An IoT-Based Smart Restroom Monitoring System with Occupancy Detection, Automated Soap Supply Management, and Real-Time Air Quality Sensing: Development and ISO/IEC 25010 Evaluation

Internet Of Things; Smart Facility Management; Occupancy Detection; Hydrogen Sulfide; Air Quality Monitoring; ISO/IEC 25010; ESP32; Educational Institutions.

Authors

  • Rowell Allysius L. Cudiamat School of Science and Technology, Centro Escolar University – Manila, Philippines
  • Rexter G. Tegio School of Science and Technology, Centro Escolar University – Manila, Philippines
  • Dan Erico M. Asur School of Science and Technology, Centro Escolar University – Manila, Philippines
  • Eliza B. Ayo School of Science and Technology, Centro Escolar University – Manila, Philippines
June 26, 2026

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Restroom management in educational institutions traditionally relies on scheduled maintenance rounds and manual monitoring, an approach that often fails to address real-time supply shortages, occupancy uncertainty, and deteriorating air quality. This study designed, developed, and evaluated a Smart Restroom Monitoring System (SRMS) for the Information Science Building of Centro Escolar University in Manila, Philippines. The prototype integrated an ESP32-WROOM-32 microcontroller, passive infrared (PIR) motion sensors for occupancy detection, HC-SR04 ultrasonic sensors coupled with a 12 V R385 pump for automated soap level monitoring and reservoir refilling, and the SEN0568 MEMS hydrogen sulfide (H₂S) gas detection sensor for continuous air quality reporting through a role-based, mobile-responsive web dashboard. A descriptive developmental research design was employed. Baseline data from 33 student respondents revealed pervasive deficiencies, including lack of soap (M = 4.79/5.00), unpleasant odors (M = 4.72), and unclean stalls (M = 4.70), with 87.9% of respondents reporting active avoidance of the facility. The deployed prototype was subsequently evaluated against the nine quality dimensions of the ISO/IEC 25010 software quality model using a four-point Likert-type instrument (N = 33). The system obtained a grand weighted mean of 3.85 (SD = 0.02), with all nine dimensions rated Excellent. All evaluators rated overall performance as Excellent and unanimously endorsed broader deployment. Findings demonstrate the technical viability of low-cost, sensor-driven facility management in educational settings and provide a replicable reference architecture for similar Internet of Things (IoT) deployments.