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Machine Learning

Our approach is to predict Wi-Fi loss 15 seconds ahead of time based on the past 60 seconds. To get the best possible result, we evaluated various ML algorithms, feature vectors and training sets. An overview will be given here.

Feature Vectors

We evaluated three different feature vectors. The first one is the full feature vector, as can be seen below:

  • Pressure (delta)
  • Absolute Pressure
  • Linear Acceleration (all axes separately)
  • Linear Acceleration (all three axes together)
  • Step Counter
  • Power Data (if the phone is charging or not)
  • Remaining Battery Capacity
  • Gravity (all axes)
  • Gyroscope
  • Magnetic Field (all axes)
  • Absolute Phone Orientation
  • Phone Rotation (all axes)
  • Wi-Fi Freuency (2.4 GHz or 5 GHz)
  • Wi-Fi Speed (IEEE 802.11b, IEEE 802.11n, ...)
  • Wi-Fi RSSI

Additionally, we used a reduced feature vector, containing the following sensors:

  • Pressure (delta)
  • Linear Acceleration (all three axes together)
  • Step Counter
  • Power Data (if the phone is charging or not)
  • Gravity (Z-axis)
  • Wi-Fi Freuency (2.4 GHz or 5 GHz)
  • Wi-Fi Speed (IEEE 802.11b, IEEE 802.11n, ...)
  • Wi-Fi RSSI

Finally, we also used just the Wi-Fi RSSI for comparison.

Algorithms

Besides using different feature vectors, we also used different ML algorithms and different configurations.

Algorithm Configuration
Radom Forest 10 Trees
20 Trees
25 Trees
Multi-layer Perceptron Classifier 1 Layer, 100 Neurons (NN1)
3 Layers, 100, 200, 300 Neurons (NN2)
5 Layers, 400 Neurons each (NN3)

Training and Test Sets

Data from 5 volunteer testers were collected for this work. Thus, we tried two different Training and Test sets. First, all data was combined and than split randomly in 70:30 relation with 70% for training the Neural Network and 30% for testing. In the second approach the data was split per user, resulting in a model that was trained on a specific user.

Results

In this section the raw results are presented. First we look at the Random Data Split to validate the effectiveness of our approach. A baseline evaluation using a Random Forest is presented, comparing an RSSI-only approach to the Reduced and Full Feature Vectors.

Random Data Split

Random Forest

{% include random-forest.md %}

Neural Network

{% include random-nn.md %}

User-based Data Split

To prove how well the trained model can generalize across users, the data is split up based on users, where the user evaluated with is not used for comparison.

Neural Network

{% include user-nn-reduced.md %}

For more data on the evaluated models, please look in the available notebooks. Learned models are also available there.

Notebooks

Alls Jupyter Notebooks can be downloaded from our GitHub repo. We provide a Dockerfile so that you can start the Docker container and see all steps done from preparing the data until the learning evaluation directly in a Jupyter Lab. Just check out the mentioned repo, build the container and start it. Please provide the right parameters in the commands below:

docker build .
docker run -p=8888:8888 -v <PATH_TO_SQLITE_TABLES>:/jupyter <CONTAINER_ID>

The output of the container will contain a URL (similar to http://0.0.0.0:8888/?token=9b7e8f0aaec254f72bbed67074deae7bab1ceb6e8473e917). Replace 0.0.0.0 with localhost or 127.0.0.1 and open the URL in your browser. That' it.