Friday, 3 October 2025

Smart Water Management Using IoT: An Integrated Approach to Conservation and Automation | Chapter 1 | Intelligent IoT Systems: From Research to Real-World Solutions

 

This chapter describes a complete IoT system for domestic and small-scale smart water management that integrates three capabilities: non-contact tank level monitoring; flow-based sealed leak detection with occupancy awareness; and automatic rainwater harvesting. A Wi-Fi-enabled microcontroller integrates sensor inputs and manages local alerts and actuators; a cloud dashboard (Blynk) provides a real-time view, notifications and remote control. This modular, inexpensive system is designed for ease of installation on standard rooftop/storage tanks while allowing user-defined thresholds and operational modes (Home/Away) to reduce noticeable false alarms. The architecture emphasises affordability and practicality, ensuring that it can be adopted even in resource-constrained settings. The design philosophy focuses on simplicity, reliability, and long-term usability. Bench test results in a simulated home environment are illustrative of a reliable system: level estimation remained within ~5% of manual level measurement; leak events prompted action within a remarkable time when Away mode was on; rain-induced lid actuation completed in a matter of seconds. The local buzzer/LED alerts perform as required during a network failure and restore automatically when service is restored. The integration of monitoring, anomaly detection, and harvesting into a single architecture minimises manual engagement, mitigates avoidable loss, and encourages more sustainable use of both stored and harvested water. In addition to benefits for the individual household, this approach is also aligned with larger sustainability objectives. It permits data-driven conservation and creates a retrofit pathway for legacy tanks. This chapter describes the design, implementation, and evaluation of the prototype, practical considerations, future upgrade paths (e.g., predictive analytics, implementation of a backup power source for the actuators, pathway to scaling functionality to multi-tank scenarios, or community-based designs) to facilitate real-world implementation and future research investigations.

 

 

Author(s) Details

Sumit Singha Chowdhury
Department of MCA, Acharya Institute of Technology, Soladevanahalli, Bengaluru, India.

 

Rakhshita B
Department of MCA, Acharya Institute of Technology, Soladevanahalli, Bengaluru, India.

 

K Ramesh Adiga
Department of MCA, Acharya Institute of Technology, Soladevanahalli, Bengaluru, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-88417-94-5/CH1

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