MQTT-PRE

This repository is for reproducibility of the experiments presented in the paper: MQTT-E: E2E encryption in MQTT via Proxy Re-encryption avoiding Broker Overloading

Reproducing the Figures

Figures 2, 3, 4, and 5

To generate Figures 2, 3, 4, and 5, use the script SimulationCriteria.py. This script allows you to simulate the required scenarios by setting the following variables:

• sigma: Experimental condition, set to 0 or 10.
• s: Scenario setting, set to 0 or 1.

By running the script with these combinations of sigma and s, you will obtain the respective figures:

• (sigma=0, s=0): Figure 2
• (sigma=0, s=1): Figure 3
• (sigma=10, s=0): Figure 4
• (sigma=10, s=1): Figure 5

Default settings

The default values are:

• sigma=0
• s=1

Speeding up the simulation

To reduce the simulation time, you can lower the value of the n_simulations variable in the script. The default value is n_simulations=100. Reducing it will speed up the process but may slightly decrease the accuracy of the results.

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Figures 6 and 7

To generate Figures 6 and 7, use the script SimulationRealScenario.py. This requires installing the Gurobi optimization library and activating a valid license. To reduce computation time, adjust the time limit in the Core.py file. Modify the following line in the class Core.py:

   model.setParam("TimeLimit", 1400)

Setting up Gurobi

1. Install the Gurobi Python library:

   pip install gurobipy

2. Obtain and activate a Gurobi license: Obtain a license from Gurobi's official website. Configure your license as required by Gurobi's installation instructions.
3. Obtain and activate a Gurobi license: Update the params dictionary in the Core.py file with your Gurobi license details:

   params = {
    "WLSACCESSID": '#################################',  # Replace with your WLSACCESSID
    "WLSSECRET": '#################################',   # Replace with your WLSSECRET
    "LICENSEID": 00000000,                              # Replace with your LICENSEID
}