DUSTIN FRANZ

Analyzing Air Quality using the Internet of Things

Research Project

Purdue University Calumet

Professor Ricardo Calix

Internet of Things (IoT)

What is it? Uniquely identifiable objects or things in an Internet-

like structureWhat can it do?

Internet of Cows Reduce Poverty gap in India Environmental Monitoring

What things?

Arduino Microcontrollers connected to environmental sensors Why Environmental Monitoring?

Important in NWI

Why Arduino? Arduino is cheap It’s fun!

What exactly are you monitoring?

MQ7 Carbon Monoxide (CO)

MQ131 Ozone (O3)

Matches type of data collected by the Indiana Department of Environmental Management (IDEM)

Reference Model

Reference model- Measuring

Arduino Uno with Networking

Physical Arduino w/ Sensors

Arduino IDE

Program structure

#Include Libraries

//Define Global Variables

Setup(){//initialize connections}

Loop(){//Read and send data}

Reference model- Connecting

HTTP

REST(Representational State Transfer) Easy to use APIs

HTTP method PUT

Why not GET? Status Codes

Data Formats CSV JSON

Reference model- COSM

CosmA twitter for data

Sharable Searchable

Similar problems

Reference model- Server, Cosm and other APIs

Using the IoT for the IoT

What can we do with just two features? Also have time and location!

More features! Weather API

Temperature, Humidity, …

Python Script

To gather all of this information into a single location

Grabs data from cosm every 5 mins

Weather API has restrictions Updated every 30 mins

JSON

Outputs to CSV file

def getTemp    grab weather data    return weather data

def writeToCSV    grab cosm data    call getTemp every 30 mins    write data to csv

def doWork    call writeToCSV for each feed

##main##    call doWork every 5 mins

Reference model- Analyzing

Problem statement

We’ve collected data from different locations in northwest Indiana and also collected polluted data from car emissions

Use Weka to use this data to try to predict polluted data vs non-polluted data

Dataset

MQ131MQ7PollutedlatitudeLongitudetemperature in Fahrenheitdew point in FahrenheitPrecipitation in the last hour in Inchespressure in in HgRelative humidityDate and Time

WEKA

Naïve Bayes

Ranker

Problems

Sensors often have a warm up period

Sensors are sensitive and can easily produce bad data

Getting polluted data

Moving Forward

Need more data of different types (Polluted data)

Need more features to better analyze data

Hope to submit to a journal

Related research Automatic Semantic Content Enrichment of Sensor

Network Based Information