Biodegradable and Renewable Antennas for Green IoT Sensors: A Review

The development and integration of the Internet of Things (IoT) sensor technology across various domains have significantly transformed our work and daily lives, enriching society. However, the increase in the number of IoT devices leads to electronic waste (e-waste), which is a growing global concern. The continued development of sustainable IoT sensors utilizing biodegradable and renewable materials will not only help with reducing e-waste but also ensure wider adaptation of sensing applications thereby benefitting the global community. This review article examines the use of biodegradable and renewable materials in developing antennas for various sensing applications, emphasizing their sustainability, biodegradability, and recyclability. The main contributions of our work are six-fold. First, we review common bio-based materials used in microwave components, detailing the selection process for biodegradable and renewable materials, as well as their comparative advantages and limitations. Second, we examine biodegradable and renewable materials in antenna technologies for sensing applications, providing a comparative analysis based on microwave component type, material properties, dielectric constant, measurement method, relative permittivity, and relevant applications. Third, we analyze design requirements for antennas utilizing these materials, comparing antenna design type/technique, substrate and conductive materials, operating frequency band, size, and gain/directivity. Fourth, we evaluate antenna fabrication techniques, discussing their advantages and challenges. Fifth, we comprehensively review applications of biodegradable and renewable antennas in green IoT sensors, with focus areas including agriculture, environmental monitoring, healthcare, wearable electronics, logistics, and food processing. Finally, we address key research challenges, future prospects, and the potential of these technologies moving forward.

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Output Power Prediction of a Photovoltaic Module Through Artificial Neural Network

With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson’s correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70% dataset used for training, 15% dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98% and the MSE of all the three phases is 0.0604.

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