A Neural Network-Based Model of Radio Frequency Energy Harvesting Characteristics in a Self-Powered Food Monitoring System

Lam, Minh Binh; Dang, Nam Trung; Nguyen, Trung-Hau

doi:10.1109/jsen.2019.2922346

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…For the RF energy harvesting system with a dedicated source, the transmission antenna's parameters, such as gain, transmit power, and frequency, have been determined; the harvested module with a higher gain antenna achieves more collected power. However, recent studies have focused on developing omnidirectional antennas with low gain, usually less than 2 dBi [14]. In a food monitoring system, the position of the transmitter and receiver are known.…”

Section: ) the Directional Antenna In Rfeh Systemmentioning

confidence: 99%

“…From the above table, the duration of one operational phase of the module was 327ms, and the average consumption current of the module was 71.7 A  , which corresponds to the power consumption of 237 W  , much smaller than the conventional systems that consume about ten mW [14]. As mentioned in Section II.B.3, the food monitoring experiments are conducted at a distance of 2m from transmitter to sensor tag.…”

Section: ) Evaluation Of the Sensor Tag Operationmentioning

confidence: 99%

“…The MOS gas sensor warming can take up to a few minutes, as is the case with the CCS811 sensor [14]. This downside has a significant impact on the performance of power-constrained applications such as energy harvesting, which achieves the level of microwatts in harvested power [15].…”

Section: Introductionmentioning

confidence: 99%

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Self-Powered Food Assessment System Using LSTM Network and 915 MHz RF Energy Harvesting

Kim

Lam

et al. 2021

IEEE Access

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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 food quality management mainly focused on the appearance of Total Volatile Organic Compounds (TVOCs) during food preservation, which has the limitations of large power consumption, complex configuration, and low accuracy. Different from these methods, we aim to measure the gradual increase in air pressure inside food packages caused by gas emissions during food quality deterioration. With this new approach, the designed sensor circuit's energy consumption is reduced significantly than in conventional systems. The sensor module's operation power is supplied by an RF energy harvester capable of converting electromagnetic waves in space into electrical energy. We adopted a Yagi three elements as a receiver antenna in the RF energy scavenging module to enhance the harvested power and transmission distance. The designed antenna has a high gain of 6.54dBi in the direction of maximum radiation and voltage standing wave ratio (VSWR) better than 1.3 in approximately 60 MHz band (890-950 MHz). To demonstrate the feasibility of the proposed system, a set of experiments were conducted with different sorts of food such as pork, chicken, and fish. Raw data of food storage temperature, air pressure, and storage time obtained from the battery-less sensor module were analyzed and utilized to assess food quality changes. Several classification models, including LSTM, 1D-CNN, MLP, and SVM were developed and trained on the pork, chicken, and fish datasets to predict different food quality states. Experimental results show that the LSTM classifier, which can extract temporal characteristics from the dataset, achieves the best accuracy of above 99% on all three datasets. The food classification performance is investigated based on training accuracy and confusion matrix.

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Section: ) the Directional Antenna In Rfeh Systemmentioning

confidence: 99%

Section: ) Evaluation Of the Sensor Tag Operationmentioning

confidence: 99%

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Self-Powered Food Assessment System Using LSTM Network and 915 MHz RF Energy Harvesting

Kim

Lam

et al. 2021

IEEE Access

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“…The label color changed as the food pH decreased during storage. As reported in [ 16 , 17 , 18 , 19 , 20 ], CO 2 concentration in the food package can also be measured to monitor food freshness when it is released from rotting meat. However, conventional methods are power-consuming, bulky, or battery-operated.…”

Section: Introductionmentioning

confidence: 99%

“…The energy sources in these works were mainly from radiofrequency (RF) waves [ 16 , 17 , 18 , 19 , 20 ], where a sensor tag was designed that is able to harvest energy transmitted from a reader for its operation. Antennas were also designed and fabricated to operate at the frequency of 915 MHz [ 16 ] and 13.56 MHz [ 17 ]. In [ 17 ], the author added a relay resonator to the reader and tag antennas to improve wireless power transmission efficiency.…”

Section: Introductionmentioning

confidence: 99%

Battery-Free and Noninvasive Estimation of Food pH and CO2 Concentration for Food Monitoring Based on Pressure Measurement

Nguyen

Chung

2020

Sensors

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In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO2) concentration in a food package from headspace pressure measurement. While being stored, food quality degrades gradually as a function of time and storage conditions. A food monitoring system is, therefore, essential to prevent the detrimental problems of food waste and eating spoilt food. Since conventional works that invasively measure food pH level and CO2 concentration in food packages have shown several disadvantages in terms of power consumption, system size, cost, and reliability, our study proposes a system utilizing package headspace pressure to accurately and noninvasively extract food pH level and CO2 concentration, which reflection food quality. To read pressure data in the food container, a 2.5 cm × 2.5 cm smart sensor tag was designed and integrated with near-field communication (NFC)-based energy harvesting technology for battery-free operation. To validate the reliability of the proposed extraction method, various experiments were conducted with different foods, such as pork, chicken, and fish, in two storage environments. The experimental results show that the designed system can operate in a fully passive mode to communicate with an NFC-enabled smartphone. High correlation coefficients of the headspace pressure with the food pH level and the headspace CO2 concentration were observed in all experiments, demonstrating the ability of the proposed system to estimate food pH level and CO2 concentration with high accuracy. A linear regression model was then trained to linearly fit the sensor data. To display the estimated results, we also developed an Android mobile application with an easy-to-use interface.

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Scott–Russel Linkage‐Based Triboelectric Self‐Powered Sensor for Contact Material‐Independent Force Sensing and Tactile Recognition

Seo,

Kong,

Chung

2024

Small

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The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG‐based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott–Russell linkage‐inspired TENG (SRI‐TENG) is developed. The SRI‐TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270 000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material‐independent self‐powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

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A Neural Network-Based Model of Radio Frequency Energy Harvesting Characteristics in a Self-Powered Food Monitoring System

Cited by 15 publications

References 30 publications

Self-Powered Food Assessment System Using LSTM Network and 915 MHz RF Energy Harvesting

Self-Powered Food Assessment System Using LSTM Network and 915 MHz RF Energy Harvesting

Battery-Free and Noninvasive Estimation of Food pH and CO2 Concentration for Food Monitoring Based on Pressure Measurement

Scott–Russel Linkage‐Based Triboelectric Self‐Powered Sensor for Contact Material‐Independent Force Sensing and Tactile Recognition

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