Design and implementation of a low-cost IoT based landslide monitoring and early warning system using ESP32 and Blynk cloud platform - Kelani, Muhammad Arif Khumaini; Abd. Rahman, Faridah; Gunawan, Teddy Surya; Kartiwi, Mira; Harun, Harlisya; Abdul Kadir, Kusyairi (IEEE, 2025)
Landslides are among the most devastating natural disasters, often resulting in significant loss of life, infrastructure damage, and environmental degradation. This paper presents the design and development of a low-cost, realtime Internet of Things (IoT)-based landslide monitoring and early warning system, utilizing the ESP32-WROVER-E microcontroller, YL-69 soil moisture sensor, and MPU6050 accelerometer-gyroscope. The system transmits environmental data wirelessly to the Blynk cloud platform, enabling remote monitoring and immediate alert dissemination via a mobile application. Calibration of the soil moisture sensor using piecewise linear regression significantly reduced measurement errors, achieving an accuracy within ±5% of actual volumetric water content. The MPU6050 effectively detected slope inclinations, classifying them into normal, warning, and danger zones with high reliability. Real-time tests under simulated landslide conditions demonstrated the system’s ability to issue timely alerts through push notifications and email, offering scalable deployment in disaster-prone regions. Unlike conventional geotechnical instruments, this system provides portability, energy efficiency, and minimal maintenance requirements. With potential for integration of additional sensors and AI-driven predictive analytics, the proposed solution advances the field of disaster risk reduction. It enhances community resilience through accessible, real-time landslide monitoring technology.