Scalable-IOT-Simulation

Simulation for IOT Body area network.

Tech Stack

Technologies used in this simulation,

Python - For writing the code for all nodes and their features.
AMAZON SQS - For data communication.
AngularJS - For creating the dashboard.

And project is present in a public repository on GitHub.

Installation

This project requires Python3 to run.

Install Python3 and required pip packages.

$ pip install boto3

For dashboard NodeJS, AngularJS and its dependencies are required.

$ npm install

Code Structure

Directories

The directories listed inside iot package are discussed in brief here

Directory	Coontents
Scalable-Dashboard	Code for dashboard to display sensor data.
body1	Code for sensors and sink inside a body.
body1/sensors	Sensors and their condfiguration (config) according to their ids.
body1/sink	Sink and its configuration.
critical_edge_node	Code for edge device responsible for handling critical data.
edge_node	Code for edge device for handling normal data and scripts for uploading and downloading data to Amazon S3 Bucket.
utils	Communication utilities for AMAZON S3 and SQS.

Sample config of a sensor:-

{   "type": "Blood Pressure", 
    "battery": 65.37704081792825, 
    "duty_cycle": 1, 
    "data_rate": 2, 
    "status": true, 
    "transmission_cycle": 3, 
    "active_power": 0.0005, 
    "transmission_power": 0.0007, 
    "distance_to_sink": 7
}

Parameter Meaning

Battery is the remaining battery in percentage.

Duty cycle is represented in percentage. For example, 1 means 1 %.

Data Rate is bytes of data that can be transmiited per second.

Status represents if the sensor is on or off.

Transmission cycle is used to transmit data to forwarder node every nth cycle.

Active Power is power loss per second.

Transmission_power is power loss per second when sensor is sending data.

Setup and Execution

Create an account on AWS and generate private and secret key and store it in your system. Further information can be found here.
Create AWS S3 Bucket for storing data. Please update this bucket name in download_s3_file.py and upload_to_s3.py in iot/edge_node directory.
Create 4 AWS SQS FIFO Queues and while configuring the queues select Content Duplication.
- Queue1 - Sensor To Forwarder
  - Update in iot/body1/sensors/sensor.py
- Queue2 - Sensor To Sink
  - Update in iot/body1/sensors/sensor.py
  - Update in iot/body1/sensors/sink.py
- Queue3 - Sink to Edge
  - Update in iot/body1/sensors/sink.py
  - Update in iot/body1/edge_node/base.py
- Queue4 - Sink to Edge (Critical Data)
  - Update in iot/body1/sensors/sink.py
  - Update in iot/critical_edge/base.py

Execution

Run iot/edge_node/base.py to start edge node.
Run iot/edge/critical_edge/base.py to start the critical edge node.
Run iot/body1/sink/sink.py to start the sink node.
Set the status in the config file of the sensor you want to start and run its corresponding python script.
Once sensors are running and data has reached edge node, run iot/edge_node/upload_to_s3.py to upload the data to S3 bucket after updating the bucket name in the script.
To display the data on the dashboard run iot/edge/download_s3_file.py after updating the bucket name and file path for your system.
To view the data on the dashboard, move to the directory where AngularJS code is present.
Run ng serve --live-reload false for a dev server. Navigate to http://localhost:4200/. The app will not automatically reload if you change any of the source files.

Simulation ends here.