RF propagation through vegetation with time-varying moisture

Cheu, Alex; Morys, Marcin M.; Anderson, Christopher R.; Durgin, Gregory D.

doi:10.1109/rws.2016.7444369

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…The difference in signal strength trend can be attributed to the wavelength of the signal relative to the test sample bag and its contents. The moisture is thought to enhance surface waves, hence enhancing signal strength at certain frequencies whilst increasing signal attenuation at others, especially higher frequencies [ 55 ]. Unlike WSN and TAG2, there is no clear pattern that can be observed from TAG1 and this indicates that there are no differences in the RSSI reading for different moisture content levels in the samples.…”

Section: Resultsmentioning

confidence: 99%

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi

Kamarudin

Zakaria

et al. 2021

Sensors

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Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

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Section: Resultsmentioning

confidence: 99%

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi

Kamarudin

Zakaria

et al. 2021

Sensors

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“…However, existing propagation models do not account for the effects of grass moisture on near-ground RF propagation in a large nonuniform grass area. Authors in [94] conducted measurements of signal strengths between two horn antennas in a nearground setting with varying moisture on grasses at 3-4 GHz. In the experiments, decreasing the moisture content of the grasses caused decreasing signal strengths, implying a dielectric waveguide effect as a result of wet grasses.…”

Section: Channel Modeling and Measurementmentioning

confidence: 99%

“…In contrast to existing models, authors in [96] considered the effects of interference between legacy transmissions and backscatter transmission, interference between backscatter nodes, and non-linear EH model in modelling and analysing the backscatter system. A time-switching (TS)/power splitting (PS) operation scheme was designed where devices can harvest Investigate the behaviour of tag Use low-cost devices to obtain Measured impedance was used impedance with varying CW real-time impedance values to optimize tag design [91], [92] Investigate the expected returns Model the backscatter channel A complete BackCom link budget was and gains of backscatter channel considering the mechanism of developed for system characterization RF propagation [93] Investigate the expected returns Model the channel of retro-directive Retro-directive channels showed similar and gains of backscatter channel tags in the presence of fading properties to one-way radio link [94] Investigate the effects of RF Take signal strength measurements Moisture showed direct relation with propagation in wet grasses between two horn antennas signal strength implying close to the ground waveguide effect [95] Model and analyze AmBC Develop models for legacy and Legacy link interference is network backscatter link and analyze addressed using OFDM. capacity bounds [96] Investigate effect of interference Model an operating scheme that Near optimal outage capacity in BackCom channel underlaying switches between harvesting and was achieved cellular network transmitting times [97] Investigate city-wide ambient Measure RF power levels along roads Cellular communication bands were RF signal density and busy areas with human traffic most suitable for BackCom in cities [98] Channel estimation in multi-tag Model the relationship between moving Estimate channel of multi-tag AmBC tag signals and collision (up to five) AmBC network.…”

Section: Channel Modeling and Measurementmentioning

confidence: 99%

Backscatter Wireless Communications and Sensing in Green Internet of Things

Toro

Leung

2022

IEEE Trans. on Green Commun. Netw.

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Internet of Things (IoT) is a key technology for enabling ubiquitous applications that interconnect with cyberphysical systems in various environments. However, its large scale adoption is strongly impeded by the limited energy available for most IoT devices that are battery-powered, and further challenged by the growing demands to pack increasing functionalities into IoT devices while shrinking their sizes. To address these problems, researchers have developed techniques for energy harvesting, wireless power transfer, and minimizing power consumption in the sensing, communication and computation components of IoT nodes, as found in many surveys. In contrast, this paper surveys Backscatter Communication (BackCom), a recently emerged technique that enables green IoT through joint wireless communication and sensing and potentially allows IoT devices to operate without batteries. The operating principle of BackCom-based green IoT, its architecture and evolution are presented. Also state-of-the-art applications such as healthcare, agriculture, human activity recognition, transportation and mobile IoT are reviewed together with the operational and security challenges faced by these applications and potential solution techniques to address these challenges while ensuring a high energy efficiency. Lastly, some future applications of BackCombased green IoT are discussed.

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Section: Channel Modeling and Measurementmentioning

confidence: 99%

“…Content may change prior to final publication. [91], [92] Investigate the expected returns Model the backscatter channel A complete BackCom link budget was and gains of backscatter channel considering the mechanism of developed for system characterization RF propagation [93] Investigate the expected returns Model the channel of retro-directive Retro-directive channels showed similar and gains of backscatter channel tags in the presence of fading properties to one-way radio link [94] Investigate the effects of RF Take signal strength measurements Moisture showed direct relation with propagation in wet grasses between two horn antennas signal strength implying close to the ground waveguide effect [95] Model and analyze AmBC Develop models for legacy and Legacy link interference is network backscatter link and analyze addressed using OFDM. capacity bounds [96] Investigate effect of interference Model an operating scheme that Near optimal outage capacity in BackCom channel underlaying switches between harvesting and was achieved cellular network transmitting times [97] Investigate city-wide ambient Measure RF power levels along roads Cellular communication bands were RF signal density and busy areas with human traffic most suitable for BackCom in cities [98] Channel estimation in multi-tag Model the relationship between moving Estimate channel of multi-tag AmBC tag signals and collision (up to five) AmBC network.…”

Section: Channel Modeling and Measurementmentioning

confidence: 99%