IoT-Based Fuzzy Logic System for Real-Time Flooded Road Detection and Decision Support
Abstract
Flooding remains a critical challenge in urban environments, often disrupting transportation systems and endangering drivers who must decide whether to cross inundated roads without precise knowledge of water depth. Existing IoT-based flood monitoring systems primarily emphasize data acquisition and remote alerts but rarely support localized, real-time decision-making for road safety. This study proposes an IoT-based flood detection and decision-support system that integrates Sugeno-type fuzzy logic inference directly into distributed edge nodes to evaluate flooded road conditions in real time. The system employs multiple JSN-SR04T ultrasonic sensors and WeMos D1 microcontrollers interconnected via a wireless network to measure water levels across multiple road segments. Each node autonomously processes sensor inputs to classify road conditions into three categories—Safe, Caution, and Not Safe—and displays results locally via OLED SSD1306 and RYG (Red-Yellow-Green) LED indicators. Experimental results demonstrate that the proposed system achieves an average sensor error of 2.2 cm compared to calibrated measurements and maintains wireless communication stability up to 25 meters with an average system response time of 1.19 seconds. Furthermore, the fuzzy inference outputs from the embedded system closely matched MATLAB-based simulations, validating computational consistency and inference reliability. The integration of edge-level fuzzy decision-making within distributed IoT nodes represents a key innovation, enabling autonomous flood assessment with minimal latency. This study contributes a low-cost, scalable, and intelligent framework for enhancing road safety and smart city flood resilience, particularly in developing urban regions prone to recurrent inundation.
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