SA-6TiSCH: Selfishness-Aware 6TiSCH

Authors:

• Yassine Boufenneche (yboufenneche@usthb.dz)
• Rafik Zitouni (rafik.zitouni@ece.fr)
• Laurent George (laurent.george@esiee.fr)
• Nawel Gharbi (ngharbi@usthb.dz)

SA-6TiSCH is a modified version of the 6TiSCH simulator. It enables the simulation of 6TiSCH networks with the presence of selfish nodes. We define a selfish node as the one that does not amply cooperate with its neighbors during the negotiation process of the protocol 6P, which enables adding new cells between neighbors. In fact, a selfish node intentionnally disagree to install a number of cells with the neighbor, either to save its energy or to disrupt the network performance.

Additional features of SA-6TiSCH compared to 6TiSCH

The purpose of SA-6TiSCH is to consider the selfishness while simulating 6TiSCH networks. For so doing, we extended the 6TiSCH simulator with the following features:

• Selfish Behavior: we define and implement the selfish behavior in 6TiSCH networks, by making nodes not fairly running 6top Protocol. This behavior is implemented in the file SimEngine/Mote/sf.py.

• Detection Algorithm: is a distributed algorithm based on fuzzy logic theory that enables each node in the network to identify its non-cooperative neighbors. It is implemented in the file SimEngine/Mote/sixp.py, and relies on the values provided by the Fuzzy Selfishness Estimator.

  • Fuzzy Selfishness Estimator: it uses the fuzzy logic theory to compute a Selfishness value given an Occupancy probability value, a Request Satisfaction Rate (RSR) value and a Distance value. It is implemented in the file SelfishnessDetector/fuzzySelfishnessEstimator.py.

• Reaction Algorithm: is a countermeasure solution that helps nodes anticipating their reaction in the presence of suspicious nodes. The reaction mechanism is implemented in the file SimEngine/Mote/sf.py.

Choosing selfish nodes

The strategy of choosing which nodes to be configured as selfish ones for a simulation is implemented in the files SimEngine/SimEngine.py and SimEngine/Mote/rpl.py. We start with the first nodes that have successfully joined the network.

Results

The custom_plots folder hosts the results files as well as Python files used to generate our plots and boxes.

Results files are stored inside the custom_plots/kpis subfolder, and they are named following this logic: NRUN_SELFRATE_exec_numMotes_NMOTES.dat.kpi (e.g., 1_040_exec_numMotes_100.dat.kpi).

• NRUN = the number of the run for the same configuration.
• SELFRATE = the selfishness rate. It stands for the the number of configured selfish nodes to the overall number of nodes
• NMOTES = the number of nodes used in the simulation.
• If the file name starts with R, this means that the reaction mechanism was activated. Otherwise, no reaction mechanism was used.

Code Organization

• SelfishnessDetector/:
  • fuzzySelfishnessEstimator.py: The Fuzzy Selfishness Estimator (FSE).
• custom_plots/: The results
  • kpis/: The result files
  • custom_plots.py: Generates plolts and boxes.
  • extract_data.py: Extracts and combines data from different result files.
  • utilities.py: Useful functions.
• SimEngine/: the simulator
  • Connectivity.py: Simulates wireless connectivity.
  • SimConfig.py: The overall configuration of running a simulation campaign.
  • SimEngine.py: Event-driven simulation engine at the core of this simulator.
  • SimLog.py: Used to save the simulation logs.
  • SimSettings.py: The settings of a single simulation, part of a simulation campaign.
  • Mote/: Models a 6TiSCH mote running the different standards listed above.
• bin/: the scripts for you to run
• gui/: files for GUI (see "GUI" section for further information)
• tests/: the unit tests, run using pytest
• traces/: example k7 connectivity traces