The Future of Water Metering: Leveraging AWS for Sustainability

Water meters are essential at every water consumption location, from homes to industrial plants. With water scarcity becoming a pressing issue globally, preventing water loss is more important than ever. Traditional meters are prone to inefficiencies, leading to significant water wastage. However, with the advent of connected water metering solutions, these challenges can be addressed effectively.

Unlike traditional meters, smart meters offer a myriad of benefits, including real-time data analysis for leak detection and operational efficiencies. By utilizing cloud-based services and predictive maintenance capabilities, companies can achieve significant cost and resource savings while also meeting their sustainability goals and corporate social responsibility initiatives.

Implementing connected meters with cloud services enables automated analytics and predictive maintenance, streamlining the data analysis process and reducing manual intervention. By leveraging pre-trained machine learning models, anomalies like leaks in water meter data can be swiftly identified and addressed.

System Architecture Overview

Figure 1: An overview of the solution architecture.

The AWS solution architecture utilizes a standard electromagnetic water meter that transmits analog signals to a Raspberry Pi Zero W for processing. This data is then sent to AWS IoT Core using MQTT messages, enabling further analysis and anomaly detection through Amazon Timestream and SageMaker endpoints.

By integrating existing metering data into AWS, operators can unlock significant value from their infrastructure. This scalable solution can be adapted for various metering devices, showcasing the versatility and efficiency of cloud-based IoT solutions.

Data Processing and Anomaly Detection

        
            {
                "response": {  
                    "flow": "1.781", 
                    "temperature": "24.1"
                }, 
                "status": "success", 
                "device_id": "water_meter_42"
            }
        
    

Upon receiving data from the water meter, AWS IoT Core triggers an IoT rule that relays the information to a Lambda function for storage in Timestream and anomaly scoring. Visualizing historical data using Amazon Managed Grafana provides valuable insights into consumption patterns and facilitates anomaly detection in real-time.

Amazon SageMaker offers pre-trained models for automated anomaly detection, allowing operators to quickly identify and address potential issues before they escalate. By setting thresholds and utilizing the RCF algorithm, anomalies in water flow can be efficiently classified and visualized for immediate action.

About the Authors

Tim Voigt

Tim Voigt is a Solutions Architect at AWS with a keen interest in developing innovative solutions for real-world challenges. Based in Germany, Tim combines his passion for technology with ongoing graduate studies in computer science to drive impactful cloud engineering projects.

Christoph Schmitter

Christoph Schmitter, a Solutions Architect in Germany, specializes in sustainability-focused cloud solutions for digital native customers. With a wealth of experience in software development and cloud strategies, Christoph is dedicated to driving sustainable transformation through technology innovation. Outside of work, he enjoys exploring cutting-edge tech and spending time with his family.