Awesome-Mobile-Crowdsensing

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Awesome-Mobile-Crowdsensing

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Research Resources on Human-Machine Collaborative Sensing by AI-Driven Unmanned Vehicles（边缘环境人机协同群智感知与决策关键技术研究）

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# Awesome-Mobile-Crowdsensing

This is a list of research resources on **Human-Machine Collaborative Sensing**
by AI-Driven Unmanned Vehicles, including related papers, simulation codes and default algorithms.

Prof. Liu often admonishes us with the phrase "**Talk is cheap, show me your code.**"
We should focus on making practical contributions to the Internet of Things and Artificial Intelligence communities.
To this end, this repository is continuously updated to help readers get inspired by our researches
(including **not only our papers but also our codes**), and realize their ideas in their own field.
To better align our research with industrial-grade applications, we always need to consider much more factors,
including larger scales, more agents, higher model training throughput, and faster simulation speeds.
Therefore, we explore several industrial tools and improve our somewhat outdated implementations of
our previous research. For example, we adopted Salesforce's distributed training framework,
called [Warp-Drive](https://catalog.ngc.nvidia.com/orgs/partners/teams/salesforce/containers/warpdrive),
an extremely fast end-to-end reinforcement learning architecture on a single or multiple Nvidia GPUs.

## Related Papers of our group
- [Ensuring Threshold AoI for UAV-assisted Mobile Crowdsensing by Multi-Agent Deep Reinforcement Learning with Transformer](https://ieeexplore.ieee.org/abstract/document/10181012)
- [Exploring both Individuality and Cooperation for Air-Ground Spatial Crowdsourcing by Multi-Agent Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/10184585)
- [Air-Ground Spatial Crowdsourcing with UAV Carriers by Geometric Graph Convolutional Multi-Agent Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/10184614)
- [Delay Sensitive Energy-Efficient UAV Crowdsensing by Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/9540290)
- [Human-Drone Collaborative Spatial Crowdsourcing by Memory-Augmented and Distributed Multi-Agent Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/9835559)
- [AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/9796732)
- [Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning](https://dl.acm.org/doi/abs/10.1145/3447548.3467070)
- [Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach](https://ieeexplore.ieee.org/abstract/document/9488791)
- [Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/9173810)
- [Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8821415)
- [Free Market of Multi-Leader Multi-Follower Mobile Crowdsensing: An Incentive Mechanism Design by Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8758205)
- [Energy-Efficient Mobile Crowdsensing by Unmanned Vehicles: A Sequential Deep Reinforcement Learning Approach](https://ieeexplore.ieee.org/abstract/document/8944303)
- [Energy-Efficient UAV Crowdsensing with Multiple Charging Stations by Deep Learning](https://ieeexplore.ieee.org/abstract/document/9155535)
- [Multi-Task-Oriented Vehicular Crowdsensing: A Deep Learning Approach](https://ieeexplore.ieee.org/abstract/document/9155393)
- [Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8676325)
- [Curiosity Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach](https://ieeexplore.ieee.org/abstract/document/9101657)
- [Blockchain-enabled Data Collection and Sharing for Industrial IoT with Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8594641)
- [Energy-efficient distributed mobile crowd sensing: A deep learning approach](https://ieeexplore.ieee.org/abstract/document/8664596)
- [Online Quality-Aware Incentive Mechanism for Mobile Crowd Sensing with Extra Bonus](https://ieeexplore.ieee.org/abstract/document/8502067)
- [Energy-efficient UAV control for effective and fair communication coverage: A deep reinforcement learning approach](https://ieeexplore.ieee.org/abstract/document/8432464)
- [Learning-based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities](https://ieeexplore.ieee.org/abstract/document/8207610/)

## Install up-to-date image
To always get the latest GPU optimized software,
we recommend you to use [Nvidia NGC](https://catalog.ngc.nvidia.com/orgs/partners/teams/salesforce/containers/warpdrive)
(which fully supports all kinds of Nvidia devices, such as A100, H100), the following:
```sh
docker build -t linc_image:v1.0 .
docker run -itd --gpus=all --network=host --name=mcs linc_image:v1.0 /bin/bash
docker exec -it mcs /bin/bash
```
In the NGC container, we first install our drl-framework.
```sh
conda create --name mcs python=3.9 --yes && source activate mcs
cd /workspace/movingpandas && python setup.py develop && cd ..
git clone https://github.com/BIT-MCS/Awesome-Mobile-Crowdsensing.git && cd Awesome-Mobile-Crowdsensing && pip install -e .
```
We recommend you do the following unittests to make sure the whole simulation and training architecture can run successfully.
```sh
python -m warp_drive.utils.unittests.run_unittests_pycuda
python -m warp_drive.utils.unittests.run_trainer_tests
python envs/crowd_sim/run_cpu_gpu_env_consistency_checks.py
```

Now you can start our simulation and baseline algorithms.
[Developing]
Currently, actual image used cannot be built with Dockerfile (because additional packages are needed).
we will update the Dockerfile to make it more customizable when time is allowed.
## Install Black Box Image

To have a glimpse of the framework,
We also provide a black box image for you to run our simulation and baseline algorithms.
```sh
# pull the image from aequatiospace directly, which is ready-to-use
docker pull aequatiospace/linc_image:v1.2.2
## QuickStart
Run random policy and debug the environment, and you will get a generated html file with randomly selected action:
```sh
python run_random_policy.py --plot_loop --moving_line --output_dir="./logs.html"
```
Train independent PPO as the baseline policies:
```sh
python train_rl_policy.py
```
Note, we should consider several things before running and debugging with the container
- We need to expose SSH port via a custom port
- We need to mount the host's localtime to the container to avoid time zone issues (because network interface is no
  longer synced)
- We need to use the `--gpus=all` flag to enable GPU support
- We need to mount an external storage for the container to store the logs and checkpoints
  Following these requirements, we can run the command as follows:
```shell
### In your Server Host ###
# start the container with all special customizations
# set your own log and checkpoints storage here, please set your program's logging dir as well
# replace your favorite port here with any thing larger than 1024
# ray support to set shm-size, 30% of the RAM is recommended
sudo docker run -itd \
--gpus=all \
--name=mcs \
-p <your-favorite-port>:22 \
-v /etc/localtime:/etc/localtime:ro \
-v /home/liuchi/.ssh:/root/.ssh \
-v /tmp/docker:/tmp \
-v /data2/saved_data:/workspace/saved_data \
--shm-size=221gb \
aequatiospace/linc_image:v1.2.3

# enter the docker with terminal
sudo docker exec -it mcs /bin/zsh
### Inside Container ###
# restart ssh within the docker and quit
/etc/init.d/ssh restart
### Back to Host ###
# Now you can happily login from the container just like any server!
ssh root@localhost -p <your-favorite-port>
```
Run the trained RL policy (Currently not elegant)
```sh
python run_rl_policy.py
```
Note that our drl-framework is based on [warp-drive](https://github.com/salesforce/warp-drive), an extremely fast end-to-end MARL architecture on GPUs.

[Developing] Do auto-scale, and drl-framework will automatically determine the best block size and training batch size
to use. It will also determine the number of available GPUs and perform training on all the GPUs.
```sh
python warp_drive/trainer_pytiotoporch.py --env tag_continuous --auto_scale
```
## Roadmap
- [X] Finish Simple Air-Ground Collaborative Simulation for MCS
- [X] Finish CUDA implementation
- [X] Finish PPO baseline
- [X] Add mobile users as PoIs
- [X] Add RLlib wrapper to support more popular RL baselines
- [ ] Try to improve the PPO in warp-drive (currently so naive.)
- [ ] Add Charging Stations with alive-and-dead settings (currently the electricity is unlimited)
- [ ] Add macro stragies of mobile users as fixed PoIs
- [X] Add our proposed key metrics for [emergency response](https://github.com/BIT-MCS/DRL-UCS-AoI-Threshold), inspired
  by Age of Information (AoI)
- [ ] Add apis for using available actions
- [ ] Add more realistic uav PnC mechanism, e.g., PD controller and A* planner (RL cannot do everything with one DNN.
  Traditional methods are essential to achieve low-level control with collision avoidance)
- [ ] Add more realistic environment dynamics, e.g., [4G MIMO](https://github.com/BIT-MCS/DRL-freshMCS), [5G NOMA](https://github.com/BIT-MCS/hi-MADRL)
- [ ] To be continued...

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