Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/15671
Authors: Falanga, Mariarosaria* 
De Lauro, Enza* 
Petrosino, Simona* 
Rincon-Yanez, Diego* 
Senatore, Sabrina* 
Title: Semantically Enhanced IoT-Oriented Seismic Event Detection: An Application to Colima and Vesuvius Volcanoes
Journal: IEEE Internet of Things Journal 
Series/Report no.: 12/9 (2022)
Publisher: IEEE
Issue Date: 15-Jun-2022
DOI: 10.1109/JIOT.2022.3148786
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9702529&casa_token=WB1hgYZ_Q2gAAAAA:A35-g6Xa--MXn_EeAOXeDz86Yy5e-_ZB7_VoiYqzRpOjVrog9D4IvbcOZPS683KbFGDn32af&tag=1
Keywords: Internet of Things (IoT)-based sensor networks
machine learning, ontology
Vesuvius and Colima volcanoes
Subject Classification05.06. Methods
Abstract: Collecting massive seismic signals is a high-priority task in seismic risk evaluation, especially in densely populated areas, with cases of strong magnitude earthquake occurrence. At the same time, with the advent of the Internet of Things (IoT) paradigm, distributed and real-time environmental monitoring, supported by device interoperability, enhances the ability to collect data and make decisions especially in critical domains such as the seismic one. A crucial role is played by Semantic Web technologies that, in IoT ecosystems, promote syntactic and semantic interoperability, by enhancing the data quality that becomes ontology-annotated. This article introduces an IoT-oriented framework to collect seismic data, process and store them into a knowledge base. An ontology called Volcano Event Ontology (VEO) modeled for the seismic domain aims at gathering seismic signals collected by sensors for seismic event detection. The ontology is built on the well-known SSN/SOSA ontology, modeled to describe the systems of sensors, actuators, and observations. Seismic data have been collected by monitoring networks at Mt. Vesuvius (Naples, Italy) and Colima volcano (Mexico) and consolidated in the ontology. Moreover, the seismic data are also processed by a classification module to detect different seismic events (Volcano-Tectonic and long-period earthquakes, underwater explosions, and quarry blasts) and then stored in the knowledge base. Prompt detection and classification are, indeed, relevant to track any variation in the volcano dynamics, becoming crucial in cases of explosive crises. Finally, the VEO-driven knowledge base can be queried to get time-based seismic data and detected events, by queries.
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