SumanKumari30 / Temperature-Alert-using-IOT-ML

In industry temperature is a crucial parameter. All the activities which are taking place in industries need temperature but excessive temperature may causes harm or danger. So to prevent that we use IoT technology by which we can get a alert when temperature crosses a certain limit and also by using ML technology we can get a prediction of the temperature trend and can be get warned.
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I have added some styles in README.md file with markdown syntax to make it attractive, not changed anything in the contents. Thanks. #1

Open Krushna-Prasad-Sahoo opened 2 years ago

Krushna-Prasad-Sahoo commented 2 years ago

Temperature-Alert-using-IOT-ML

Ever thought that you receive a Alert message on your phone when Temperature cross threshold value in Industrial plant. Temperature Monitoring is the most important measurement parameters that is always used for monitoring and controlling in Industries.

Things used in our project:

Hardware required

  1. Bolt module
  2. Bread board
  3. Buzzer
  4. Male to female jumper wires
  5. LM35 Sensor

Software required

  1. Twilio
  2. Bolt cloud
  3. PyCharm IDE

Hardware Setup:

Step 1- Firstly We took Bread board for connections. Step 2- Just hold the LM35 sensor in a manner such that you can read LM35 written on it. Step 3- Then, you just need to identify the pins of the sensor as VCC,output and ground from your left to right. Step 4- Take male to female wire,connect the 3 pins of the LM35 to the bolt wifi module as follows: VCC pin of the LM35 connects to 5V of the Bolt wifi module. Ouput pin of the LM35 connects to A0(analog input pin) of the bolt wifi-module. Ground pin of the LM35 connects o the ground. Lastly, Take short pin of buzzer and connect it with ground and long pin with digital pin 0.

Software programming

SID = 'AC5645f61df50abd639026df881e89daa1' AUTH_TOKEN = 'e2024f1d108687efe19db34a74443bee' FROM_NUMBER = '+17032918270' TO_NUMBER = '+919749522710' API_KEY = '2f794fd6-f7e3-413c-8dbf-eff43f335184' DEVICE_ID = BOLT6907221'

NOTE - We store all the credentials in a separate file since it is sensitive data which should not be shared with anyone. Hence it is a good practice to avoid using credentials in code directly. After replacing all the values, save the file using CTRL+X.

The algorithm for the code can be broken down into the following steps -

Fetch the latest sensor value from the Bolt device. Check if the sensor value is in the range specified in our min and max values. If it is not in range, send the SMS. Wait for 10 seconds. *Repeat from step 1.

CODE EXPLAINATION:

FOR PREDICTION

  1. complete the connections as described in hardware setup, Power up the circuit and let it connect to the Bolt Cloud. (The Green LED of the Bolt should be on)
  2. Go to cloud.boltiot.com and create a new product. While creating the product, choose product type as Input Device and interface type as GPIO. After creating the product, select the recently created product and then click on configure icon.
  3. In the hardware tab, select the radio button next to the A0 pin. Give the pin the name 'temp' and save the configuration using the 'Save' icon.
  4. Move to the code tab, give the product code the name 'predict', and select the code type as js.
  5. Write the following code to plot the temperature data and run the polynomial regression algorithm on the data, and save the product configurations.

    setChartLibrary('google-chart');
    setChartTitle('Polynomial Regression');
    setChartType('predictionGraph');
    setAxisName('time_stamp','temp');
    mul(0.0977);
    plotChart('time_stamp','temp');
  6. In the products tab, select the product created and then click on the link icon. Select your Bolt device in the popup and then click the 'Done' button.
  7. Click on 'deploy configuration' button and then the 'view this device' icon to view the page that you have designed.
  8. Wait for about 2 hours for the device to upload enough data point to the Cloud. You can then click on the predict button to view the prediction graph based on polynomial regression algorithm.
Krushna-Prasad-Sahoo commented 2 years ago

You can go through it & merge in you repo.

SumanKumari30 commented 2 years ago

Sure will luk into it

On Sun 31 Oct, 2021, 11:39 PM Krushna Prasad Sahoo, < @.***> wrote:

You can go through it & merge in you repo.

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