This repository is the official implementation of Online Context-Aware Task Assignment in Mobile Crowdsourcing via Adaptive Discretization, published at IEEE TNSE.
To install requirements:
pip install -r requirements.txt
We use the gpflow library for all GP-related computations and gpflow uses tensorflow. Our code uses the TIM+ algorithm, for which you must link the C++ TIM+ code to Python. Follow here for linking instructions. Once the library has been generated, place it both in the root directory where main.py is and also inside the tim_plus directory.
We ran a total of three simulations. Moreover, none of the algorithms that we implement and test do offline-learning, thus there is no 'training' to be done. However, to be able to repeat the simulations and also improve speed, we first generate the arm contexts, rewards, and other setup-related information and save them as HDF5, in the case of Simulation I and Simulation II, and pickled DataFrames, in the case of Simulations III. By default, when you run the script (main.py), it re-generates new datasets and runs the simulations on them.
To run Simulation I, provide the argument simple_uni to the main.py script for uniform context arrivals and simple_nuni for non-uniform arrivals.
To run Simulation II, use the argument dpmc. You can provide the --use_saved_sim argument to use pre-generated and saved datasets.
To run Simulation III, use the argument gp. You can provide the --use_saved_sim argument to use pre-generated and saved datasets.