Smart water consumption measurement system for houses using IoT and cloud computing

Author:

Fuentes Henry,Mauricio DavidORCID

Abstract

AbstractPresently, in several parts of the world, water consumption is not measured or visualized in real time, in addition, water leaks are not detected in time and with high precision, generating unnecessary waste of water. That is why this article presents the implementation of a smart water measurement consumption system under an architecture design, with high decoupling and integration of various technologies, which allows real-time visualizing the consumptions, in addition, a leak detection algorithm is proposed based on rules, historical context, and user location that manages to cover 10 possible water consumption scenarios between normal and anomalous consumption. The system allows data to be collected by a smart meter, which is preprocessed by a local server (Gateway) and sent to the Cloud from time to time to be analyzed by the leak detection algorithm and, simultaneously, be viewed on a web interface. The results show that the algorithm has 100% Accuracy, Recall, Precision, and F1 score to detect leaks, far better than other procedures, and a margin of error of 4.63% recorded by the amount of water consumed.

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Reference34 articles.

1. Abreu, V., Santin, A., Xavier, A., Lando, A., Witkovski, A., Ribeiro, R., & et al. (2018). A smart meter and smart house integrated to an IdM and key-based scheme for providing integral security for a smart grid ICT. Springer, 23(4), 967–981. Retrieved from https://doi.org/10.1007/s11036-017-0960-4.

2. Alvisi, S., Casellato, F., Franchini, M., Govoni, M., Luciani, C., Poltronieri, F., & et al. (2019). Wireless middleware solutions for smart water metering. Sensors, 19(8), 1853. Retrieved from https://www.mdpi.com/1424-8220/19/8/1853.

3. Barnes, J.W. (1994). Statistical Analysis for Engineers and Scientists: a Computer-Based Approach. New York: McGraw-Hill College.

4. Chen, Y., & Han, D. (2018). Water quality monitoring in smart city: a pilot project. Elsevier Science BV, 89, 307–316. Retrieved from https://linkinghub.elsevier.com/retrieve/pii/S0926580517305988.

5. Chung, W.-Y., & Yoo, J.-H. (2015). Remote water quality monitoring in wide area. Elsevier Science SA, 217, 51–57. Retrieved from https://linkinghub.elsevier.com/retrieve/pii/S0925400515000982 .

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3