Machine Learning-based Positioning using Multivariate Time Series
  Classification for Factory Environments
Nisal Hemadasa Manikku Badu Marcus Venzke Volker Turau Yanqiu Huang
Indoor Positioning Systems (IPS) gained importance in many industrial
applications. State-of-the-art solutions heavily rely on external
infrastructures and are subject to potential privacy compromises, external
information requirements, and assumptions, that make it unfavorable for
environments demanding privacy and prolonged functionality. In certain
environments deploying supplementary infrastructures for indoor positioning
could be infeasible and expensive. Recent developments in machine learning (ML)
offer solutions to address these limitations relying only on the data from
onboard sensors of IoT devices. However, it is unclear which model fits best
considering the resource constraints of IoT devices. This paper presents a
machine learning-based indoor positioning system, using motion and ambient
sensors, to localize a moving entity in privacy concerned factory environments.
The problem is formulated as a multivariate time series classification (MTSC)
and a comparative analysis of different machine learning models is conducted in
order to address it. We introduce a novel time series dataset emulating the
assembly lines of a factory. This dataset is utilized to assess and compare the
selected models in terms of accuracy, memory footprint and inference speed. The
results illustrate that all evaluated models can achieve accuracies above 80 %.
CNN-1D shows the most balanced performance, followed by MLP. DT was found to
have the lowest memory footprint and inference latency, indicating its
potential for a deployment in real-world scenarios.