Integrating Iot With Ai-Enabled Wireless Sensor Networks For Landslide Monitoring And Early Warning Systems

Abstract

Monitoring landslides is essential to understand their dynamics and to reduce the risk of human losses by raising warnings before a failure. Monitoring natural environments presents significant challenges, particularly in areas lacking infrastructure. Wireless Sensor Networks (WSNs) have emerged as a practical solution for data collection in such environments. Landslides, among the most devastating natural disasters worldwide, are often triggered by heavy rainfall or seismic activity, particularly prevalent in regions like India. This paper proposes an innovative landslide monitoring system aimed at mitigating link failures in routing protocols. The system incorporates a diverse range of sensors including soil moisture, rainfall, vibration, and humidity sensors, coupled with GSM sensor connectivity through ESP8266 modules. Data collected from these sensors is processed using AI techniques to enhance sensor node lifespan and facilitate real-time assessment of landslide magnitudes. By comparing collected data against predefined threshold values, the system enables timely warning and mitigation strategies. This integrated approach offers a promising solution for effective landslide monitoring and early warning systems in challenging environments.