Smart Irrigation System for Precision Agriculture—The AREThOU5A IoT Platform

Boursianis, Achilles D.; Papadopoulou, Maria S.; Gotsis, Antonis G.; Wan, Shaohua; Sarigiannidis, Panagiotis; Nikolaidis, S.; Goudos, Sotirios K.

doi:10.1109/jsen.2020.3033526

Cited by 109 publications

(71 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The microstrip patch antenna has been widely used as an RF energy harvester in various applications and for different frequency bands of operation [ 6 , 7 , 8 , 9 ]. It exhibits several comparative advantages, such as ease of fabrication, relatively low cost, small physical size compared to the wavelength of the operating frequency, and medium complexity [ 10 , 11 , 12 ]. Therefore, it has been established as an attractive technique for RF EH applications [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications

Boursianis

Papadopoulou

Koulouridis

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

A triple-band single-layer rectenna for outdoor RF energy applications is introduced in this paper. The proposed rectenna operates in the frequency bands of LoRa, GSM-1800, and UMTS-2100 networks. To obtain a triple-band operation, a modified E-shaped patch antenna is used. The receiving module (antenna) of the rectenna system is optimized in terms of its reflection coefficient to match the RF-to-DC rectifier. The final geometry of the proposed antenna is derived by the application of the Moth Search Algorithm and a commercial electromagnetic solver. The impedance matching network of the proposed system is obtained based on a three-step process, including the minimization of the reflection coefficient versus frequency, as well as the minimization of the reflection coefficient variations and the maximization of the DC output voltage versus RF input power. The proposed RF-to-DC rectifier is designed based on the Greinacher topology. The designed rectenna is fabricated on a single layer of FR-4 substrate. Measured results show that our proposed rectenna can harvest RF energy from outdoor (ambient and dedicated) sources with an efficiency of greater than 52%.

show abstract

Section: Introductionmentioning

confidence: 99%

Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications

Boursianis

Papadopoulou

Koulouridis

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm has applications in environmental monitoring, such as monitoring the temperature of heat-island [ 6 ], gas density monitoring [ 7 , 8 ], monitoring the city air pollution [ 9 , 10 , 11 , 12 ], smart agriculture [ 13 , 14 ], smart battlefield [ 15 ]; where the objective is to monitor the distribution of a correlated physical quantity such as density of gasses, pollutants, radiations, moisture, temperature, etc. In modern days the smart Internet of things (IoT) devices can act as nodes of sensor network for monitoring of the desired quantities in the extent of a vast area, such as extent of a city, a forest, or even a deserted area.…”

Section: Introductionmentioning

confidence: 99%

Communication-Efficient Tracking of Unknown, Spatially Correlated Signals in Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

Alasti

2021

Sensors

View full text Add to dashboard Cite

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for climate and environmental monitoring. In this paper, the percentage of wireless sensors that initiate a communication attempt is assumed as cost. The performance evaluation results show that the proposed spatial tracking approach is low-cost and can model the spatial signal over time with the same performance as that of spatial modeling.

show abstract

“…The correlated spatial signal g(x, y) is analytically formed using (13). According to this approach the synthetic signal is formed by superposition of a large number of two-dimensional Gaussian distributions G(mx, my, σ), that are randomly distributed at center points (mx, my), over the sensor field, each formed according to (14).…”

mentioning

confidence: 99%

“…310 Figure 2, illustrates an instance of the synthetic spatial signal constructed using (13). For 311 generation of this spatial signal, σ 1 and σ 2 are assumed equal to 5 and 10, respectively.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Communication-Efficient Tracking Of Unknown, Spatially Correlated Signals In Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

Alasti¹

2021

Preprint

View full text Add to dashboard Cite

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for environmental monitoring. In this paper, the percentage of the communication attempts of wireless sensors is assumed as cost. Performance evaluation results show that the proposed spatial tracking approach is low cost and can model the spatial signal over time with the same performance as that of spatial modeling.

show abstract

Smart Irrigation System for Precision Agriculture—The AREThOU5A IoT Platform

Cited by 109 publications

References 48 publications

Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications

Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications

Communication-Efficient Tracking of Unknown, Spatially Correlated Signals in Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

Communication-Efficient Tracking Of Unknown, Spatially Correlated Signals In Ad-Hoc Wireless Sensor Networks: Two Machine Learning Approaches

Contact Info

Product

Resources

About