Low-Power IoT Seismic Detection with Machine Learning Integration

This paper presents a real-time seismic monitoring system based on low-power Internet of Things (IoT) nodes equipped with Micro-Electromechanical Systems (MEMS) accelerometers and Machine Learning (ML) algorithms for earthquake detection. The system employs edge computing to process data locally to lower latency and reduce cloud infrastructure dependence. Designed for energy-efficient operation, this approach enables continuous monitoring in remote or resource-constrained environments. The addition of ML enhances the detection of seismic events, offering a costeffective and scalable solution for early warning applications. Performance metrics were used to evaluate how effectively the ML model performs, achieving high accuracy (92%) and precision (94%).