Earthquake detection at the edge: IoT crowdsensing network

Abstract

Earthquake Early Warning state of the art systems rely on a network of sensors connected to a fusion center in a client-server paradigm. 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 using Raspberry, NodeMCU, and the Crowdquake machine learning model.

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

“…Besides wildfire monitoring, participatory crowdsensing has been employed for near real-time detection of other disaster events such as earthquakes [255], landslides [256], and floods [257]. A common approach in these contributions is to consider only sensor readings with an associate position information (either from GPS or cellular networks).…”