Wireless Internet-connected sensor devices for weather data ...mwang2/projects/WSN_whether_13f.pdfDHT11 temperature and humidity sensor. We use Adafruit CC3000 breakout board to make

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

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

http://learn.adafruit.com/wifi-weather-station-arduino-cc3000/introduction

http://learn.adafruit.com/adafruit-arduino-lesson-1-blink

http://ruby.railstutorial.org/ruby-on-rails-tutorial-book

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Wireless Internet-connected sensor devices for weather data ...mwang2/projects/WSN_whether_13f.pdfDHT11 temperature and humidity sensor. We use Adafruit CC3000 breakout board to make