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GrainSense: A Wireless Grain Moisture Sensing System Based on Wi-Fi Signals

Published: 09 September 2024 Publication History

Abstract

Grain moisture sensing plays a critical role in ensuring grain quality and reducing grain losses. However, existing commercial off-the-shelf (COTS) grain moisture sensing systems are either expensive, inconvenient or inaccurate, which greatly limit their widespread deployment in real-world scenarios. To fill this gap, we develop a system called GrainSense which leverages COTS Wi-Fi devices to detect the grain moisture without the need for dedicated sensors. Specifically, we propose a wireless grain moisture detection model based on the refraction phenomenon of Wi-Fi signals and the Multiple-Input-Multiple-Output (MIMO) technology. On one hand, we correlate the grain moisture with the phase difference between two refracted Wi-Fi signals that propagate along different paths, based on which grain moisture can be deduced accordingly. On the other hand, to reduce the multi-path interference in indoor environments (e.g., the granary), we adopt Wi-Fi beamforming to enhance the refracted signal. In particular, a new signal feature (i.e., the Wi-Fi CSI beamforming ratio) is designed to eliminate the effect of sub-carrier frequency bias and cumulative phase bias. To validate the effectiveness of the developed system, we conduct extensive experiments with different types of grains in both the laboratory and the granary. Results show that the system can accurately estimate the grain moisture with an mean absolute error smaller than 5%, which meets the requirements for commercial usage. To the best of our knowledge, this is the first model-based work that achieves accurate grain moisture detection based on wireless sensing.

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    cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 8, Issue 3
    September 2024
    1782 pages
    EISSN:2474-9567
    DOI:10.1145/3695755
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    Published: 09 September 2024
    Published in IMWUT Volume 8, Issue 3

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    Author Tags

    1. Grain moisture sensing
    2. Wi-Fi CSI
    3. Wi-Fi beamforming

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    • (2024)Size Matters: Characterizing the Effect of Target Size on Wi-Fi Sensing Based on the Fresnel Zone ModelProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997268:4(1-22)Online publication date: 21-Nov-2024
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