Efficient Water Usage Prediction and Metering System Using IoT

D. Suresh,D. Sarulatha.

Published 2021 in Unknown venue

ABSTRACT

Water is one of the most valuable natural commodities. Water is used for various aspects such as drinking, cooking, etc. At the same time, water is wasted and it is not efficiently consumed. Conserving water is becoming increasingly important in the world as it faces a widening gap between ever reducing water supplies due to poor water governance and increased demand from population growth. So water cost is increasing day by day. Therefore, there is a need to keep track of day-to-day water usage. Also, people living in the flat face the problem where the customer who is not using the water most of the time and the customer using the maximum amount of water have to pay the same amount for water consumption, this results in unfair billing. To overcome this problem, this proposed system calculates water bill based on the volume of water consumed, rather than a flat rate or a fee based on the size of the property serviced, and generates a fair bill accordingly. This system calculates the flow consumption of each and every inlet of the flat through the water flow sensor and also the system automatically generates the accurate water consumption bill for each flat along with the graphical analysis of water consumption data using a live web page. And also, it has a special advantage of leakage detection, open tap detection, and no flow detection alert SMS. The complete leakage of water will be avoided. The data will be updated to the web page frequently and data can be monitored wirelessly. When no bill is paid, the water connection can be automatically stopped. Thus, this project will help us to do effective water conservation.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    Unknown venue

  • Publication date

    2021-05-14

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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