by Dimitrios Amaxilatis (SparkWorks Ltd.), Ioannis Chatzigiannakis (Sapienza University of Rome) and Simos Papadogeorgos (Power Made SA)

Water metering, a major challenge for most cities around the world, is achieved by collecting data from many thousands of water meters using either on-site visits or manual drive-by methods. The main obstacles to the digitisation of this process are the number of water meters and their operational lifetime requirements, making the use of technologies like 4/5G-based connectivity highly inefficient and inappropriate. Our work benefits from a hierarchical architecture setup using multiple layers and edge computing to increase the data collection rates, prolong the lifetime of the whole infrastructure, minimise the additional cost while increasing the benefits, and enabling the data-driven extraction of useful conclusions like consumption profiling or incident detection.

Smart water grids are an evolution of traditional water metering systems that refer to the introduction of continuous, on-demand, and bidirectional data streams between low-level water metering and flow monitoring devices, utility companies, and end-users. As with smart electrical grids, this modernisation improves the management of water reserves, risk management, and infrastructure monitoring offering benefits to both utility companies, end-users. Such information can also help reduce the losses of the system and the misuse of water resources, making the whole system environmentally friendly. High-fidelity real-time data can help utilities identify leaks, malfunctions, schedule maintenance or upgrade interventions, and promote a more sustainable operation.

The deployment of such an end-to-end data collection system is challenging. The volume of data generated from all the water meters can easily overwhelm traditional data collection systems or communication networks and even result in extremely high communication costs that can make the transition unprofitable. Novel approaches are needed to benefit from low power, low cost, and low maintenance communication technologies and systems, as well as cloud-enabled architectures that can scale regardless of the amount of data they receive.

SparkWorks Ltd is a nascent technology company delivering advanced hardware and software products in the areas of edge computing, Internet of Things, building automation, e-health, data analytics, and ambient intelligence. SparkWorks develops and offers Tethys [1, L1], a system that used novel alternative approaches to tackle all the problems presented above, including Edge Computing, to distribute the processing of water metering data in multiple layers of the system. Tethys follows a multi-level architecture combining multiple communication technologies like Wireless-Mbus, LoRA, and 4/5G mobile networks to get the best connectivity on each level, balancing data throughput, operational cost, and device lifetime as best as possible. Figure 1 shows how our system is organised in three distinct layers, the End device layer, the Edge Computing layer, and the Cloud services layer, that communicate with each other, exchange data and control signals, and gradually process and extract all the available information from each packet received from every water meter, pressure meter and water valve in our system. The whole software stack can be deployed in minutes using Amazon Web Services (AWS) and can scale at any needed level, leveraging the cloud-ready solutions provided, including AWS Timestream, for data storage and querying, AWS Lambda for data processing, AWS IoT Core for management of all the IoT infrastructure and AWS API Gateway for exposing the collected data to client applications. Our edge nodes (called Mox and Tergo), as well as the Tethys Cloud Services, are powered by the AWS Greengrass [L2] runtime, allowing us to move components of our data processing and analytics pipeline from the cloud to the edge devices on-demand and based on the processing load of each device. All this happens on top of the basic data collection and processing.

Figure 1: The Tethys system architecture and its layered operation where the end device layer contains all the smart water sensing and control equipment which can be off-the-shelf (i) water consumption meters, (ii) water pressure meters, or (iii) remote-controlled valves, the edge computing layer contains the (i) Mox and (ii) Tergo nodes that collect the data packets from the end devices and forward them to the cloud services that build the cloud services layer on the top of our stack.
Figure 1: The Tethys system architecture and its layered operation where the end device layer contains all the smart water sensing and control equipment which can be off-the-shelf (i) water consumption meters, (ii) water pressure meters, or (iii) remote-controlled valves, the edge computing layer contains the (i) Mox and (ii) Tergo nodes that collect the data packets from the end devices and forward them to the cloud services that build the cloud services layer on the top of our stack.

Tethys is also capable of analysing the recorded water consumption data to identify patterns, behaviours and anomalies. These patterns can be used to easily identify leaks, burst pipes, or even inefficient water use by customers – behaviours that can be changed to improve their water consumption and environmental conscience. The components that provide this feedback to utilities and end-users were developed in conjunction with researchers from the University of Rome. “La Sapienza” University of Rome is Rome’s first university and among the oldest in Europe, founded in 1303. The Department of Computer, Control, and Management Engineering “Antonio Ruberti” (DIAG) has been recently recognised as one of the 180 departments of excellence in Italy.

Data security and data privacy play an important role in Tethys as they do in all remote metering applications. The system is capable of moving encryption keys to and from the edge layers, maintaining the privacy of the end-user data at all times.

Tethys has been deployed and operational, since 2019, in the campus of the Aristotle University of Thessaloniki, monitoring 24 buildings, two Tergo nodes, 28 Mox nodes, and more than 50 end devices, collecting data on variable intervals ranging from three minutes to one hour, based on the hardware of the end devices. The deployment of all the water metering infrastructure was performed by  Power Made SA, including the configuration of the water meters and the interfacing of the existing water meters with IoT enabled smart water meters. Based on the data generated by the Tethys’ system deployment we were able to assess the effect of the COVID-19 pandemic on water consumption in each building and the behaviour of their users as presented in [2] and [3]. We were thus capable of identifying how certain buildings (focused on teaching and students) were left unused during the main lockdown periods while others (medical school and hospital buildings) kept operating almost as before, even with increased water consumption due to the increased hygiene measures employed.

Links:
[L1] https://www.sparkworks.net/projects/project-tethys/
[L2] https://aws.amazon.com/greengrass/ 

References:
[1] D. Amaxilatis, et al.: “A smart water metering deployment based on the fog computing paradigm”, Applied Sciences 10.6 (2020): 1965.
[2] M. Zecchini, et al.: “Identifying Water Consumption Patterns in Education Buildings Before, During and After COVID-19 Lockdown Periods”, IEEE International Conference on Smart Internet of Things (SMARTIOT 2021).
[3] M. Zecchini, et al.: “Using IoT Data-Driven Analysis of Water Consumption to support Design for Sustainable Behaviour during the COVID-19 Pandemic”, SEEDA 2021.

Please contact:
Dimitrios Amaxilatis
SparkWorks Ltd., Ireland
This email address is being protected from spambots. You need JavaScript enabled to view it.

Next issue: January 2022
Special theme:
"Quantum Computing"
Call for the next issue
Image ERCIM News 127
This issue in pdf

 

Image ERCIM News 127 epub
This issue in ePub format

Get the latest issue to your desktop
RSS Feed