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Implementation and Documentation of consensus network calibration simulations

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Simulation environment to test, compare and evaluate multiple co-calibration methods

Run a given scenario

A scenario can be configured and then multiple methods are applied to the same configuration.

python evaluation_runner.py experiments/scenario_A/

Visualize results of a scenario

Once run, the results can be visualized:

python visualization_runner.py experiments/scenario_A/

Define a scenario

A scenario is defined inside a config.json, which stores information about the

  • random state
  • reference sensors
  • device under test
  • measurand
  • sensor readings
  • methods to be used

All options can also be loaded from an existing (i.e. from a previous run, to achieve or manipulate a specific setting).

Define a method

Available methods are defined in method_args/<method_name>.json.

  • class name (as used in the actual source cocalibration_methods.py)
  • arguments to initialize the class

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