Self-Powered Food Assessment System Using LSTM Network and 915 MHz RF Energy Harvesting
نویسندگان
چکیده
This study proposes a self-powered food monitoring system that benefits from far-field RF Energy harvesting and deep learning techniques. Recent smart IoT systems for quality management mainly focused on the appearance of Total Volatile Organic Compounds (TVOCs) during preservation, which has limitations large power consumption, complex configuration, low accuracy. Different these methods, we aim to measure gradual increase in air pressure inside packages caused by gas emissions deterioration. With this new approach, designed sensor circuit's energy consumption is reduced significantly than conventional systems. The module's operation supplied an harvester capable converting electromagnetic waves space into electrical energy. We adopted Yagi three elements as receiver antenna scavenging module enhance harvested transmission distance. high gain 6.54dBi direction maximum radiation voltage standing wave ratio (VSWR) better 1.3 approximately 60 MHz band (890-950 MHz). To demonstrate feasibility proposed system, set experiments were conducted with different sorts such pork, chicken, fish. Raw data storage temperature, pressure, time obtained battery-less analyzed utilized assess changes. Several classification models, including LSTM, 1D-CNN, MLP, SVM developed trained fish datasets predict states. Experimental results show LSTM classifier, can extract temporal characteristics dataset, achieves best accuracy above 99% all datasets. performance investigated based training confusion matrix.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3095271