Autonomous Data-Driven Water Management Using IoT and Machine Learning

The Internet of Things (IoT) based applications rapidly engulf human-driven systems. The inherent benefits, like process automation, have motivated researchers to propose novel IoT applications in multifaceted domains, contributing towards the economy of effort and conservation of precious resources. Existing literature emphasizes innovative water monitoring systems, but more attention is needed to efficiently use data from water sensors. The paper initially proposes a novel IoT-enabled water management system for data collection and analysis over an extended period. Based on the data collected after system deployment, we leverage the power of statistical techniques and Machine Learning to identify valuable trends from actual water consumption records, such as isolating the source of water wastage, forecasting future water demands, detecting anomalies in data and evaluating the impact of different environmental variables on water consumption. This paper also proposes a subsystem to control the usage of water.