Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/14259
Authors: Rincon-Yanez, Diego* 
De Lauro, Enza* 
Falanga, Mariarosaria* 
Senatore, Sabrina* 
Petrosino, Simona* 
Title: Towards a semantic model for IoT-based seismic event detection and classification
Issue Date: 1-Dec-2020
DOI: 10.1109/SSCI47803.2020.9308329
Keywords: Ontology model
Seismic events
Machine Learning Techniques
Vesuvius volcano
Seismic Monitoring
Abstract: In the seismic domain, collecting seismic signal and alerting movements of earth crust is crucial for monitoring and forecasting seismic activities. At the same time, with the advent of the Internet of Things (IoT) paradigm, the device interoperability is the minimum requirement for communication among any available sensing device. Semantic web technologies promote this interoperability, by enhancing the quality of data that become ontology-annotated. The paper introduces an ontology model for describing the seismic domain, through the data collection from sensors, to gather seismic signals aimed at the seismic event recognition. The ontology has been built on the well-known SOSA and SSN ontologies, modeled to describe systems of sensors, actuators, and observations. The ontology, namely VEO (Volcano Event Ontology), has been modeled on actual data sensors, collected by a monitoring network at Mt. Vesuvius (Naples, Italy). Along with the ontology model of the seismic domain, a machine learning-based classification has been accomplished to identify seismic events (underwater explosions, quarry blasts, and thunders). A VEO-driven knowledge-base collects raw seismic data and detects events, accessible by SPARQL queries.
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