Enhancing energy efficiency in an internet of things-based automatic weather monitoring node.

Abstract

This dissertation presents a comprehensive study on the design and implementation of an IoT-based weather monitoring system. The research investigated the potential for lever- aging IoT technologies to improve weather data collection and analysis. The primary objective was to develop a weather node capable of monitoring key parameters such as temperature, humidity, rainfall, and pressure. The system incorporates power manage- ment features, including deep sleep modes to extend battery life, and uses solar power to ensure sustainable operation. The methodology considered a detailed design of the weather node, the selection of ap- propriate sensors, and the development of a user interface to display real-time data. The study also evaluated the system’s power consumption across different operational states and conducted a statistical analysis of the weather data. Key findings include the impact of deep sleep modes on power efficiency and the effectiveness of solar power in maintain- ing system operation. Additionally, statistical models were developed to interpret the collected data, which are crucial for predicting weather trends and informing agricultural practices. The research further demonstrated that the designed IoT weather station closely aligned with NASA POWER data, as evidenced by the high Pearson correlation coefficients across all weather parameters. For temperature, humidity, wind direction, pressure and rainfall, the correlation coefficients indicated strong agreement. The statistical tests revealed that most differences in means were not significant at the 0.05 level, confirming the IoT station’s reliability in accurately capturing weather data. Additionally, the IoT weather station showcased remarkable energy efficiency, consuming only 1.744 Wh per hour compared to 6.4 Wh per hour for traditional systems. This low energy footprint, combined with its ability to operate autonomously, makes it an ideal solution for remote and power-limited regions. The station’s strong alignment with NASA data and its sustainable operation underscore its potential as a dependable and practical tool for real-time weather monitoring and analysis.