Radio Frequency Identification and Sensing Techniques and Their Applications—A Review of the State-of-the-Art

Cui, Lei; Zhang, Zonghua; Gao, Nan; Meng, Zhaozong; Zhen, Li

doi:10.3390/s19184012

Cited by 140 publications

(72 citation statements)

References 100 publications

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“…Specifically, we use low-cost conductive threads and computer-controlled embroidery to integrate ordinary clothing with near-field-responsive inductor patterns that are capable of wirelessly connecting multiple skin-mounted sensors to a reader separated by up to a metre in distance through proximity to the functional patterns. In contrast with prior efforts to integrate NFC functionality into textiles 43 , the near-field-enabled clothing are entirely fabric-based and robust to daily wear because they do not incorporate fragile silicon integrated circuits or require connectors to interact with nearby devices. We develop textile designs that are compatible with NFC-enabled smartphones and devices without any modification, and demonstrate their use in enabling spinal posture monitoring and continuous measurement of temperature and gait during exercise with multiple wireless, batteryfree sensors.…”

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confidence: 99%

Wireless battery-free body sensor networks using near-field-enabled clothing

et al. 2020

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Networks of sensors placed on the skin can provide continuous measurement of human physiological signals for applications in clinical diagnostics, athletics and human-machine interfaces. Wireless and battery-free sensors are particularly desirable for reliable long-term monitoring, but current approaches for achieving this mode of operation rely on near-field technologies that require close proximity (at most a few centimetres) between each sensor and a wireless readout device. Here, we report near-field-enabled clothing capable of establishing wireless power and data connectivity between multiple distant points around the body to create a network of battery-free sensors interconnected by proximity to functional textile patterns. Using computer-controlled embroidery of conductive threads, we integrate clothing with near-field-responsive patterns that are completely fabric-based and free of fragile silicon components. We demonstrate the utility of the networked system for real-time, multi-node measurement of spinal posture as well as continuous sensing of temperature and gait during exercise.

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confidence: 99%

Wireless battery-free body sensor networks using near-field-enabled clothing

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“…The review article "Radio Frequency Identification and Sensing Techniques and Their Applications-A Review of the State of the Art" [1] presents an overview of sensor tags based on high frequency (HF) RFID, ultra-high frequency (UHF) RFID, and chipless RFID technologies. In particular, the article presents selected work on tag antenna designs and antenna printing techniques and on the RF subsystem design of tag chips and chip products, highlighting applications of sensor tags and technical challenges.…”

Section: Summary Of Special Issue Papersmentioning

confidence: 99%

Augmented RFID Technologies for the Internet of Things and Beyond

Tarricone

Grosinger

2020

Sensors

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Radio frequency identification (RFID) is one of the crucial enabling technologies for the Internet of Things (IoT). This is leading to a continuous augmentation of RFID technologies, in terms of sensing capabilities, energetic autonomy, usability, and cost affordability, and this special issue proposes an overview on such a challenging scenario. The proposed results, in terms of cost reduction, miniaturization, and compatibility with complex systems and technologies, as well as the identification of the relevant criticalities, also pave the way to future steps being taken that go beyond the current IoT.

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“…It provides infrastructure to capture, store, and process data coming from various sensors [1]. Radio Frequency IDentification (RFID) and Wireless Sensor Network (WSN) technologies are used to develop such embedded systems [2]. IoT is creating (1) What is the benefit of applying BRBES to compute air pollution prediction?…”

Section: Introductionmentioning

confidence: 99%

“…Better performance of BRBES than other knowledge-driven approaches in terms of dealing with uncertainties is the key benefit of applying BRBES over sensor data of air pollutants. (2) What is the usefulness of adopting Deep Learning for air pollution prediction? Predicting pollution level based on the discovered hidden pattern of sensor data is the advantage of adopting Deep Learning architecture.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Kabir

Islam

Hossain

et al. 2020

Sensors

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Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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Radio Frequency Identification and Sensing Techniques and Their Applications—A Review of the State-of-the-Art

Cited by 140 publications

References 100 publications

Wireless battery-free body sensor networks using near-field-enabled clothing

Wireless battery-free body sensor networks using near-field-enabled clothing

Augmented RFID Technologies for the Internet of Things and Beyond

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

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