Wireless Internet-connected sensor devices for sensor devices for weather data analytics
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Wireless Internet-connected sensor devices for
weather data analytics
FINAL REPORT
COEN233- Computer Networks
FALL 2013
Mahitha Thokala Mithila Shekar Mayuri Chavan
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Table of Contents
1.Introduction....................................................................................................................4
1.1. Objective ...............................................................................................................4
1.2. Problem .................................................................................................................4
1.3. Why this is a project related to this class ..............................................................4
1.4. Why other approach is no good ............................................................................5
1.5. Why do you think your approach is better .............................................................5
1.6 Statement of the problem .......................................................................................5
1.7 Area or scope of investigation ................................................................................5
2. Theoretical bases and Literature review .......................................................................7
2.1. Definition of the problem ........................................................................................7
2.2. Theoretical background of the problem .................................................................7
2.3. Related research work ...........................................................................................7
2.4 Advantage/disadvantage of previous research .......................................................8
2.5 Solution to solve this problem .................................................................................8
2.6 where your solution is different from others ............................................................10
2.7 why your solution is better ......................................................................................10
3. Hypothesis ...................................................................................................................11
4. Methodology .................................................................................................................11
4.1. How to generate/collect input data ........................................................................11
4.2. How to solve the problem ......................................................................................11
4.3. How to generate output .........................................................................................12
5. Implementation .............................................................................................................13
6. Data analysis and discussion ........................................................................................15
7. Conclusions and recommendations ...............................................................................17
7.1. Summary and conclusions ......................................................................................17
7.2. Future enhancements .............................................................................................17
8. Bibliography
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TABLE OF FIGURES
Figure: 0 ...............................................................................................................6
Figure: 1 ...............................................................................................................9
Figure: 2 ...............................................................................................................9
Figure: 3................................................................................................................9
Figure: 4................................................................................................................10
Figure: 5...............................................................................................................15
Figure: 6...............................................................................................................16
Flow Chart 1 ........................................................................................................13
Flow Chart 2 ........................................................................................................14
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1. INTRODUCTION
1.1 Objective
In the fields of industry, agriculture and medicine, the information of temperature and humidity
need to be detected in real time. The transmission mode of traditional data sampling system
mostly adopts the wired mode. Because of the transmission distance and geography
environment, the wired mode causes high cabling costs ,more maintenance and poor
scalability . Thus the application of wired mode has been limited. WSN technology which
combines sensor, information processing and communication technologies has the
characteristics of low power consumption, low costs and so on, and also have wide
applications in environment data sampling, security monitoring, and target tracking. It is
important to solve the inefficiency of traditional wired monitoring mode and realizing the
convenient data sampling and data transmission. Also the ubiquitous nature of miniature
wireless sensors and rapid developments in the wireless network technology have
revolutionized home monitoring and surveillance systems. The new means and methods of
collecting data efficiently have led to novel applications for indoor wireless sensor networks.
The novelty of this project is to use real-time sensors installed in a subject’s home so that data
can be collected in a real environment and make it available for future machine learning.
1.2 What is the problem
Usually, the temperature collected and stored is not local to a particular place such as home or
office. For example- It is mostly to the entire city.
Even if the data collected is from a particular home/office, it is still not made available on the
cloud and therefore the data collected is limited for machine learning.
In our project, we plan to overcome this by deploying temperature sensors locally, at home or
office which will gather data from that particular place and store it on the server in the cloud.
Now, such data collected from hundreds of other homes are also available on the cloud which
make it more valuable and which enables complex data analytic algorithms.
1.3 Why this is a project related to this class
We are using temperature sensors deployed at home to collect sensor data. The data collected
is stored on the cloud and can be used for machine learning.
This project is related to class as it deals with the following-
TCP/IP based client.
WSN- Wireless Sensor Network applied to home/office environments.
WiFi connectivity is used for the sensors to talk to the Internet.
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Server is on the Internet(cloud) receiving data from the client.
1.4 Why other approach is no good
Most of the approaches use servers at home which have limited functionality.
Servers at home have processing power which is less.
Servers at home cannot be accessed from outside.
Servers at home are usually small computers that cannot handle large amount of data
collected.
1.5 Why do you think your approach is better
Our approach is better as we use servers which are in the cloud and collect large
amount of information from the sensors.
This can be used for machine learning.
It can also be accessed from outside for various other purposes.
1.6 Statement of the problem
In our project, we plan to deploy a few temperature sensors locally, at home or office which will
gather data from that particular place and store it in the server on the cloud. This enables us to
perform complex data analytic algorithms.
1.7 Area or scope of investigation
1. Wireless sensor devices
2. Embedded devices
3. Web frameworks
4. Cloud computing
5. Data analytics
6. WiFi communication
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ARCHITECTURE
FIGURE: 0
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2. THEORETICAL BASES AND LITERATURE REVIEW
2.1 definition of the problem
In our project, we are deploying a sensor at home, where we wish to monitor the temperature
changes in a home over a period of time. This temperature data is then made available on
cloud, which has added advantages over a local server like accessibility, large amount of data
handling & large process handling power.
2.2 theoretical background of the problem
Traditionally, for temperature sensing using wireless sensors, data is collected on local PC and
then it is processed. So one has to look critically for the PC configuration as to how much data
it can handle and also how much would be the processing time.
But in this project, we are going to use the web server. So, one has nothing to worry about the
large amounts of data handling and processing time, etc.
2.3 related research to solve the problem
Our work takes inspiration from past research on Wireless Sensor Network Based Smart
Home: Sensor Selection, Deployment and Monitoring .
This research deals with the installation and configuration of unobtrusive sensors in an elderly
person’s house - a smart home . The overall system is envisaged to use machine learning to
analyze the data generated by the sensor nodes. The novelty of this project is that instead of
setting up an artificial test bed of sensors, the sensors have been installed in a subject’s home
so that data can be collected in a real, not artificial, environment.
We are building on this previous work and expanding the system architecture to be a cloud
hosted solution. Considering the time limitations of the project, we are going to limit our
sensing parameters to temperature and humidity while identifying sensors for electricity,
motion, water flow etc. as future scope for the project.
We are deploying sensors in a subject’s home to monitor temperature and humidity changes
over a period of time.
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2.4 Advantage/disadvantage of previous research
Advantages:
Unlike other research projects, intrusive sensors such as cameras and microphones
have not been used. The system completely relies on unobtrusive sensors for data.
Also, internet based servers are used to overcome the limitation by using the local
server, wherein the client talks to the server through ISP (Internet service provider)
2.5 Solution to solve this problem
We are going to make use of Arduino micro controllers board as the main controller for the end
device. A TI CC3000 WiFi module to provide connectivity to the Internet.
A DHT11 temperature and humidity sensor for sensing purposes.
A brief description of the various components is provided below:
Arduino: Arduino is a tool for making computers that can sense and control the physical world.It
is an open-source physical computing platform based on a simple MCU board, and a
development environment for writing software for the board.
CC3000: CC3000 is WiFi chip from TI needed for WiFi communication. This is all in module
has low power WiFi and micro controller friendly interface.
DHT11: DHT11 is a temperature & humidity sensor
We need to program these devices as per our application.
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FIGURE 1
`
FIGURE 2: TEMPERATURE SENSOR
FIGURE 3: WI-FI CHIP
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FIGURE 4: SUMMARY OF CONNECTIONS
The above connected module (Arduino+CC3000+DHT11) i.e client is connected to the home
router(Access Point). The AP now talks to the Internet via local ISP(Internet Service Provider).
The web server is run in the Heroku cloud.(A Cloud Platform).
2.6 where your solution is different from others
As mentioned earlier, we are going to use web based server for data collection. So the life
would be easy even if we have to handle huge amount of data.
Also the hardware components that we are going to use are energy efficient (WiFi chip, DTH11
sensor).
2.7 why your solution is better
We can use the data collected to analyze the air quality at homes and monitor the day
to day weather changes at a granular level.
We are using Arduino microcontroller which offers many advantages over other
microcontrollers like inexpensive, simple, clear platform for programming, cross
platforms, open source and extensible hardware/software.
We are going to use web server, running on the Heroku cloud which will eliminate the
main obstacle of handling huge data.
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3. HYPOTHESIS
Our hypothesis is to demonstrate the concept of Internet of things(IoT) where embedded
devices are programmed to sense and collect temperature data from homes and store them on
the cloud.
This data made available on the cloud can be used for machine learning of weather changes,
air quality and used for predictions and precautions.
4.METHODOLOGY
4.1 How to generate/collect input data -
Input is the sensor data consisting of temperature and humidity at home using DHT11
sensor.
4.2 how to solve the problem
- design
Our design includes Arduino Uno board, Adafruit CC3000 breakout board and
DHT11 temperature and humidity sensor.
We use Adafruit CC3000 breakout board to make the WiFi communication which uses SPI for
communication so you can push data as fast as you want or as low as you want.It has a proper
interrupt system with IRQ pin so you can have synchronous connections. It supports 802.11b/g,
open/WEP/WPA/WPA2 security, KIP & AES.
A built in TCP/IP stack with a "BSD socket" interface. TCP and UDP in both client and server
mode, up to 4 concurrent sockets. It can connect to an Access point.
-languages used
C language is used for programming the embedded device and Ruby on Rails for web
application.
- tools used
Postgre SQL Database is used to store the data collected from sensor.
Heroku Cloud as Infrastructure as service for Running Web application(i.e. deployed to Apache
web server).
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4.3 how to generate output
Open the HTML page on the web browser, i.e client is requesting the temperature and humidity
data. Client connects to Apache web server running on Heroku cloud which retrieves the data
stored in Postgre SQL Database.
The module(Arduino+CC3000+DHT11) has the data of temperature and humidity which is
stored in Postgre SQL Database.
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5. IMPLEMENTATION
FLOW CHART: 1
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6. DATA ANAYLSIS AND DISCUSSION
FIGURE 5: SCREENSHOT SHOWING TEMPERATURE DATA COLLECTED ON THE HTML PAGE
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FIGURE 6: DATAPOINTS GRAPH – TEMPERATURE IN CELCIUS AND TIME IN UTC FORMAT
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7. CONCLUSION AND RECOMMENDATIONS
7.1 Summary and Conclusions
We have built a simple WiFi-connected weather station ,finding the temperature and humidity
at any given location (i.e. home or office) and stores it in the cloud to be able to access
remotely.
Big data refers to the massive amounts and varieties of information, particularly in unstructured
form, being generated by websites, sensors, social media and other sources.
While programming frameworks such as Hadoop are widely used to analyze these large data
sets, vendors are moving quickly to provide big data applications for various industries that
take care of the heavy lifting combining both wireless sensor networks and Big data analytics,
we want to predict temperature and humidity into the future(say more than 30 days).
7.2 Future Enhancements
This project can be further extended to sense various other kind of sensor data such as humidity, light intensity, air quality etc.,
8. BIBLIOGRAPHY
1. Wireless Sensor Network Based Smart Home: Sensor Selection, Deployment and
Monitoring.
- Debraj Basu, Giovanni Moretti, Gourab Sen Gupta, Stephen Marsland
School of Engineering and Advanced Technology Massey University, New Zealand
{D.Basu, G.Moretti, G.Sengupta, S.R.Marsland}@massey.ac.nz
2. A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic,
“ALARM-NET: Wireless Sensor Networks for Assisted-Living and Residential Monitoring”,
Technical Report CS- 2006-13, Wireless Sensor Network Research Group, Department of
Computer Science, University of Virginia.
3. D. Estrin, R. Govindan, J. Heidemann, and S. Kumar, BNext century challenges: Scalable
coordination in sensor networks,[ in Proc. 5th Annu. ACM/IEEE Int. Conf. Mobile Comput.
Netw., 1999, pp. 263–270.
4. http://arduino.cc/en/Guide/Introduction
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5. http://learn.adafruit.com/wifi-weather-station-arduino-cc3000/introduction
6. http://learn.adafruit.com/adafruit-arduino-lesson-1-blink
7. http://ruby.railstutorial.org/ruby-on-rails-tutorial-book