Smart, Portable IoT Weather Station with Seismic Activity Detection
Wanniarachchi, Tharanga (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2026051913443
https://urn.fi/URN:NBN:fi:amk-2026051913443
Tiivistelmä
The growing demand for decentralized environmental monitoring has driven the development of compact and power-efficient Internet of Things (IoT) systems. This thesis presents the design and implementation of a Smart Portable IoT Weather Station with Seismic Activity Detection, aimed at enabling realtime environmental and geophysical data collection in remote regions, focusing on the northern Nordic region, focusing mainly Finland.
The system is built around the Raspberry Pi Pico W, a Wi-Fi enabled microcontroller known for its low power consumption and compatibility with a wide range of sensors. It integrates a DHT22 for temperature and humidity, a BMP280 for pressure and altitude, a BH1750 for ambient light, and an ADXL345 accelerometer for seismic vibration detection. Real-time sensor data is transmitted via Wi-Fi to a cloud platform for continuous logging, analysis, and visualization.
The research focuses on optimizing power performance through efficient sensor scheduling, data acquisition intervals, and adaptive transmission control, while monitoring the power usage using INA219 Sensors. The resulting prototype demonstrates reliable, low-power monitoring with high accuracy, making it suitable for scalable, long-term IoT applications in environmental and seismic observation.
The system is built around the Raspberry Pi Pico W, a Wi-Fi enabled microcontroller known for its low power consumption and compatibility with a wide range of sensors. It integrates a DHT22 for temperature and humidity, a BMP280 for pressure and altitude, a BH1750 for ambient light, and an ADXL345 accelerometer for seismic vibration detection. Real-time sensor data is transmitted via Wi-Fi to a cloud platform for continuous logging, analysis, and visualization.
The research focuses on optimizing power performance through efficient sensor scheduling, data acquisition intervals, and adaptive transmission control, while monitoring the power usage using INA219 Sensors. The resulting prototype demonstrates reliable, low-power monitoring with high accuracy, making it suitable for scalable, long-term IoT applications in environmental and seismic observation.