This repository contains the code for the final project of the 2016/2017 course in Wireless Communications. The project consists in studying and implementing some Fountain Codes Based algorithms and testing their performances in a WSN simulator. The implemented algorithms can be found at these links: http://ieeexplore.ieee.org/document/4215776/ [1] http://ieeexplore.ieee.org/document/4505472/ [2]
- Any Operating systems (tested in Windows 10, and MACOS X 10.11.6)
- Python 2.7. For used libraries, see source code
Each file is representative of one developed algorithm. In Dati folder is present some auxiliary data used for EDFC and the Mathematica code used to solve the optimization problem.
The WSN is created as a Random Geometric Graph (RGG). An exemple of RGG can be found in plot_grafo.py. Sensor nodes are objects of class Sensor, which extends the more general class Storage. In the file Node.py can be found all the specifications about the two classes.
All the algorithms are differentied in how they disseminate, collect, encode and store packets, but all of them use message_passing.py to decode data.
In RSD.py is present a python implementation of the Robust Soliton Distribution generation and sampling.
Simply run python EDFC.py for the first algorithm in [1].
In the main you can find:
```comparison_network_dimensions(iteration_to_mediate, L_RW, mp): ```
plot decoding performances for different network deployments, specified in main. Choose the number of iterations to mediate, the random walk length and the number of message passings.
```comparison_solution_opt_problem(iteration_to_mediate, L_RW, n0, k0, mp)```
plot decoding performances changing the solution for redundancy coefficients. Choose the number of iterations to mediate, the random walk length, number of
nodes and sesnors and message passings.
```comparison_length_of_random_walk(iteration_to_mediate, punti, n0, k0, mp)``
Comparison for several length of the random walk. Choose the number of iterations to mediate, number of nodes and sesnors and message passings.
```dissemination_cost(iteration_to_mediate)```
plot the time complexity of the algorithm
Simply run LTCDS-I.Py for the first algorithm in [2].
In the main you can find:
```comparison network dimensions(iteration_to_ mediate,C1,MP)```
plot decoding performances for different network deployments, specified in main. Choose the number of iterations to mediate, C1 and
the number of message passings.
```comparison_C1(iteration_to_mediate, MP)```
plot the system beahviour for several values of system parameter C1. Choose the number of iterations to mediate and message passings
```figure_5(iteration_to_mediate, MP)```
plot figure 5 of [2]. Choose the number of iterations to mediate and message passings
```dissemination_cost(iteratin_to_mediate)```
plot the time complexity of the algorithm
Simply run LTCDS-II.py, for the second algorithm in [2].
In the main you can find:
```comparison network dimensions(iteration_to_ mediate, C2, C3, MP)```
plot decoding performances for different network deployments, specified in main. Choose the number of iterations to mediate, C1, C2 and
the number of message passings.
```comparison_C2(iteration_to_mediate, MP)```
plot the system beahviour for several values of system parameter C2. Choose the number of iterations to mediate and message passings
```comparison_C3(iteration_to_mediate, MP)```
plot the system beahviour for several values of system parameter C3. Choose the number of iterations to mediate and message passings
```dissemination_cost(iteratin_to_mediate)```
plot the time complexity of the algorithm