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.
This simplified code implements a DRL-based social-aware incentive mechanism to solve the optimal sensing strategy for all vehicles in vehicular crowdsensing.
You just need to install torch, numpy, random, csv, time, json, argparse by pip or conda
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
If you have any question, please email ynzhao@bit.edu.cn.
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}
}