IoT-sensor-data-anomaly-detection

This repository contains a Streamlit app for detecting anomalies in IoT sensor data using an LSTM-based machine learning model. The app allows users to upload sensor data, process it, and visualize detected anomalies. https://iot-sensor-data-anomaly-detection-zqbd3yzrtsnnhnwhbm4bcs.streamlit.app/

References

Data source: https://github.com/hkayann/grove-dataset-generation Description as taken from the source:

• Contains humidity, temperature, light, loudness, and air quality data in order.
• Environment is 25 m2 studio room contains 2 people.
• Data is collected from 10/03/2021 18:36 PM to 11/03/2021 18.36 PM.
• Data might be considered as normal, there are no anomalies created on purpose.
• The groveHighAccTempDataset contains timestamp + temperature data. Environment is the same.

Features

• Upload a custom CSV file or use the default dataset provided.
• Visualize IoT sensor data, including Temperature, Humidity, Air Quality, Light, and Loudness.
• Highlight detected anomalies in the sensor data using a machine learning model.
• Download detected anomalies in CSV format.
• Interactive and clean UI with progress bars, sidebar navigation, and custom styling.

Tech Stack

• Streamlit: For creating the web interface.
• TensorFlow/Keras: For training and running the LSTM anomaly detection model.
• Matplotlib: For plotting sensor data and anomalies.
• Pandas: For data manipulation and CSV handling.

Installation

Prerequisites

• Python 3.8 or higher
• Git installed on your local machine

Clone the repository

git clone https://github.com/ACSE-vg822/IoT-sensor-data-anomaly-detection.git
cd IoT-sensor-data-anomaly-detection-iot
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
streamlit run streamlit_app.py

Project Structure

IoT-sensor-data-anomaly-detection/
│
├── src/                         
│   ├── pipeline/
│   │   └── predict_pipeline.py   
│   └── components/ 
│   │    ├── data_ingestion.py 
│   │    └── model_trainer.py 
│   ├── exception.py
│   └── logger.py
│
├── artifacts/                    
│   ├── model_new.keras             
│   └── data.csv                  
├── streamlit_app.py               
├── requirements.txt
├── setup.py             
└── README.md                      

Feel free to open issues and contribute to this project!