Here's how Perfomatix helped Alabama based start-up in developing technology to combine AI 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. As a preferred IoT development partner, Perfomatix has firm understanding of customer movements better to putting preventive maintenance in place.

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:

15. Architecture Diagram:

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