a mock retail apparel store covering an area of 207 m2, while the quantitative experimental results are examined in a small-scale testbed to showcase the accuracy of item-level tag localization.
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This content will become publicly available on June 1, 2026
An Additively Manufactured Novel RFID-Based 3-D Printed Leaf Moisture Sensor for Smart Farming
This work presents the design, analysis, and experimental validation of a novel chip-based 3-D printed ultra-high frequency (UHF) radio frequency identification (RFID) sensor designed for leaf moisture detection for smart farming applications. The presented sensor is designed to operate at a frequency of 915 MHz and utilizes an integrated circuit (IC) chip having a specified impedance of 18.06−j164 Ω at 915 MHz. The proposed sensor is fabricated by the state-of-the-art Nano Dimension DragonFly IV 3-D printer. The 3-D printer uses both dielectric and conductive inks in a single printer for producing additive manufactured electronics (AME). The performance of the sensor is validated by experiments conducted on Valley Oak, Japanese tree lilac, and Crabapple leaf samples. The sensor’s functionality is based on its ability to detect variations in the dielectric properties of leaves, which are caused by changes in moisture content. This is achieved by analyzing the radio frequency (RF) backscattered signal, measured in terms of the received signal strength indicator (RSSI) levels, using a standard RFID reader. Experimental results demonstrate a consistent linear relationship between RSSI levels and leaf moisture content that is used to obtain a calibration curve that can accurately determine unknown moisture levels. By integrating advanced fabrication techniques with reliable RF sensing mechanisms, this work offers a sustainable, and scalable solution for monitoring plant health and optimizing agricultural productivity.
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- Award ID(s):
- 2320798
- PAR ID:
- 10618738
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Sensors Journal
- Volume:
- 25
- Issue:
- 11
- ISSN:
- 1530-437X
- Page Range / eLocation ID:
- 20666 to 20674
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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