Towards Accelerated Localization Performance Across Indoor Positioning Datasets
The localization speed and accuracy in the indoor scenario can greatly impact
the Quality of Experience of the user. While many individual machine learning
models can achieve comparable positioning performance, their prediction
mechanisms offer different complexity to the system. In this work, we propose a
fingerprinting positioning method for multi-building and multi-floor
deployments, composed of a cascade of three models for building classification,
floor classification, and 2D localization regression. We conduct an exhaustive
search for the optimally performing one in each step of the cascade while
validating on 14 different openly available datasets. As a result, we bring
forward the best-performing combination of models in terms of overall
positioning accuracy and processing speed and evaluate on independent sets of
samples. We reduce the mean prediction time by 71% while achieving comparable
positioning performance across all considered datasets. Moreover, in case of
voluminous training dataset, the prediction time is reduced down to 1% of the
benchmark's.