IoT Application
CASE STUDY
FOR
Electronic-Nose Device
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Project
Overview
Electronic-nose devices have received considerable attention in the field of sensor
technology during the past twenty years, largely due to the discovery of numerous
applications derived from research in diverse fields of applied sciences.
We have made a leading platform for our client to sell futuristic electronic nose
devices. It converts the raw data fetched from the multiple sensors of electronic nose
devices into 2-dimensional graphs, which can be easily understood and analyzed by
device owners and researchers to resolve the problems under investigation.
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Project
Requirement
Our client emphasized developing a solution that can simplify to analyze and
understand the data fetched from multiple electronic nose devices and utilize them in
a meaningful way for future research.
The client was looking for a fast, reliable and user-friendly platform that can be easily
accessible by people. The client wanted us to develop a solution leveraging emerging
technologies to streamline the whole process of using electronic nose devices
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Our
Solution
After understanding the requirement of our client, we started formulating the basic
framework of our solution which included advances in sensor design, material
improvements, software innovations and progress in microcircuitry design, systems
integration, etc.
q We had planned to build a web application on AWS using Python as a core
programming language and Flask as its web framework
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Features of the
Solution
q We have applied the Principal Component Analysis (PCA) algorithm, which
reduces the dimensionality of a data set while keeping as much information as
possible by eliminating the less required variables
q To execute PCA algorithms in the cloud, we have used Amazon Sagemaker,
which is capable of handling millions of data
q We also gave the option for the users to view raw data in the form of graphs in the
sample graph section. After filling in required data and uploading files of raw data,
they can opt to view sample graph or PCA graph, as per the requirement
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Basic
Framework
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Functionality
Summary
Framework
Admin
1. Add application
2. Upload training data
3. Add ml model definition
4. Train ml model with training data
5. Assign ML training model to the application used by mobile app
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Functionality
Summary
Framework
Mobile App
1. Connect mobile app to i-nose
2. Select application on the mobile app for detection
3. Get sample data from i-nose for the application
4. Send sample data to server via text file
5. Server runs inference from saved machine learning model for sample data
6. Server sends push notification to mobile app informing detection results
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Screenshot of
the Solution
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Technology
Used
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Python
Flask Framework
Principal Component Analysis (PCA) Algorithm
AWS Sagemaker as cloud machine learning platform
AWS EC2 Instance
AWS S3
Google Graph
HTML5
JavaScript
CSS3
jQuery
Bootstrap 4
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Future
Enhancement
In the further advancement of this platform, we are planning to directly fetch raw data
from the IoT devices and then store it in the database. After fetching raw data, the
platform will apply PCA algorithms and show graphs to the users.
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Thank You
Contact Us:
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