SHAS

A smart home automation system with voice control and user authorization

Team members

Name	TAMUCC ID	Email
Venkata Sai Pavan Arepalli	A04268973	varepalli@islander.tamucc.edu
Siva Parvathi Chirumamilla	A04269507	schirumamilla2@islander.tamucc.edu
Ramya Reddy Dhudipala	A04267473	rdhudipala@islander.tamucc.edu
Venkata Hemanth reddy manchala	A04269074	hmanchala@islander.tamucc.edu

 

We divided our project into 4 major modules in regards with code.

• Android App - The UI of the application
• Machine learning module - Authorizing speaker
• Python Flask Api - Api used as a connection between android app and Ml module - hosted in ubuntu server
• Arduino code - Code that runs on ESP8266 chip, works with and controls electronic appliances, according to the codes received from Android App.

Execution and testing

• Pre- requisites
  • Java installed
  • Python installed
  • Android studio IDE to run the Android app code
  • Jupyter book or Google Colab to train the model.
  • Pycharm IDE installed.
  • Arduino IDE installed.
  • A working NodeMcu ESP8266.
    • 3 LED'S
    • One bread board.
    • 10 Jumper cables (Male - Male)
  • Ubuntu or any linux server with python installed to serve flask Api.
  • We have one already installed and running on http://142.93.57.145:5000
  • Mosquitto or any other MQTT broker - connection between android device an IOT device.
  • We have one already installed and running on http://142.93.57.145:1883
  • Downloaded source code.

Android app execution:

• Open the android project in IDE, and wait for gradle sync.
• Build and run apk file or use the already built apk file included with source code.

Ml execution:

• Open the code in python folder from downloaded source code using Jupyter notebook or Google Colab.
• Install Required modules.
• Execute Preprocessingfile.ipynb File to extract features.
• Execute ModelTraining_ANN.ipynb File to build the model.
• Execute TestFile.ipynb File to test the model/system built.

Things to do:

• Change all the paths in code as per your directory.
• Four options are there when you run Speakerverification.py
• i) Record audio for training
• ii) Train Model
• iii) Record Audio for Testing
• iv) Test Model(Follow the same order).
• Store your audio recorded for training in training_set and testing audio in testing_set Folder.

Python Flask Api execution:

• Open shas-flas folder in PyCharm IDE
• Install all requirements from requirement.txt file
• Run the code.

Arduino app execution:

• Open the sketch from Arduino folder from downloaded source code.
• Install the libraries Arduinowebsockets, MD_MAX72XX, PubSubClient, and WebSockets.
• Attach the NODE MCU to the computer's USB port.
• Connect the LED's and cable to the device as shown in the picture, added with arduino code.
• Edit in the code and added the following details:
  • Wi-Fi login information
  • mqtt_server information
  • and MQTT port information.
• Save the file and upload the sketch to the ESP8266 chip.
• You may view the serial monitor window for output.
• It displays the Wi-Fi and MQTT connection information after the sketch has been uploaded.

Now that everything is set up please open the Android app and give commands.

Commands supported as of now:

Open access - Any once can give these commands:

• Turn on bedroom lights
• Turn off bedroom lights
• Turn on living room lights
• Turn off living room lights

Restricted access - Only parents have access to these commands:

• Open garage door
• Close garage door
• Open main door
• Close main door