DRL-SIM

This is the code accompanying the paper: "Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning" by Yinuo Zhao and Chi Harold Liu, published at TITS.

Description

This simplified code implements a DRL-based social-aware incentive mechanism to solve the optimal sensing strategy for all vehicles in vehicular crowdsensing.

Dependencies

You just need to install torch, numpy, random, csv, time, json, argparse by pip or conda

Usage

To generate E-R social graph, first you need to config the variable mu and user_number in generate_data.py. And then run the following command by

python generate_data.py

Then, copy the value of E-R social graph into self.V['relationship'] in env_setting.py, and config other environment parameters there.

After that, run the training and testing process by

python train.py --root-path [PATH to where to save results file and model] --user-num [USER NUMBER]

Finally, find the training and testing results under --root-path

Contact

If you have any question, please email [email protected].

Paper

If you are interested in our work, please cite our paper as

@article{zhao2020social,
  title={Social-aware incentive mechanism for vehicular crowdsensing by deep reinforcement learning},
  author={Zhao, Yinuo and Liu, Chi Harold},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  volume={22},
  number={4},
  pages={2314--2325},
  year={2020},
  publisher={IEEE}
}