Cyber-Physical Systems for Smart Water Networks: A Review

被引:8
作者
Bhardwaj, Jyotirmoy [1 ,2 ]
Krishnan, Joshin P. [1 ]
Marin, Diego F. Larios [3 ]
Beferull-Lozano, Baltasar [1 ]
Cenkeramaddi, Linga Reddy [4 ]
Harman, Christopher [2 ]
机构
[1] Univ Agder, WISENET Ctr, N-4879 Grimstad, Norway
[2] Norwegian Inst Water Res, N-0349 Oslo, Norway
[3] Univ Seville, Dept Elect Technol, Seville 41004, Spain
[4] Univ Agder, ACPS Grp, N-4879 Grimstad, Norway
关键词
Sensors; Monitoring; Sensor systems; Machine learning; Water pollution; Internet of Things; Intelligent sensors; Cyber-physical systems; Internet-of-Things; machine learning; optimal control; and smart water networks; DISSOLVED-OXYGEN; NEURAL-NETWORKS; DRINKING-WATER; QUALITY; CLASSIFICATION; OPTIMIZATION; CHALLENGES; AQUAPONICS; INTERNET; IOT;
D O I
10.1109/JSEN.2021.3121506
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.
引用
收藏
页码:26447 / 26469
页数:23
相关论文
共 142 条
[1]  
Adidrana D, 2019, 2019 INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL, INFORMATICS AND ITS APPLICATIONS (IC3INA), P166, DOI [10.1109/IC3INA48034.2019.8949585, 10.1109/ic3ina48034.2019.8949585]
[2]   Digital Twin Technology for Aquaponics: Towards Optimizing Food Production with Dynamic Data Driven Application Systems [J].
Ahmed, Ayyaz ;
Zulfiqar, Shahid ;
Ghandar, Adam ;
Chen, Yang ;
Hanai, Masatoshi ;
Theodoropoulos, Georgios .
METHODS AND APPLICATIONS FOR MODELING AND SIMULATION OF COMPLEX SYSTEMS, 2019, 1094 :3-14
[3]  
Alpaydin E., 2020, Introduction to machine learning
[4]   A Data Stream Processing Optimisation Framework for Edge Computing Applications [J].
Amarasinghe, Gayashan ;
De Assuncao, Marcos D. ;
Harwood, Aaron ;
Karunasekera, Shanika .
2018 IEEE 21ST INTERNATIONAL SYMPOSIUM ON REAL-TIME DISTRIBUTED COMPUTING (ISORC 2018), 2018, :91-98
[5]  
[Anonymous], 2005, WATER INTELL ONLINE
[6]   Multivariate classification and modeling in surface water pollution estimation [J].
Astel, A. ;
Tsakovski, S. ;
Simeonov, V. ;
Reisenhofer, E. ;
Piselli, S. ;
Barbieri, P. .
ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2008, 390 (05) :1283-1292
[7]   Leak detection using Random Forest and pressure simulation [J].
Aymon, L. ;
Decaix, J. ;
Carrino, F. ;
Mudry, P-A. ;
Mugellini, E. ;
Khaled, O. A. ;
Baltensperger, R. .
2019 6TH SWISS CONFERENCE ON DATA SCIENCE (SDS), 2019, :109-110
[8]   Dynamic Forecast of Daily Urban Water Consumption Using a Variable-Structure Support Vector Regression Model [J].
Bai, Yun ;
Wang, Pu ;
Li, Chuan ;
Xie, Jingjing ;
Wang, Yin .
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2015, 141 (03)
[9]  
Barto AG, 1995, ANALYSIS, DESIGN AND EVALUATION OF MAN-MACHINE SYSTEMS 1995, VOLS 1 AND 2, P407
[10]   Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model [J].
Barzegar, Rahim ;
Aalami, Mohammad Taghi ;
Adamowski, Jan .
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2020, 34 (02) :415-433