An efficient IoT-based smart water meter system of smart city environment

Water is a precious need of our lives. Due to the rapid population and urbanization, water usage monitoring is a significant problem facing our society. One solution is to control, analyze, and reduce the water consumption of the houses. The emerging of the Internet of Things (IoT) concept lately in...

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Main Authors: Al-Madhrahi, Raad, Alduais, Nayef A. M., Abdullah, Jiwa, Mahdin, Hairulnizam, Haider Yusef Saad, Abdul Malik, Nasser, Abdullah, Alduais, Husam Saleh
Format: Article
Language:English
Published: Science and Information Organization 2021
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Online Access:http://eprints.utm.my/id/eprint/30599/1/AbdulMalikHaider2021_AnEfficientIoTBasedSmartWater.pdf
http://eprints.utm.my/id/eprint/30599/
http://dx.doi.org/10.14569/IJACSA.2021.0120848
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Summary:Water is a precious need of our lives. Due to the rapid population and urbanization, water usage monitoring is a significant problem facing our society. One solution is to control, analyze, and reduce the water consumption of the houses. The emerging of the Internet of Things (IoT) concept lately in our lives has offered the opportunity to establish water usageefficient smart devices, systems and applications for buildings and cities. Many studies have suggested designing an IoT-based smart meter system; however, the IoT sensor node has limited studies, especially in battery life. Therefore, this study aims to implement and analyze an efficient data collection algorithm for IoT-based smart metering applications in consideration with energy consumption. The system items used are Arduino Uno, Wi-Fi-ESP8266, and water flow sensors. The applied algorithm is an efficient data collection algorithm for water meter (EDCDWM) to reduce the number of packet transmissions. Arduino performed this system's implementation, while the simulation and analysis performed by MATLAB R2019b. The average percentage of energy saved by the applied algorithms of EDCDWM absolute change; and EDCDWM with relative differences in all nodes are around 60% and 93%, respectively.