IDEAL2018-An-Artificial-Bee-Colony-Algorithm-for-Optimizing-the-Design-of-Sensor-Networks

Code and data for the article "An Artificial Bee Colony Algorithm for Optimizing the Design of Sensor Networks"

DOI: 10.1007/978-3-030-03496-2_35

Citation

@inproceedings{panizo2018artificial,
  title={An Artificial Bee Colony algorithm for optimizing the design of sensor networks},
  author={Panizo, {\'A}ngel and Bello-Orgaz, Gema and Carnero, Mercedes and Hern{\'a}ndez, Jos{\'e} and S{\'a}nchez, Mabel and Camacho, David},
  booktitle={International Conference on Intelligent Data Engineering and Automated Learning},
  pages={316--324},
  year={2018},
  organization={Springer}
}

Requisites

Having installed docker (https://www.docker.com/) and docker-compose (https://docs.docker.com/compose/install/).

Running

1. clone the repository.
2. go to folder with the repository and run "docker-compose up".
3. In the terminal find the line "Copy/paste this URL into your browser when you connect for the first time,to login with a token:" copy the url and paste it un your broswer.
4. open host_data->notebooks->*.ipynb" to run the different experiments.

Structure

Inside jupyter home is the proyect structure:
├── historic_evolution
├── Hive
├── notebooks
│   ├── Case1_evolution.ipynb
│   ├── Case_1_experiments.ipynb
│   ├── Case2_evolution.ipynb
│   ├── Case_2_experiments.ipynb
│   ├── Case3_evolution.ipynb
│   └── Case_3_experiments.ipynb
├── sources
│   ├── mongo_connection
│   ├── plotting
│   ├── problem_formulation
│   ├── parallel_executions.py
│   ├── SensorNetworkDesignABC.py
│   └── settings.py
└── test

• sources: contains the code of the proyect.
• Hive: Custom Hive library for the ABC optimization.
• historic_evolution: contains the output pictures of the an execution evolution.
• notebooks: this folder stores the notebooks that allows to run the different experiments. The ones called evolution generates pictures with the fitness of the ABC for each iteration.
• test: this folder stores python unittest files.

Sources estructure

• mongo_connection: contains all the code related to experiment execution/storage/loading
• plotting: code related to plot different metrics.
• problem_formulation: code for the three different SNDP that are tested, includes the feasibility tests.
• parallel_executions.py: code for loading several experiments in different threads.
• SensorNetworkDesignABC.py: code for the ABC for solving the SNDP.
• settings.py: python class for configuring the ABC for solving the SNDP