oneM2M: Enabling Water Network Twins

Imagine a bustling smart city with interconnected systems managing energy, transportation, and water supply. Among these, the water network is critical. It ensures clean water flows to homes, businesses, and public spaces. But what happens when something goes wrong—a sensor malfunctions, or a valve isn’t properly adjusted? Contaminated water might reach the public, or resources may be wasted due to inefficiencies.

Now picture a virtual guardian watching over the entire network, monitoring every pipe, valve, and water quality metric in real-time. This guardian isn’t just a dream—it’s WaterNetworkTwin. Using IoT and the oneM2M standard, WaterNetworkTwin transforms the way cities manage water, ensuring safety, efficiency, and sustainability.

Motivation and the Problem

As cities grow rapidly, the demand for smarter, more sustainable infrastructure has never been greater. Water, the lifeline of any urban ecosystem, is at the core of this transformation. Yet, effective water management is fraught with challenges:

• Public Health Risks
• Resource Wastage
• Human Dependency and Errors
• Lack of Interoperability

Water is more than a utility; it’s a fundamental right and a driver of economic activity and community well-being. Mismanagement of this resource can have cascading effects on public health, environmental sustainability, and urban development.

Our Solution

WaterNetworkTwin is a digital twin platform that integrates IoT devices with the oneM2M standard to create a smart, proactive water management system.

What makes it special? WaterNetworkTwin doesn’t just collect data—it processes, analyzes, and acts on it in real time. With predictive analytics, it ensures issues like contamination or leakage are detected and resolved before they escalate. By providing actionable insights through a user-friendly interface, WaterNetworkTwin empowers stakeholders to make data-driven decisions, ensuring reliable and efficient water networks.

How It Works

WaterNetworkTwin combines IoT, real-time analytics, and simulation tools to provide comprehensive water network management. Here’s how:

• IoT Sensors: We use sensors to collect important data about the water quality and infrastructure, including TDS (Total Dissolved Solids), pH levels, and temperature.
• Communication Layer: The sensors send this data to a central system using Wi-Fi.
• oneM2M Interoperability Layer: This layer makes sure different devices can work together, allowing for real-time data sharing and control and a ton of other features.
• Data Processing: The system analyzes the data to track important metrics, find any problems, and send alerts when needed.
• Actuation Manager: Based on the analysis, actions like adjusting valves or activating pumps are initiated.
• Visualization and Simulation: Users can monitor the water network in real-time with the dashboard allowing stakeholders to test different scenarios and improve operations.

Features

• Real-Time Monitoring
• Predictive Analytics
• Seamless Integration with multiple devices and vendors.
• Actuation Capabilities
• Simulation Engine

Hardware Setup

In The WaterNetworkTwin project, we have utilized 5 IoT Nodes, 3 Water Quality Nodes, and 2 Device Control Nodes. These device control nodes are used to control the flow of water along the network.

Water Quality Nodes

The Water Quality nodes have an ESP32 Microcontroller, allowing seamless connectivity and efficient data processing. We used analog sensors to measure Total Dissolved Solids (TDS) and temperature parameters, providing data for the following metrics:

• Uncompensated TDS
• Compensated TDS
• Temperature
• TDS Voltage

Below is the Schematic Diagram for the Water Quality Nodes:

This system ensures that water quality is measured using a non-intrusive approach, preventing contamination of the water sampled. The collected data is accessible through OM2M, allowing for comprehensive tracking and analysis. OM2M, enabling comprehensive tracking and analysis.

In the Water Quality Nodes, real-time actuation and coefficient updates happen simultaneously, with values adjusted from the dashboard. The ESP32, running RTOS, handles two parallel tasks: one loop for server subscriptions and another for posting data every 5 seconds. This ensures smooth operation and quick response to user preferences, such as restarting or recalibrating the system.

Device Control Nodes

The Device Control Nodes, as the name suggests, are the IoT Devices that act as actuators and control the water network. `In the WaterNetworkTwin we have made one IoT device to control the motor function, turning it on/off via Subscription to the foresaid device. Another IoT Device or node to control the water flow in the network, i.e. which path to take. This device is connected to 6 Solenoids in the system, which are also controlled via subscription to the devices directly

1)Motor Controller

The motor controller is designed to optimize performance and enhance efficiency. It leverages an ESP32 module for seamless connectivity and efficient data processing, ensuring reliable operation.

Sensors are incorporated to measure parameters, providing data for the following metrics:

• Voltage
• Current
• Power
• Energy
• Frequency
• Power Factor

This data provides valuable insights into motor performance. The motor controller's primary function is to deliver real-time status updates of the motor, enabling prompt action in case of any issues. Furthermore, by analyzing motor behaviour and performance data, the system enables predictive maintenance, allowing for proactive measures to be taken to prevent potential issues and minimize downtime.

2)Solenoid Controller

We’re using an ESP32 microcontroller and an 8-channel relay module to control six solenoid valves in a water pipeline. The solenoid valves regulate the flow of water through different pipeline sections, which is essential for our experimental setup.

The ESP32 sends commands to the relay module, which switches the solenoids on or off to open or close specific water flow paths. This system allows precise, automated control of water distribution, making it efficient and easy to manage without manual intervention.

Software Setup

In this project, we have used a combination of Internet of Things, Machine Learning and leveraged the oneM2M standard to create the WaterNetworkTwin.

The flow begins with the IoT Nodes that collect data as outlined in the Hardware Setup. These nodes communicate with the system through the Communication Layer, in our case, HTTP over Wi-Fi, ensuring data transmission to the central platform. This platform is essential for managing data and enabling seamless interaction between devices, which is crucial for the coordination needed to support a digital twin. The collected data is then stored and processed in the Data Warehouse, facilitating real-time analysis and insights. This data is leveraged for Visualization and Simulation, creating a dynamic representation of the water network. Additionally, it supports Actuation Control, allowing the system to trigger actions such as adjusting valves or pumps to maintain optimal water management.

oneM2M API Usage and Overview

We have used Eclipse OM2M the open-source Interoperability Layer created by LAARS-CNRS. The OM2M resource tree is as follows

Access Control Policy(ACP):

Access Control Policy defines the rules and policies that govern access to resources within the oneM2M system. It specifies the permissions and restrictions associated with different types of users and entities in the system. The default acp_admin ACP as well as acp_DM for posting data from the sensor end to and retrieving data from the OM2M.

Subscription:

Subscription defines the mechanism to enable event-based notifications within the oneM2M system. It allows our devices to subscribe to specific resources, ensuring they receive real-time updates whenever changes occur. In the WaterNetworkTwin, subscriptions are utilized to monitor key metrics and trigger actions like valve adjustments or alerts when anomalies are detected in the water network.

Descriptor:

The descriptor serves as a comprehensive summary of the specific IoT device's metadata, providing information about the node's location, the type of sensor/controllers used, and the detailed description of data parameters along with Units, Resolution and Accuracy. The example descriptor for Motor Control is followed.

Dashboard:

We have created a Dashboard for the WaterNetworkTwin to visualize and also simulate the water network. We have used Grafana and ReactJS to make this dashboard and it is served using Nginx Via Docker-Compose. This dashboard has multiple views i.e. Actuation, Simulation, Motor Control.

ActuationPage:

The Actuation Page is an interactive interface that displays the water network. Clicking on the solenoid valves triggers a request to oneM2M, which subsequently sends notifications to the devices themselves. When clicking on the motor, a command is sent to oneM2M to turn the motor on or off. If you click on a node, the latest data from the water quality devices sent to oneM2M is displayed in the bottom left corner of the screen. Additionally, a legend for the network is provided in the bottom left corner as well.

Simulation Page:

This page offers a map-based view of the water network, enabling users to place nodes anywhere within the network virtually. These virtual nodes simulate data without the need for physical IoT devices, allowing for the analysis of various scenarios. Users can model and evaluate the impact of factors such as soil and sand impurities mixed with the water, providing valuable insights for optimizing the network.

3D-Dashboard:

This page offers a 3D view and explanation of how Gracie goes to the test setup and can turn the motor on or off. This helps the user feel more engaged.

DemonstrationVideo:

Conclusion:

The WaterNetworkTwin project demonstrates the implementation of a digital twin system for water network monitoring and management at IIIT Hyderabad. By integrating IoT nodes, oneM2M standards, and real-time analytics, we've created a solution that enables comprehensive monitoring of water quality, automated flow control, and network simulation capabilities through an intuitive dashboard.

Team Involved

Mentors:

• Dr. Karthik Vaidhyanathan
• Dr. Deepak Gangadharan
• Ms. Anuradha Vattem

Project Team:

• Likhith Kanigolla
• Varada Sasidhar
• Surya Suhaas Modadugu
• Himanshu Fanibhare

Team Photo: