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Title: Development and testing of TV whitespace based modules for IoT applications
Authors: Sen, Sandeepan
Keywords: Engineering::Electrical and electronic engineering::Wireless communication systems
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Sen, S. (2021). Development and testing of TV whitespace based modules for IoT applications. Master's thesis, Nanyang Technological University, Singapore.
Abstract: The work presented in this dissertation is focused on two major aspects in the development and testing of TV-whitespace modules, used for IoT applications. The usage of TV whitespace in this scenario is very helpful because if the free-of-cost availability of unlicensed spectrum of TV bands that can be used as frequencies on which Wi-Fi operates. TV Whitespace (TVWS) operates in the frequency range of 470MHz-740MHz, giving the devices operating at this range multiple features that are beneficial. TVWS is a major contributor in enabling IoT systems, especially in remote locations owing to penetration, large coverage i.e., a line-of-sight communication of up to 10km as well as its cost effectiveness. Due to its comparatively low bit rate, TVWS is usually used for low data rate applications such as IoT sensor network systems or automated security systems. The first part of this work focuses on the DC and RF testing of the newly developed WhizMicro high-power and dongle modules, a TVWS chipset-based board used for digital communication. The WhizMicro module are of two types – a high power module and a dongle module. The high-power module is powered by a 5V supply to initiate the board while a 24V supply is used to trigger the power amplifier present on the board. The board consists of multiple regulators, filters, amplifiers, switches, and passive components that help maintain the outputs across the whole layout. The dongle module operates on a 5V USB supply that is used to initiate the board. Thereafter, the board operates in the desired mode with the help of multiple filters, regulators, amplifiers, switches, and passive components. The RF testing was done using a high impedance probe connected to a spectrum analyzer (USB - SA44B) and a signal generator (VSG25A). The probe had an inbuilt 20dB attenuator and hence the output obtained on the spectrum analyzer was fine-tuned to match the desired outputs. Due to parallel software development for the WhizMicro module, multiple changes were made in the circuit schematics to operate and test the boards for transmitter and receiver sides. The final outcome of this part of the work was to successfully measure the RF outputs across the transmitter and receiver paths on the WhizMicro High-Power as well Dongle module and hence to check the performance of the newly fabricated devices. Based on the RF outputs obtained, the board is to be modified to increase its performance and then the software integration would help deploy the board commercially. The second part of this work focuses on the data analysis of the output of sensors connected to another TVWS-based WhizNano module, using multiple regression models to predict the most ideal method for the given scenario. The WhizNano module has multiple integration protocols like ADC, UART, SPI, PWM and I2C; enabling the board to be integrated with sensors easily. The codes are developed using C programming and compiled using Keil IDE. The firmware of WhizNano contains multiple pre-defined parameters which cannot be accessed during the device and sensor integration. The IC is provided with multiple operational modes, making it suitable for variable load scenarios. The WhizNano module is connected to a Soil moisture sensor and the data obtained is run through multiple Machine Learning regression models to help predict the ideal model for the given scenario and also understand the change in outputs obtained with increased duration. The code to analyze the data obtained from the sensor is written in the Python programming language. The final outcome for the second part of the work is to successfully and accurately predict the soil moisture level measured by the sensor connected to the WhizNano node and gateway. The best prediction model for this work was found to be Random Forest Regression, Decision Tree Regression as well as K-nearest neighbor Regression; with an accuracy of ~84%. The future scope of this work is to deploy this system as an IoT-agriculture system that has an automated system to water the soil to obtain optimal crop growth.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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