This project includes all the code used for the creation of the figures, as well as for the simulations of : "Emission Strategies to Optimize Monitoring".
All the code is writen in Python. The packages used are :
math, matplotlib, numpy, random, statistics
All the parameters fixed in the paper are represented in this file.
The module named 'simulation" is a generic module to simulate emissions of sensors, according to an period update function (here the function is the file "f_M_tau_function_and_plots/f_M_tau.py"), and provide the performance metrics.
Represent the function f_M_tau and all the function helping for the plots of the refered paper.
In ordre to simulate the type of examples presented in the definitions of the period update function.
Run the file :
using_of_Cycle_M_and_vizualisation/toy_example.py
You will be asked for the parameters to be set, and the function will display an illustration of the emissions.
You will file the data-base named "json_files/data_base_for_choice_of_f.json". It is already in the reppository. Otherwise, you can run :
using_of_Cycle_M_and_vizualisation/filling_json_db.py
By default, it initialise the json file (function "initialisation_of_json_file(json_to_fill)"), then fill it with the metrics of time of monitoring and diversity penalty for functions with parameters of M and tau :
M_list = [1, 2, 3, 5, 10, 15, 20, 30, 40, 50, 75, 100, 125, 150, 200]
tau_list = [0.5 + 0.05 * i for i in range(250)]
By default, it will show the data base following the order of the figures shown in the paper, from the part named "simulation".
These modules are used for further publications.
Gwen MAUDET, IMT Atlantique, IRISA, OCIF
Mireille BATTON-HUBERT, EMSE
Patrick MAILLE, IMT Atlantique, IRISA, Dyonisos
Laurent TOUTAIN, IMT Atlantique, IRISA, OCIF