An Automated System to Monitor the Usage of Water in Apartments Using IOT and Artificial Neural Network

In the today's society we are growing towards a world where we are using different techniques to automate different daily life tasks to reduce human efforts also the automation of different daily life tasks always offers a lot of advantages like high efficiency and accuracy to do those tasks. Today we are not only focused on the creation of automated systems but also on creation of smart systems like smart homes, smart cities etc. This includes a lot of tasks to be done with the help of Internet of Things, Machine Learning, Artificial Intelligence etc. All these technologies are widely used in order to create a system which can be used to complete a task automatically with the need of any human effort. In such a technology driven society there is always a need to create a system which can be used to monitor the usage of water in different apartments of a building. The main problem here is that there is no way to know how much of water is used in different apartments of a building and the cost of installation of separate water meters is too high also it will need a lot of construction work to install it. Currently there is no system to which is efficient enough to complete this such type of task as it need human efforts at many stages. In this there is a need of creation a system which can be installed easily with very less or no construction also it should be able to collect data and then it should continuously stream whole data to server for processing and generate result in real time also, the overall cost should be low enough that it can be used commercially. In this paper we are focusing on creating a low-cost smart device which can be used to collect the data of water consumption. To achieve this, we have proposed to create a device with Arduino along with some sensors and modules to collect multiple data of water consumption from different apartments. After collecting this data this device will send this data to server with the help of a Wi-Fi module attached to the device. At the same time, we are using an Artificial Neural Network model for the analysis of this data for generating high accuracy results. Finally, these results will be shown on an Android App and Web App for better availability and better usability of the whole system which will allow the user to access it anywhere any time.