analyzing air quality using the internet of things
Post on 23-Feb-2016
67 Views
Preview:
DESCRIPTION
TRANSCRIPT
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
top related