Small Profits and Quick Returns: A Practical Social Welfare Maximizing Incentive Mechanisms for Deadline-sensitive Tasks in Crowdsourcing
This is python code for the paper work published in IEEE Internet of Things Journal. You can access to the paper through this link.
- Language: Python
- Required Packages: numpy, pandas, matplotlib, munkres
- To install the required packages, type the following command:
- Python 2
pip install numpy pandas matplotlib munkres
- Python 3
pip3 install numpy pandas matplotlib munkres
- Class 'task': task.py, Class 'provider' or 'worker': provider.py, and Class 'platform': platform.py
- Since some of simulations may take quite a long time, simulation results are attached in 'data' directory.
- Visualization of the behavior of task
python3 task_figure.py
- Visualization of the punctual behavior of worker
python3 provider_figure.py
- Simulation for optimal task-worker matching
python3 optimal_run.py
- Visualization of the results of 3)
python3 optimal_visualization.py
- Simulation for one time competition between our work and the benchmark
python3 capacity_run.py
- Visualization of the results from 5)
python3 capacity_visualization.py
- Simulation for reselection process which consider the movement of workers and tasks between two platforms
python3 reselection.py
- Visualization of the results from 7)
python3 reselection_visualization.py
- Simulation for multiple reselection process which converges to balance point
python3 reselection_multiple_round.py
- Visualization of the results from 9)
python3 reselection_multiple_round_visualization.py
- Simulation for parameter setting
python3 parameter_run.py
- Visualization of the results from 11)
python3 parameter_visualization.py