An IoT-based hygiene monitoring system in the restroom

Public restroom hygiene is a critical factor influencing public health and user comfort. Traditional maintenance approaches, which rely on manual labour and fixed cleaning schedules, are often inefficient and costly, requiring janitors regardless of actual conditions. This research presents an Internet of Things (IoT)-based restroom hygiene monitoring system designed to optimise cleaning by dispatching staff only when hygiene thresholds are exceeded, particularly during periods of elevated ammonia levels and poor Indoor Air Quality (IAQ). The system enables remote, continuous cleanliness assessment by monitoring ammonia levels and analyzing IAQ through humidity and gas resistance metrics. Alerts are triggered based on correlations among humidity, temperature, and occupancy data, enabling timely interventions. The system utilises the InfluxDB time-series database and the Message Queuing Telemetry Transport (MQTT) protocol over Wi-Fi to support real-time monitoring and long-term trend analysis, providing actionable insights for effective hygiene management. A five-month focused analysis within a two-year monitoring period (January 2023 to December 2024) revealed ammonia concentration spikes ranging from 0 ppm to 13 x 1012 ppm. These peaks were linked to sewer issues and cleaning without detergent, while poor IAQ levels (up to 466 x 103 ppm) were associated with excessive use of detergent and air fresheners. Statistical analysis revealed a strong inverse correlation (r = 0.91) between humidity and temperature regarding ammonia, as well as a moderate inverse correlation (r = 0.45) between humidity and gas resistance. Despite 274 users recorded in a single day, effective ventilation was maintained at safe ammonia levels. These findings suggest that overuse of cleaning agents may worsen air quality. The study demonstrates the potential of IoT-enabled systems for responsive, data-driven, and cost-effective restroom hygiene management.

Text 121522.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) Official URL or Download Paper: https://ieeexplore.ieee.org/document/11071697/

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