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The accurate monitoring and management of dairy cattle behavior are critical for improving farm productivity as well as animal welfare and health status. In this paper, we present a self-powered dairy-cattle-behavior monitoring system that harnesses 915 MHz radio-frequency (RF) energy harvesting and bidirectional long short-term memory (Bi-LSTM) networks. The system aims to enable continuous and real-time monitoring of cattle behaviors while eliminating the need for battery replacements. By harvesting RF energy from the surrounding electromagnetic radiation, our system achieves long-term, self-sustainable operation, reducing maintenance efforts and costs. The Bi-LSTM network effectively captures the temporal dependencies and patterns in the collected sensor data, enabling accurate behavior recognition and prediction. Experimental results demonstrate the effectiveness of the proposed system in accurately classifying cattle behaviors, with an overall accuracy of 96.79%. Compared with traditional manual observation methods and battery-dependent systems, our self-powered monitoring system offers enhanced automation, improved welfare monitoring, and increased operational efficiency. The combination of RF energy harvesting, and Bi-LSTM networks affords a promising approach for self-powered and intelligent dairy-cattle-behavior monitoring, facilitating optimized management practices in the dairy industry.

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.

In the past decades, the emerging concern about food safety has led to the increasing demand for monitoring food quality across the world. Aiming towards a novel solution for monitoring food, this study proposes a non-destructive method with self-powering capability for online food monitoring, which can be extendable to IoT applications. Furthermore, the study introduces a novel deep neural network model to predict different states of food quality based on the monitoring results. To monitor the variation in food quality, the paper proposes the detection of total volatile organic compounds (TVOCs) inside the food packages, which have been released during food deterioration. A low-power sensor mote comprised of a capacity humidity sensor and a metal-oxide (MOX) gas sensor was manufactured for this purpose. The self-powering capability of the mote is provided through an energy harvester module, which benefits from the far-field Radio Frequency Energy Harvesting (RFEH) technology. The operating frequency of the module was chosen at the 915-MHz ISM band. The analysis of the harvester performance showed that the harvester could generate 3.3-V dc with an RF input power of as low as -8 dBm, which was sufficient for the mote operation. To verify the proposed solutions, a demonstration to monitor the deterioration of packaged pork and fish was conducted in eight days under ambient and refrigerated storage conditions, using the self-developed RF-powered sensor mote. The raw variations in TVOCs were analyzed to evaluate the reliability of the proposed TVOC-based method. A one-dimensional (1-D) convolutional neural network (CNN) model was trained on the TVOCs dataset to predict different states of food quality. To investigate the applicability of the proposed 1-D CNN to multi-class determination of food quality, two other supervised machine learning algorithms using 2-D inputs, including Multilayer Perceptron (MLP), and Support Vector Machine (SVM), are studied. Their classification accuracies based on the confusion matrix are identified and compared.

Wireless power transfer (WPT) has long been one of the main goals of Nikola Tesla, the forefather of electromagnetic applications. In this paper, we investigate radio-frequency beamforming in the radiative far field for WPT. First, an analytical model of the channel fading is presented, and a blind adaptive beamforming algorithm is adapted to the WPT context. The algorithm is computationally light, because we need not explicitly estimate the channel state information. Then, a testbed with a multiple-antenna software-defined radio configuration on the transmitting side and a programmable energy harvester on the receiving side is developed in order to validate the algorithm in this specific power application. From the results, it can be seen that the implementation of this version of beamforming indeed improves the harvested power. Specifically, at various distances from 50 cm to 1.5 m, the algorithm converges with two, three, and four antennas with an increasing gain as we increase the number of antennas. These encouraging results could have far-reaching consequences in providing wireless power to Internet of Things devices, our target application.