IoT-Based Multi-Parameter Road Safety Monitoring System for Landslide Detection and Real-Time Hazard Mitigation

Abstract

The increasing frequency of hydro-meteorological and geotechnical disasters necessitates the development of integrated, real-time monitoring frameworks to safeguard lives and infrastructure. This research presents the design and implementation of an IoT-based automated multi-hazard Early Warning System (EWS), specifically engineered to mitigate the risks of heavy rainfall, landslides, and structural bridge overloads. The proposed system integrates a suite of high-precision environmental sensors including soil moisture, tipping-bucket rain gauges, seismic vibration detectors, and strain-gauge load cells with an ESP32 microcontroller acting as a high-speed edge-computing node. The framework employs a Sensor Fusion Algorithm to cross-validate environmental triggers, such as identifying a landslide state only when soil saturation and ground tremors exceed simultaneous safety thresholds. Upon hazard detection, the system initiates a multi-layered response: localized acoustic alarms via a piezoelectric buzzer, physical access control through a servo-actuated barrier to block hazardous road segments, and real-time data dissemination to a cloud-based RESTful API for community-wide alerts. Experimental results validate the system’s ability to maintain high data fidelity with an average alert latency of under 2.5 seconds. The significance of this work lies in its scalable, low-cost architecture, providing a proactive and intelligent disaster mitigation tool suitable for high-risk mountainous regions and aging bridge infrastructures.