Rapid urbanization has intensified challenges related to water wastage and indoor air pollution in Indian households. Commercial monitoring systems are expensive and designed for individual functions rather than integrated household monitoring. This paper presents the design, implementation, and 45-day field validation of an affordable smart monitoring system combining water leakage detection, tank level supervision, and indoor air quality monitoring on a single ESP32 platform. The system integrates YF-S201 flow sensors, MQ-135 gas sensor, DHT22 temperature-humidity sensor, and HC-SR04 ultrasonic sensor with a TensorFlow Lite Micro LSTM model deployed directly on ESP32 for edge-based leakage prediction. The model achieves 92.8% accuracy with 12ms inference time, operating without cloud connectivity. A four-layer alert system (Blynk, Telegram, SMS, Voice Call) and three-tier automated mitigation (buzzer, fan + window, valve shut-off) ensure comprehensive safety response. Experimental testing from 01-Apr-2026 to 15-May-2026 in a 3BHK apartment demonstrated 97.3% uptime, 96.8% leakage detection accuracy with 28s average response, 98.2% gas detection accuracy, and 450L water saved. Total prototype cost is ?2,900, representing 64% reduction compared to commercial alternatives. The system operates at 1.8W average power with 100% SD card data logging success (12,960 records). This research contributes toward smart domestic infrastructure by integrating water conservation and environmental safety into a single IoT framework suitable for middle-class households and AMRUT 2.0 smart city applications.
Internet of Things, Smart Home, Water Leakage Detection, Indoor Air Quality, ESP32, Edge Computing, TensorFlow Lite, Water Conservation, Automated Mitigation.
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