SCIoT project

The Split Computing on IoT (SCIoT) project provides tools to use Edge Impulse models in ESP32 devices, using split computing techniques.

Publications

If you use this work, please consider citing our work:

F. Bove, S. Colli and L. Bedogni, "Performance Evaluation of Split Computing with TinyML on IoT Devices," 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2024, pp. 1-6, DOI Link.
F. Bove and L. Bedogni, "Smart Split: Leveraging TinyML and Split Computing for Efficient Edge AI," 2024 IEEE/ACM Symposium on Edge Computing (SEC), Rome, Italy, 2024, pp. 456-460, DOI Link.

Clone the repository and navigate into it:

git clone https://github.com/UBICO/SCIoT.git
cd SCIoT

Create the virtual environment and install the dependencies:

uv sync

Activate the virtual environment:

source .venv/bin/activate

Save your keras model as test_model.h5 in src/server/models/test/test_model/
Save your test image as test_image.png in src/server/models/test/test_model/pred_data/
Split the model by running python3 model_split.py from src/server/models/
Configure the paths as needed using src/server/commons.py

From the repository's root directory, activate the virtual environment:

source .venv/bin/activate

Start the MQTT broker:

docker compose up

Run the edge server:

python3 src/server/edge/run_edge.py

Run the analytics dashboard:

streamlit run src/server/web/webpage.py