Article: A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas Journal: International Journal of Nanotechnology (IJNT) 2023 Vol.20 No.5/6/7/8/9/10 pp.851 - 879 Abstract: The improper distribution of in-house water consumption in the metropolitan regions of several Indian states has raised severe issues from the last few decades. Due to increased human population and inefficient water usage, the average volume of water in the country's aquifers has begun to decline. Traditional water distribution and monitoring systems are unable to address this serious issue. The water crisis in the metropolitan areas needs more efficient and reliable solutions to overcome this water distribution problem. The working of the proposed model is as follows: In the first stage, the data collection technique is proposed for water distribution in metropolitan areas through energy efficient two-phase routing protocol (EE-TPRP) using cloud-assisted wireless sensors. In the second stage, an efficient water demand prediction model (EWDM) using Backpropagation-feed-forward neural network (BP-FNN) is used for the prediction of water consumption for optimal distribution to users. The EE-TPRP protocol is compared to LEACH, MOD-LEACH, and DEEC protocol, where it has reduced overhead and enhanced network lifetime. The BP-FNN is compared to the Regression model, Fuzzy model, and ARIMA model where it has improved the prediction efficiency of the water distribution in metropolitan areas. Inderscience Publishers - linking academia, business and industry through research

Title: A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas

Authors: Mohammad Faiz; A.K. Daniel

Addresses: Department of Computer Science and Engineering, Madan Mohan Malaviya University, Gorakhpur, Uttar Pradesh, 273010, India ' Department of Computer Science and Engineering, Madan Mohan Malaviya University, Gorakhpur, Uttar Pradesh, 273010, India

Abstract: The improper distribution of in-house water consumption in the metropolitan regions of several Indian states has raised severe issues from the last few decades. Due to increased human population and inefficient water usage, the average volume of water in the country's aquifers has begun to decline. Traditional water distribution and monitoring systems are unable to address this serious issue. The water crisis in the metropolitan areas needs more efficient and reliable solutions to overcome this water distribution problem. The working of the proposed model is as follows: In the first stage, the data collection technique is proposed for water distribution in metropolitan areas through energy efficient two-phase routing protocol (EE-TPRP) using cloud-assisted wireless sensors. In the second stage, an efficient water demand prediction model (EWDM) using Backpropagation-feed-forward neural network (BP-FNN) is used for the prediction of water consumption for optimal distribution to users. The EE-TPRP protocol is compared to LEACH, MOD-LEACH, and DEEC protocol, where it has reduced overhead and enhanced network lifetime. The BP-FNN is compared to the Regression model, Fuzzy model, and ARIMA model where it has improved the prediction efficiency of the water distribution in metropolitan areas.

Keywords: water distribution; WSN; artificial neural network; cloud; feed-forward; gateway; sensor node.

DOI: 10.1504/IJNT.2023.134038

International Journal of Nanotechnology, 2023 Vol.20 No.5/6/7/8/9/10, pp.851 - 879

Received: 30 Dec 2021
Accepted: 06 Apr 2022
Published online: 10 Oct 2023 *

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Title: A hybrid WSN based two-stage model for data collection and forecasting water consumption in metropolitan areas

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