  This paper presents a novel approach to detect abnormalities in dynamic
systems based on multisensory data and feature selection. The proposed method
produces multiple inference models by considering several features of the
observed data. This work facilitates the obtainment of the most precise
features for predicting future instances and detecting abnormalities. Growing
neural gas (GNG) is employed for clustering multisensory data into a set of
nodes that provide a semantic interpretation of data and define local linear
models for prediction purposes. Our method uses a Markov Jump particle filter
(MJPF) for state estimation and abnormality detection. The proposed method can
be used for selecting the optimal set features to be shared in networking
operations such that state prediction, decision-making, and abnormality
detection processes are favored. This work is evaluated by using a real dataset
consisting of a moving vehicle performing some tasks in a controlled
environment.
