Energy-Efficient LoRaWAN Controlled Water Meter

Smart meters for enhanced water supply network modelling and infrastructure planning

Smart water meters at household connections are being installed in large numbers throughout the world due to the benefits they are expected to bring to the water utilities and water consumers. Smart metering provides high‐resolution readings and promises benefits to the water utilities, such as demand forecasting, regulating time‐of‐use watering, and making intelligent operation and planning decisions. For the consumers, smart metering promises improved billing and demand reduction by providing detailed and timely information about their water use and early notification of possible water leaks in their premises. However, the fine‐grained information collected by smart meters raises growing concerns of privacy invasion due to personal behavior exposure (private activity, daily routine, etc.). Nevertheless, there is no readily available technology for protecting water consumers from revealing their in‐home private activities. Thus, a viable argument in favor of smart metering technologies will not be possible without proactively accounting for the associated privacy challenges. Here, we present a practical technology coupling a dedicated apparatus with a control model for increasing personal privacy. We quantify the level of privacy achieved using information‐theoretic criterion and an empirically based occupancy detection method between the smart meter readings and actual water use. Furthermore, we evaluate and compare privacy protection using the best effort approach previously developed for masking activities revealed from smart electricity meters. The main results reveal that simple control actions can disguise personal behavior patterns and, thus, hedge against privacy breach in smart water meters. Furthermore, we quantify the trade‐off between the size of the apparatus and the level of privacy protection it provides. Our results demonstrate how “privacy friendly” smart water metering technology could be implemented in real‐life systems and reduce the privacy concerns of water consumers.

The water leakage detection on pipelines has improved with the advent of remote environmental wireless sensor network (WSN) and Internet of Things (IoT) technologies over the past few years. Leakage detection in underground water pipelines involves algorithms to predict leakage and estimate its location, in which accuracy depends significantly on the distance between the water meters stationed at district water stations. This research proposes to deploy smart wireless water meter nodes at intermediate distances to enhance the accuracy sharply. This requires water meters to operate autonomously, harvesting energy from surroundings, to be self-sufficient. This research proposes a heterogeneous communication with smart water meters that focuses on transferring data and reducing energy consumption by establishing communication with wireless sensor nodes for smart water meter applications.

This article deals with the use of machine learning to detect sudden water leakage. A smart water meter, which enables monitoring the water consumption of the observed object, is used as the source of input data. Based on these data and their analysis, a symbolic regression, which must know not only the input parameters but also the structure of the model, was finally used to build the model. After finding a suitable function and standard deviation from the model, it is possible to set the required sensitivity and thereby detect anomalous states of water consumption in monitored time windows. Since the smart water meter also has a ball valve, if a sudden water leakage is detected, the water meter can autonomously close the main supply and thus avoid extensive damage.

This paper reviews on some of the recent methodologies of intelligent water metering system used in the smart water meter (SWM) to overcome the problem of water crises. The conservation of water is one of the vital problems in worldwide that needs proper attention. Due to the unpredictable rise in water consumption, the entire fresh water sources are diminishing day by day. At present, there is a gradual advancement in the technologies used for the purpose of monitoring of the proper utilization of water sources. This resulted into better functioning and solving various water loss issues in broad spectrum. The different frameworks available for SWM including the advanced communication protocols are also being discussed in this paper that could help for further advancement in the SWM.KeywordsSmart water meter (SWM)FrameworksProtocolsIntelligent water metering system

Water monitoring in households provides occupants and utilities with key information to support water conservation and efficiency in the residential sector. High costs, intrusiveness, and practical complexity limit appliance-level monitoring via sub-meters on every water-consuming end use in households. Non-intrusive machine learning methods have emerged as promising techniques to analyze observed data collected by a single meter at the inlet of the house and estimate the disaggregated contribution of each water end use. While fine temporal resolution data allow for more accurate end-use disaggregation, there is an inevitable increase in the amount of data that needs to be stored and analyzed. To explore this tradeoff and advance previous studies based on synthetic data, we first collected 1 s resolution indoor water use data from a residential single-point smart water metering system installed at a four-person household, as well as ground-truth end-use labels based on a water diary recorded over a 4-week study period. Second, we trained a supervised machine learning model (random forest classifier) to classify six water end-use categories across different temporal resolutions and two different model calibration scenarios. Finally, we evaluated the results based on three different performance metrics (micro, weighted, and macro F1 scores). Our findings show that data collected at 1- to 5-s intervals allow for better end-use classification (weighted F-score higher than 0.85), particularly for toilet events; however, certain water end uses (e.g., shower and washing machine events) can still be predicted with acceptable accuracy even at coarser resolutions, up to 1 min, provided that these end-use categories are well represented in the training dataset. Overall, our study provides insights for further water sustainability research and widespread deployment of smart water meters.

The evolution of urban water metering and conservation in Australia

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review

Flow time series decomposition to identify non-revenue water components in drinking water distribution systems: A data-driven approach.

Smart Water Meters (SWM) hold significant potential to mitigate water scarcity challenges in tourism-intensive destinations, yet their effectiveness in non-residential contexts remains underexplored. This study evaluates the impact of digital metering and feedback mechanisms on water consumption within tourism accommodations, utilising a geolocated panel dataset encompassing 213 high water-consuming establishments in San Bartolomé de Tirajana (Canary Islands). As a leading year-round destination, this region faces operational constraints that complicate conservation initiatives. Our comprehensive dataset integrates water network technical characteristics, tourism demand and supply variables, and climate factors across sub-municipal tourism zones to account for diverse guest profiles and management practices. Employing a random-effects regression model, we identify key determinants of water usage and uncover distinct consumption patterns between hotels and apartment complexes. The results reveal that while SWM installation significantly improves consumption tracking and enables early leak detection—primarily benefiting utilities—access to frequent digital feedback via web platforms correlates with increased water use. These findings challenge the assumption that digitalisation alone ensures efficiency, emphasising the importance of behavioural, organisational, and financial strategies while offering actionable insights for policymakers, utilities, and tourism operators to optimise digital engagement and adapt water governance.

The integration of Internet of Things (IoT) technology into water monitoring and billing systems heralds a new era of efficiency and reliability. By introducing an IoT-based system for automated water billing and quality monitoring, municipalities and utility companies can revolutionize their operations. At the heart of this system are smart water meters, which enable real-time tracking of water consumption, thereby minimizing errors in billing and providing consumers with accurate usage data. Moreover, the deployment of continuous sensor monitoring ensures that water quality parameters are vigilantly observed, allowing for prompt detection of any contamination events. This continuous monitoring is particularly critical given the increasing threats to water quality from various sources. Through the utilization of advanced sensors capable of measuring critical parameters such as pH, turbidity, and chemical levels, the system can swiftly identify deviations from established norms, enabling rapid response measures to be implemented.

Driven by the information age, a paradigm shift is starting to occur in the urban water planning and management field, mainly due to the introduction of advanced metering and communications technologies. This chapter explores the potential role of intelligent water metering systems, and the smart application of resultant data sets for the future of water planning and management in the built environment. Intelligent water meters essentially perform three functions; they automatically and electronically capture, collect and communicate up-to-date water usage readings on a real-time (or nearly real-time) basis. To translate this data into useful information, a comprehensive intelligent water metering system is required, which integrates high-resolution metering (e.g., 0.01?l; 5?sec), communication technologies (e.g., GPRS) and information management systems, in order to provide real-time information on how, when and where water is being consumed for the customer and water business. This chapter seeks to promote the uptake and application of intelligent water metering systems by examining the benefits and drivers of, and barriers to, intelligent metering. Specifically, it presents applicable examples of where intelligent metering systems can significantly enhance current activities in terms of citywide urban planning, infrastructure planning, water demand management and customer satisfaction.

Urban water conservation through customised water and end-use information

An autonomous and intelligent expert system for residential water end-use classification

Smart city technologies present opportunities for local governments, while also exposing them to new risks. While privacy threats and algorithmic biases have received significant attention within urban studies, cybersecurity has received far less. Our study examines systematic variation across smart city technologies in cyber-risks. We administered a survey to cybersecurity experts in which they ranked the vulnerabilities, threats, and consequences of potential cyberattacks for nine different smart city technologies. Analysis using a Markov-based approach indicates that cyber-risks vary greatly across technologies. Notably, technologies that are more vulnerable in technological terms are also viewed as most likely to be impactful if an attack were to occur, and most likely to be targeted by nation-states. Case studies of emergency or security alerts and smart water meters—ranked as most risky and less risky respectively—underscore and help explain this variation, while also highlighting that local governments vary in their ability to vet vendors, and that cyber-protections are typically subordinated to other concerns in procurement decisions. Our study illustrates the utility of disaggregating the concept of the “smart city,” and the analytic utility of comparisons across technologies rather than geographic units and provides a useful framework for local government decision-making.