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Small Profits and Quick Returns: An Incentive Mechanism Design for Crowdsourcing under Continuous Platform Competition,

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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.

Prerequisites

  • Language: Python
  • Required Packages: numpy, pandas, matplotlib, munkres
  • To install the required packages, type the following command:
  1. Python 2
pip install numpy pandas matplotlib munkres
  1. Python 3
pip3 install numpy pandas matplotlib munkres

Running the code

  • 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.
  1. Visualization of the behavior of task
python3 task_figure.py
  1. Visualization of the punctual behavior of worker
python3 provider_figure.py
  1. Simulation for optimal task-worker matching
python3 optimal_run.py
  1. Visualization of the results of 3)
python3 optimal_visualization.py
  1. Simulation for one time competition between our work and the benchmark
python3 capacity_run.py
  1. Visualization of the results from 5)
python3 capacity_visualization.py
  1. Simulation for reselection process which consider the movement of workers and tasks between two platforms
python3 reselection.py
  1. Visualization of the results from 7)
python3 reselection_visualization.py
  1. Simulation for multiple reselection process which converges to balance point
python3 reselection_multiple_round.py
  1. Visualization of the results from 9)
python3 reselection_multiple_round_visualization.py
  1. Simulation for parameter setting
python3 parameter_run.py
  1. Visualization of the results from 11)
python3 parameter_visualization.py

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Small Profits and Quick Returns: An Incentive Mechanism Design for Crowdsourcing under Continuous Platform Competition,

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