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Jupyter Notebook Python
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data
models
packets
.gitignore
README.md
capture.py
channel_hopper.py
classifier.py
classify_packets.py
classify_realtime.py
get_rssi.py
packet_identify.py
paper_1.ipynb
paper_2.ipynb
realtime_classifier.py
requirements.txt
sample_stream.py
sample_to_x.py
slim_packet.py
train.py
training_data.py

README.md

wifi-passive-localization

Implementation of device-free passive localization. This concept refers to the ability to determine the location of a human in an environment using passive monitoring of Wi-Fi access point signal strengths over time. As a human moves through the environment, they cause fluctuations in the signal strengths which can be observed and classified.

Sample code is included to capture packets on a Linux device, pre-process captured packets, and train a machine learning model to classify future captured data. Two Jupyter notebooks are included with the code used to generate the graphs in the submitted paper.

Created for a Computer Science Independent Work project at Princeton University in Fall 2016, as part of the COS IW 02 Policy Issues in the Internet of Things seminar.

Usage

  1. On a Linux machine, run capture.py -d duration label (as root) to capture duration seconds of packets to a file packets/packets-<label>-<timestamp>.pkl.
  2. Generate training data for the model with training_data.py label data. Pass the name of the saved packets files and their associated category labels as the data parameter. Multiple files can be provided for each label. (Example: 0 packets/packets-0-0.pkl packets/packets-0-1.pkl 1 packets/packets-1-0.pkl packets/packets-1-1.pkl) This saves a file data/training-data-<label>.pkl.
  3. Train the model with train.py data [-l label] [--plot]. If --plot is specified, a graphical visualization of the training data will be displayed. Otherwise, if -l label is specified, the trained model will be saved to models/model-<label>.pkl.
  4. Run classify_packets.py model packets to run the model file model on the packets file packets. The script will output a predicted category label for each sample.
  5. Run classify_realtime.py model (as root) to use the model file model to classify packets in real-time.