Low-cost IoT for Seismic Monitoring: Experimental Evaluation of On-board Sensor Fusion for Noise Reduction

Seismic monitoring is essential for a range of monitoring and warning tasks, but traditional high-cost stations limit network density, which is essential for effective monitoring systems. Recent advances in MEMS technology have enabled the development of low-cost alternatives to classic seismic instrumentation. In this paper, a novel IoT-based seismic node is proposed, integrating 5 ADXL355 MEMS accelerometers with an on-board fusion algorithm for noise reduction. The node transmits compressed miniSEED data via MQTT, ensuring compatibility with both cloud platforms for the IoT and seismology standards. Field tests were carried out within the INGV Snowbilla network to compare its performance against an analog MEMS accelerometer. Results show improved seismic data quality, with an average improvement of about 7 dB in noise PSDs compared to using a single, standalone MEMS, bringing performance very close to that of the reference accelerometer. Further experiments showed that, while increasing the number of MEMS provides incremental noise improvements, those do not scale linearly but tend to diminish when fusing additional MEMS. This study demonstrates the potential of low-cost seismic nodes for enhancing earthquake monitoring, also providing an in-depth analysis of the improvements achievable through on-board combination of multiple, very low-cost MEMS measurements, while also bridging IoT-based solutions with traditional seismology architectures.