The repository contains the code and dataset link of the paper "A Framework for Spectrum Classification using Crowdsensing Platforms" by A. Scalingi, D. Giustiniano, R. Calvo-Palomino, N. Apostolakis and G. Bovet. Please cite the paper if you plan to use it in your publication.
We propose a framework that relies solely on Power Spectrum Density (PSD) data collected by low-cost RTL-SDR receivers. This release contains the backend application of the framework developed with Flask which consist in two main packages. First, the unsupervised transmission detection that works with PSD data already collected by the backend of the Electrosense platform and that provides stable detection of transmission boundaries. Second, the data-driven deep learning solution to classify the radio frequency communication technology used by the transmitter, using transmission features in a compressed space extracted from single PSD measurements over at most 2 MHz band for inference.
We release the lightweight version of the framework as Docker container.
We release the compressed version of the dataset that contains the measurements of real-world data collected from 47 different sensors deployed across Europe.
To access to the dataset: https://zenodo.org/record/7521246
If you encounter any issue with Zenodo please write an email to: [email protected] with subject "Access PSD Dataset - [Your Name]"
DOI: 10.5281/zenodo.7521246.
In order to run the repository you need the following software:
- Python 3
- Docker Engine
- Docker Compose
Install Docker Engine https://docs.docker.com/engine/install/ubuntu/
Install Docker Compose https://docs.docker.com/compose/install/
After docker installation run the container with the following command docker compose up.
The container will expose the port 5005.
After run the container make sure that the backend is running: (1) Open the browser and (2) Type localhost:5005. If "App is Running" shows up then the backend is running correctly. The backend expose the API to run the technology classification pipeline. Without the frontend, test the technology classification through curl with:
curl -i -H "Content-Type: application/json; charset=utf-8" -X POST -d '{"snsid" : "202481596708292", "snsname" : "rack_3", "month" : "May", "day" : "1" , "nation" : "Esp", "technology" : "test", "startf" : "20", "endf" : "1500", "freq_start":"20000000", "freq_end":"1500000000" }' http://localhost:5005/services/api/v1.0/usertc/pipeline
BSD 3-Clause License
Copyright (c) 2023, Alessio Scalingi, All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that
the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions, and the
following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions, and
the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote
products derived from this software without specific prior written permission.
4. When using this software, you are required to cite the following paper:
A Framework for Spectrum Classification using Crowdsensing Platforms
A. Scalingi, D. Giustiniano, R. Calvo-Palomino, N. Apostolakis and G. Bovet
2023
DOI 10.1109/INFOCOM53939.2023.10228867
@INPROCEEDINGS{10228867,
author={Scalingi, Alessio and Giustiniano, Domenico and Calvo-Palomino, Roberto and Apostolakis,
Nikolaos and Bovet, Gérôme},
booktitle={IEEE INFOCOM 2023 - IEEE Conference on Computer Communications},
title={A Framework for Wireless Technology Classification using Crowdsensing Platforms},
year={2023},
volume={},
number={},
pages={1-10},
doi={10.1109/INFOCOM53939.2023.10228867}
url=}
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