Smart Water Distribution System Research Articles

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems

Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures. The 3D CNN is tested for a real WDN in Austin using the realistic sized leaks (e.g., 3 L/s for 150-mm pipes) that are generated from hydraulic simulations. The model's performance is evaluated using detection probability, false alarm rate, and localization pipe distance metrics. In addition, the strength of using DL for leak identification is examined by comparing the CNN results with those from an optimization-based model. The 3D CNN performed better than the optimization model indicating that DL has advantages over conventional tools such as optimization methods. However, its adaptability may limit its use in some cases. Since DL can be significantly impacted by hydraulic simulation model, a way to handle modelling error must be determined. In addition, as network changes occur, retraining is required that may be time consuming and have difficulty with the number of failure conditions.

Decentralized periodic event‐triggered control for smart water distribution systems via digital twin approach

AbstractDecentralized periodic event‐triggered control can reduce the working time and data transmissions in the sensor nodes for feedback control loops, making it suitable for wireless networked control systems, especially those with large physical scales, such as smart water distribution systems. However, the design of this control approach requires accurate system model, which is normally difficult to obtain. In this work, we assume a digital twin of the original system can be obtained by some modeling and simulation tools, for example, Simscape in MATLAB/Simulink for water distribution systems, and thus present an event‐triggered controller design approach. We first present the design of such an event‐triggered controller for the digital twin; and then analyze the condition under which if both the original system and its digital twin can be satisfied, the same controller can work for the original system and some type of stability can be guaranteed at the same time. The proposed approach is applied in a smart water distribution system to show the feasibility.

Dynamic switched periodic event‐triggered control systems under denial‐of‐service attacks

AbstractPeriodic event‐triggered control (PETC) can save network bandwidth and sensor energy at the same time; therefore, it is a promising approach for applications, especially large scale plants, such as smart water distribution systems. This work extend PETC by considering denial of service (DoS) attacks. For the PETC, a dynamic variable is introduced with a switched strategy. Different from other dynamic variables, our variable keeps store energy when there is no attacks and only release energy when attacks happen. Thus, the inter‐event intervals when attacks happen can be prolonged while guarantees the same stability and performance. If the variable equals to 0 before the ending of the attacks, then characteristic of the attack duration is analyzed to guarantee a slower Lyapunov convergence rate. The presented approach is verified via a numerical example from smart water distribution systems.

Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks

Smart water distribution systems (SWDSs) have not only enhanced the monitoring and control of the entire water infrastructure but have also jeopardized its security and resilience. The state-of-the-art examined the uncertainties in the water supply network to obtain optimal design parameters to ensure hydraulic integrity. However, the investigations of uncertainties in SWDSs from the cybersecurity perspective remain unexplored. To address this limitation, this paper utilizes a robust chance-constrained optimization strategy to scrutinize the vulnerable location of SWDSs against False Data Injection (FDI) attacks. This is achieved by factoring in the probabilistic behavior of water demand at junctions that can potentially facilitate stealthy FDI attacks on the hourly measurements of the pump’s flow rate. The proposed nonlinear model is validated with several case studies reflecting the real-world cyberattack consequences including cutoff water supply of the network, enhanced power consumption of the pump, and the undesirable pressure surge in the system at strategic locations of SWDSs. As a result of the studied cases, the cyberattack consequences on the entire water supply network are obtained while highlighting the threat-prone regions of the network. This paper contributes to embedding additional security layers to the existing water infrastructure at the vulnerable links and junctions.

Artificial Intelligence in Smart Water Distribution

Abstract: This paper deals with the study of Trustworthy aspect of Artificial Intelligence (AI) which is known as Trustworthy Artificial Intelligence (TAI) and how it can be used to make a Water Distribution System (WDS) into Smart Water Distribution System (SWDS). The main premise of this paper is to conduct a literature review of Trustworthy Artificial Intelligence along with few notable contributions made by AI in making water distribution systems smart.

A Survey of Cyber-Physical Attacks and Detection Methods in Smart Water Distribution Systems

Modern technologies empower water distribution systems (WDS) for better services in the processes of water supply, storage, distribution, and recycling. They improve real-time monitoring, automating, and managing. However, the limitations of these technologies introduce cyber-physical attacks to the WDS. The main goals of cyber-physical attacks include disrupting normal operations and tampering the critical data, which have negative impacts on the WDS. Therefore, it is vital to develop and implement solutions to increase the security of the WDS by detecting and mitigating cyber-physical attacks. Since security for WDS is relatively new, there are no surveys on this topic despite its vital importance. Therefore, in this paper, we provide a comprehensive survey for the common cyber-physical attacks and common detection mechanisms for the WDS. We compare the attacks and detection methods with emphasis on ideas, methods, evaluation results, advantages, limitations, etc. We further provide a future research direction. We realize that there are still not many research attempts in this area and we hope that this work can trigger more research activities related to the WDS.

An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming.

Design and Implementation of IOT Based Smart Water Distribution System

The aim of this project is to manage and plan the usage of water using IoT. This system can be easily installed in residential societies. Water level measurement is very important in some water related fields. This project permits both meter reader as well as individual domestic/ industrial consumers to use regular smart phones. Flow sensor is placed in pipes which continuously inform the water level at the current time to the web server. This information will be updated on the cloud and using an android application, user can visualize the water level on a smart phone anywhere that is connected to internet. So there is a need to monitor and protect the water with a real time water quality monitoring system in order to make active measurement to reduce contamination.

E-Metering and Fault Detection in Smart Water Distribution Systems using Wireless Network

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB

E-Metering and Fault Detection in Smart Water Distribution Systems using Wireless Network

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB

A Wireless Sensor Network Model for E-Metering and Fault Identification in Smart Water Distribution Systems

Water distribution system is a network that supplies water to all the consumers through different means. Proper means of providing water to houses without compromising in quantity and quality is always a challenge. As it is a huge network keeping track of the utilization is difficult for the utility. Hence through this project we come up with a solution to solve this issue. Current technologies like Low Power Wide Area Networks, LoRa and sensor deployment techniques have been in research and were also tested in few rural areas but issues due to hardware deployment and large scale real time implementation was a challenge hence through this system we aim to create and simulate a real time scenario to test a sensor network model that could be implemented in large scale further. This project aims in building a wireless sensor network model for a smart water distribution system. In this system there is bidirectional communication between the consumer and the utility. Each house has a meter through which the amount of water consumed is sent to the utility board. The data has two fields containing the house ID and the data (water consumed); it is being sent to the data collection unit (DCU) which in-turn sends it to the central server so that the consumption is monitored in real time. All this is simulated using NETSIM and MATLAB.

IoT-based water demand forecasting and distribution design for smart city

Abstract The percentage of fresh water resource availability in the world is diminishing every year. According to a world economic forum survey, the increase in water demand will result in high scarcity globally in the next two decades. The eradication of the water demand increase and reducing the losses during the transportation of water is challenging. Thus accordingly, an Internet of Things (IoT)-based architecture integrated with Fog for underground water distribution system has been proposed. Towards designing an IoT water distribution architecture for a smart city, we need to first forecast the water demand for consumers. Hence, accordingly, water demand forecasting has been carried out on a daily basis for a period of three months as a case study using autoregressive integrated moving average (ARIMA) and regression analysis. Based on water demand forecasting analysis, a water distribution design for an IoT-based architecture has been carried out using hydraulic engineering design for proper distribution of water with minimal losses which would result in the development of a smart water distribution system (SWDS). This has been carried out using EPANET.

Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning–Based Anomaly Detection Techniques

Smart water infrastructures are prone to cyber-physical attacks that can disrupt their operations or damage their assets. An algorithm was developed to identify suspicious behaviors in the different cyber-physical components of a smart water distribution system. The algorithm incorporated multiple modules of anomaly-detection techniques to recognize different types of anomalies in the real-time monitoring and control data. Trained artificial neural networks were used to detect unusual patterns that do not conform to normal operational behavior. Principal component analysis was conducted to decompose the high-dimensional space occupied by the sensory data to uncover global anomalies. The algorithm was trained using a historical data set of trusted observations and tested against a validation and a test data set, both featuring a group of simulated attack scenarios. The proposed approach successfully identified all the attacks featured in the Battle of the Attack Detection Algorithms (BATADAL) data sets with high sensitivity and specificity. Nevertheless, the performance was sensitive to high background noise in the sensory data.

Adaptive water demand forecasting for near real-time management of smart water distribution systems

This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24 h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EANNs). It is implemented in a fully automated, data-driven and self-learning Demand Forecasting System (DFS) that is readily transferable to practice. The main characteristics of the DFS are: (a) continuous adaptability to ever changing water demand patterns and (b) generic and seamless applicability to different demand signals. The DFS enables applying two alternative WDF approaches. In the first approach, multiple EANN models are used in parallel to separately forecast demands for different hours of the day. In the second approach, a single EANN model with a fixed forecast horizon (i.e., 1 h) is used in a recursive fashion to forecast demands. Both approaches have been tested and verified on a real-life WDS in the United Kingdom (UK). The results obtained illustrate that, regardless of the WDF approach used, the novel methodology allows accurate forecasts to be generated thereby demonstrating the potential to yield substantial improvements to the state-of-the-art in near real-time WDS management. The results obtained also demonstrate that the multiple-EANN-models approach slightly outperforms the single-EANN-model approach in terms of WDF accuracy. The single-EANN-model approach, however, still enables achieving good WDF performance and may be a preferred option in engineering practice as it is easier to setup/implement.