State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model.

Earthquake Detection at the Edge: IoT Crowdsensing Network / Bassetti, Enrico; Panizzi, Emanuele. - In: INFORMATION. - ISSN 2078-2489. - 13:4(2022). [10.3390/info13040195]

Earthquake Detection at the Edge: IoT Crowdsensing Network

Bassetti, Enrico
;
Panizzi, Emanuele
2022

Abstract

State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model.
2022
earthquake early warnings; crowdsensing; edge computing; Internet of Things
01 Pubblicazione su rivista::01a Articolo in rivista
Earthquake Detection at the Edge: IoT Crowdsensing Network / Bassetti, Enrico; Panizzi, Emanuele. - In: INFORMATION. - ISSN 2078-2489. - 13:4(2022). [10.3390/info13040195]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1629265
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