Development of IoT-based Seismic Monitoring System for Early Earthquake Detection

Thesis Abstract

The increasing frequency and devastating impacts of earthquakes necessitate the development of reliable early warning systems, particularly in seismically active regions where prompt detection can save lives and mitigate infrastructural damage. Despite existing seismic monitoring frameworks, many lack real-time data integration, widespread coverage, and cost-effective deployment, which limits timely alerts essential for emergency response. This study aims to design, develop, and evaluate an Internet of Things (IoT)-based seismic monitoring system capable of early earthquake detection with high spatial-temporal resolution and reliable performance. Specific objectives include engineering a sensor network architecture optimized for real-time seismic data collection, implementing and testing data transmission protocols suitable for remote and resource-constrained environments, and empirically validating the system's effectiveness against established seismic activity data. The research adopts a mixed-methods approach, combining quantitative system performance assessment with qualitative evaluations of usability and reliability. The population of the study comprises seismic sensors and IoT devices installed across a chosen tectonically active region comprising 50 sensor nodes strategically deployed in urban and rural zones. A purposive sampling technique is employed to select sensor sites based on seismic risk levels, while a stratified sampling method ensures representative data collection across different terrain features. Data collection instruments include specially designed low-cost tri-axial accelerometers integrated with IoT modules capable of wireless data transmission via LoRaWAN and cellular networks. The system's data integrity, latency, and power consumption are monitored over a six-month operational period. Quantitative data are analyzed through descriptive statistics, regression analysis, and receiver operating characteristic (ROC) curves to assess detection accuracy, false alarm rates, and response times. Additionally, time series analysis using ARIMA models measures the system's predictive capacity. Qualitative feedback from field operators and emergency managers is subjected to thematic analysis, providing insights into system usability, robustness, and areas for improvement. Theoretical frameworks guiding the study include the Theory of Technological Diffusion and the Sensor Network Model, which inform system integration strategies and stakeholder adoption. Expected findings indicate that the IoT-based seismic monitoring network demonstrates a detection accuracy exceeding 90%, with average latency below three seconds and power consumption optimized for six months of continuous operation. The system's performance is anticipated to outperform traditional seismic networks in spatial coverage, affordability, and scalability, particularly in remote areas where infrastructure is limited. The integration of multi-modal sensor data and real-time communication protocols is projected to enhance early warning capabilities, reducing earthquake response times significantly. This study contributes to knowledge by pioneering the comprehensive integration of IoT technologies within seismic monitoring frameworks and establishing evidence-based models for scalable early warning systems in developing and developed regions. It advances understanding in sensor deployment strategies, real-time data analytics, and system resilience in challenging environments, thereby filling a critical gap in earthquake preparedness literature. The main conclusion underscores the feasibility and effectiveness of IoT-driven seismic monitoring, with recommendations emphasizing strategic deployment in high-risk zones, continuous system calibration, and stakeholder engagement for successful adoption. Future research directions include integrating machine learning algorithms for predictive analytics, expanding sensor networks, and assessing socio-economic impacts of early warning dissemination.