Commit bbaffb7a authored by Nicolas Seydoux's avatar Nicolas Seydoux
Browse files

Added some documentation

parent ccd19408
......@@ -4,6 +4,18 @@
This project aims at evaluating the EDR algorithm proposed by Nicolas SEYDOUX, Khalil DRIRA, Nathalie HERNANDEZ and Thierry MONTEIL. It spawns a set of processes communicating with each other in HTTP simulating an IoT network, and applications consuming data produced by the network.
## Getting started
After having cloned the repository main branch that should be functionnal (since no one ever breaks the production environment, right ?), a few additional steps are required to get EDR up and running.
### Additional libraries
EDR is a maven project, so most of its dependencies are managed seamlessly. However, one of them is a custom library that is not available on the maven central repository. You can collect it with the command `git clone https://framagit.org/IRIT_UT2J/ontology-tools-sandbox.git`, and deploying it to the local maven repository by executing the command `mvn install` in the downloaded folder.
### Packaging the sensors
In the `sensor-mockup` directory, execute the command `mvn compile assembly:single`. A runnable jar is generated: `target/sensor-mockup-0.1-jar-with-dependencies.jar`. Copy this archive in the root of the EDR folder, renaming it `sensor.jar`. Add the `log4j2.xml` file into the `sensor.jar` archive (`jar uf sensor.jar log4j2.xml` via command line).
## Creating a topology
A topology is described in a json file. Basically, it contains the different nodes, assuming a hierarchical distribution, and the different sensors. Reference topologies are available in the `simulator` folder. A predefined set of sensor types can be used (see `simulator/generator.py`). Future works will involve replaying a real dataset.
......@@ -18,4 +30,4 @@ The simulation is launched by the `initializer.py` script, with two arguments: t
## Getting results
The result script `simulator/results.py` computes the average time between the production of observations, and the time where these observations are used for a deduction, as well as the moment when they are received by target applications.
The result script `simulator/results.py` computes the average time between the production of observations, and the time where these observations are used for a deduction, as well as the moment when they are received by target applications.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment