Smart Water Meter Powered by Internet of Things [Client Case Study]
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The document details a case study on an IoT-enabled smart water meter developed by a startup in Alabama to monitor and control water usage through AI and machine learning. The project includes features such as real-time anomaly detection, automatic flow shut-off, and comprehensive data visualization via a mobile app and web application. The solution leverages a multi-tier architecture and integrates various technologies, including Apache Kafka and Spark, to manage water flow data efficiently.
Smart Water Meter Powered by Internet of Things [Client Case Study]
- 1. Smart Water Meter Powered by Internet of Things (IoT) Client Case Study
- 2. About the Client: The client is a Alabama, USA based start-up that is developing technology to combine artificial intelligence with smart devices and the Internet of Things (IoT) to monitor and control residential and commercial water usage. The technology will help to boost water efficiency, reduce insurance claims due to water damage, and reduce demands on local water supply and wastewater treatment facilities.
- 3. As a preferred IoT development partner, Perfomatix has firm understanding of customer movements better to putting preventive maintenance in place.
- 4. About the Project: Connect an IoT enabled water flow meter to the water pipes View the water usage data over Cloud, consumed through a mobile app Detect anomalous water flow and shut the flow if needed Automatically shut off the flow when the user does not respond to alerts. The client needed a complete IoT smart water meter platform which could solve the following use cases:
- 5. Learn flow patterns using machine learning and predictive analytics. Regularly capture the flow rate and transmit relevant information to the analytics engine Control water flow from the web and mobile application.
- 6. Challenges: To develop an IoT based smart water monitoring device that can report water flow and push user notifications based on a machine learning model of AI-based dataramp architecture. To gather a large amount of raw data from various use cases that are required for making decisions on flow rates by the rule engines running on the Dataramp platform. To do Real-time prediction on high-velocity data generated from a huge array of IoT devices. Visualise this data and integrate it with the apps and platform. Project Requirements include:
- 7. Connect an IoT enabled flow meter to the water pipes. View the water usage data over the cloud, consumed through a mobile app. Detect anomalous water flow and shut the flow if needed. Automatically shut off the flow when the user does not respond to alerts. ArkLabs needed a complete platform with the following functionalities:
- 8. Implementation: The project is to develop an IoT enabled Smart Water Monitoring Device that can detect the water flow. It leverages Artificial Intelligence and will notify if there is a significant abnormality and can shut off the water supply. The smart water monitoring device is based on a machine learning model of AI-based dataramp architecture.
- 9. The real-time data is collected from the flow meter which has components such as water flow sensors and these data will be sent to the particle cloud when the device detects the water flow, which has the corresponding device ID along with the water usage and time. These data will be fetched by the data interface module of the dataramp from the particle cloud via rest APIs and get pushed into the Kafka topic.
- 10. The data pipeline component of the dataramp will receive device data in a distributed manner and will be buffered in Kafka. This means the data which comes in the dataramp platform will be divided into a numbered stream of ordered messages and stores in different topics like water usage, device id, user id, etc.
- 11. The batch processing and stream processing capabilities of the dataramp architecture will be leveraged to employ a predictive algorithm to predict various water flow scenarios. Further processing the dataramp will introduce data into the spark streaming which contains the ML module.
- 12. Here, receivers of the spark streaming will receive and chop up the data streams into batches and send to the ML module, where ML module contains ML model (which will be previously persisted to the data store) and score Model and it will push out the result to an external data stores or to the dashboard using REST endpoints. Datastore (elasticsearch) will consume the data in 2 minutes from a Kafka topic and push it to the web browser using socket connection for displaying the water flow.
- 13. The Solution: Web application iOS App Android App Built a complete supply chain solution with the following components: A complete IOT based platform to save water was custom built with the following components:
- 14. IOT enabled water flow meter was created using an ultrasonic water flow meter to integrate with Particleboard. Native mobile apps were built using iOS and Android framework. A multi-tier architecture was developed using NodeJS. Following are the highlights of the IoT smart water meter solution:
- 16. Screen:
- 17. Technology Stack Used: Front End AngularJS, HTML5, CSS, Bootstrap Back End NodeJS, StrongLoop Database MongoDB Hosting Amazon Web Services Android Java, XML iOS Objective-C Analytics Apache Kafka, Apache Spark, Elasticsearch
- 18. Looking for a similar application? Drop us an email to setup a meeting with our development team: sales@perfomatix.com Or, you can fill our contact form: https://www.perfomatix.com/contact-us/
- 19. Thank you