Machine Learning in the Internet of Things: a Semantic-enhanced Approach

Tracking #: 1806-3019

This paper is currently under review
Michele Ruta
Floriano Scioscia
Giuseppe Loseto
Agnese Pinto
Eugenio Di Sciascio

Responsible editor: 
Guest Editors IoT 2017

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Full Paper
Novel Internet of Things (IoT) applications and services rely more and more on an intelligent understanding of the environment from data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking meaningful representation and explanation of taxonomies. This paper proposes a framework for a semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection by treating the typical classification problem of ML as a resource discovery. Outputs of classification are endowed with machine-understandable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis has allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.
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