PyIncentiveBC

PyIncentiveBC is a Python implementation of "Rewards and Penalties" approaches used in Blockchain-based systems.

Overview

PyIncentiveBC allows researchers and developers to simulate and compare different reward and penalty mechanisms in blockchain systems. It provides a flexible framework for evaluating the effectiveness of various incentivization approaches.

Quick Start

Getting Started for Beginners :

For users new to Python or blockchain technology, we recommend using PyIncentiveBC through CodeOcean, which requires no installation:

1. Access CodeOcean

   • Open your web browser and go to the PyIncentiveBC CodeOcean capsule
   • You my be need to create an account to run the simulation.

2. Explore the Capsule

   • On the left side, you'll see a file browser showing the project structure
   • The main file we'll be working with is PyIncentiveBC_demo.ipynb

3. Run the Simulation

   • Click on the "Reproducible Run" button at the top of the page
   • This will execute the default simulation using pre-set parameters

4. View Results

   • After the simulation completes, you'll see the output displayed in the notebook
   • This includes graphs comparing different incentivization approaches

Congratulations! You've run your first PyIncentiveBC simulation. Remember, you can always come back to this capsule to run more simulations or explore the code further.

For Advanced Users :

For advanced users or those who prefer local installation, follow these steps:

1. Clone the repository:

git clone https://github.com/ouaguid/pyIncentiveBC.git

2. The package has been tested for Python 3.7.3. Required packages are available in requirements.txt. Install required packages:

pip install -r requirements.txt

3. Run the example Jupyter notebook:

jupyter notebook PyIncentiveBC_demo.ipynb

Example

An example Jupyter notebook is available in PyIncentiveBC_demo.ipynb

CodeOcean capsule

To check this software running you can access the codeocean capsule via: https://codeocean.com/capsule/2547976/tree/v3

Citation

If you use PyIncentiveBC in your work, please cite the following publications:

Ouaguid, Abdellah, Mohamed Hanine, Zouhair Chiba, Noreddine Abghour, and Mohammed Ouzzif. 2024. "PyIncentiveBC: A Python Module for Simulation of Incentivization Mechanism Implemented in Blockchain-Based Systems" Computation 12, no. 9: 179. https://doi.org/10.3390/computation12090179

A. Ouaguid, N. Abghour, M. Ouzzif, Towards a new reward and punishment approach for blockchain-based system, in: 2019 International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), IEEE, 2019, pp. 1–7.

As BibTeX:

@Article{computation12090179,
AUTHOR = {Ouaguid, Abdellah and Hanine, Mohamed and Chiba, Zouhair and Abghour, Noreddine and Ouzzif, Mohammed},
TITLE = {PyIncentiveBC: A Python Module for Simulation of Incentivization Mechanism Implemented in Blockchain-Based Systems},
JOURNAL = {Computation},
VOLUME = {12},
YEAR = {2024},
NUMBER = {9},
ARTICLE-NUMBER = {179},
URL = {https://www.mdpi.com/2079-3197/12/9/179},
ISSN = {2079-3197},
DOI = {10.3390/computation12090179}
}

@inproceedings{ouaguid2019towards,
	title={Towards a New Reward and Punishment Approach for Blockchain-based System},
	author={Ouaguid, Abdellah and Abghour, Nourdine and Ouzzif, Mohamed},
	booktitle={2019 International Conference on Systems of Collaboration Big Data, Internet of Things \& Security (SysCoBIoTS)},
	pages={1--7},
	year={2019},
	organization={IEEE}
}

Support

If you encounter any issues or have questions, please open an issue on GitHub or contact us at [[email protected]].

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