Supporting sensor orchestration in non-stationary environments

Abstract

The aim of sensor orchestration is to design and organise multi-sensor systems both to reduce manual design efforts and to facilitate complex sensor systems. A sensor orchestration is required to adapt to non-stationary environments, even if it is applied in streaming data scenarios where labelled data are scarce or not available. Without labels in dynamic environments, it is challenging to determine not only the accuracy of a classifier but also its reliability. This contribution proposes monitoring algorithms intended to support sensor orchestration in classification tasks in non-stationary environments. Proposed measures regard the relevance of features, the separability of classes, and the classifier's reliability. The proposed monitoring algorithms are evaluated regarding their applicability in the scope of a publicly available and synthetically created collection of datasets. It is shown that the approach (i) is able to distinguish relevant from irrelevant features, (ii) measures class separability as class representations drift through feature space, and (iii) marks a classifier as unreliable if errors in the drift-adaptation occur.