FTM Parameters Optimization

Code for the research paper:

• Krzysztof Kurczab, Maksymilian Wojnar, Kamil Szczech, and Katarzyna Kosek-Szott. "Improving Robustness of Indoor Positioning by Tuning IEEE 802.11 Fine Timing Measurement Parameters" (under review).

Installation

Python package

1. Clone the repository:

   git clone https://github.com/ml4wifi-devs/ftm-optimal.git
   

2. Install requirements:

   pip install optuna~=4.1.0
   

ns-3 network simulator

The wifi-ftm-ns3 extension of the ns-3 network simulator needs to be installed on your machine. You can read more on ns-3 installation process in the official installation notes.

1. Download and unzip wifi-ftm-ns3:

   git clone https://github.com/tkn-tub/wifi-ftm-ns3.git
   mv wifi-ftm-ns3/ns-allinone-3.33-FTM-SigStr/ns-3.33 $NS3_DIR
   

2. Copy the scenario file to the ns-3 scratch directory:

   cp $PROJECT_DIR/scenario.cc $NS3_DIR/scratch
   

3. Build ns-3:

   ./waf configure -d optimized --enable-examples --enable-tests --disable-werror --disable-python
   ./waf
   

Usage

Run the following command to start the optimization process:

python main.py --nWifi=<N_WIFI> --dataRate=<DATA_RATE> [ARGS]

The process takes a significant amount of time to complete. The results are stored in the SQLite database. To run the evaluation process in the background, execute the following command:

nohup python main.py --nWifi=<N_WIFI> --dataRate=<DATA_RATE> [ARGS] &

Analysis

To install the required packages for the graphical analysis, run the following command:

pip install optuna-dashboard

To start the dashboard, run the following command:

optuna-dashboard sqlite:///<FILENAME>.db