Smart Water Taps Using IoT Sensors and Deep Reinforcement Learning for Personalized Consumption Control

This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested approach aims to give people more control over their water use in response to water shortages and the need for sustainable resource management. It installs IoT sensors on taps to track water use in real time. A DRL framework is fed data that these sensors have collected. The DRL algorithm develops individual consumption profiles for each user by factoring in past water use, environmental conditions, and personal tastes. Using this unique profile, water efficiency may be improved instantly. Users may easily enter their water conservation choices, budgets, and the system's user interface. Adjusting the water's flow rate by the user profiles learned by the DRL actively regulates water use while keeping people happy. The results of the experiments reveal that our method effectively reduces both water use and waste. Integrating IoT sensors with DRL methods provides a flexible and scalable answer for smart water management that economically benefits the environment and consumers.