Monitoring and Datalogging System for Suspended Sediment Concentration in a Trapezoidal Flume Using IoT and Fuzzy Logic - Nuha, Khasna Ulin; Oktivasari, Prihatin; Yatmadi, Denny (Journal of Telecommunication Electronics and Control Engineering (JTECE), 2026)
This study seeks to establish a system for monitoring and forecasting suspended sediment concentration (SSC) via the Internet of Things (IoT) and fuzzy logic, implemented through LabVIEW on a laboratory-scale trapezoidal flume prototype. The system incorporates a flowmeter sensor (YF-S201), a water turbidity sensor (SEN0189), and a photodiode sensor for the measurement of SSC. The sediment samples utilized were silty clay soil from the West Flood Canal (KBB). The ESP32 microcontroller acquires data every 10 seconds, transmits it over TCP-IP communication to LabVIEW, stores it in an Excel file, and simultaneously uploads it to Google Sheets as a cloud database. The monitoring functionality is presented via an LCD, displaying real-time data from Google Sheets through API capabilities, thereby functioning as a synchronized integrated datalogger. Execution of fuzzy logic: The LabVIEW Fuzzy Logic Designer employs the Sugeno model, utilizing flow rate and turbidity as input variables, with SSC predicted as the output. The testing results on 2050 measurement data indicate a steady average time recording period of 8-10 seconds without latency, with a time recording error of merely 0.2%. Integration of monitoring and datalogging of IoT-based fuzzy logic real-time assessment of SSC in a trapezoidal flume.