QuakeSense, a LoRacompliant Earthquake Monitoring Open System Detecting disruptive events, such as earthquakes, using environmental monitoring systems is a particularly promising, but rather challenging, opportunity. The Internet of Things (IoT) can play a significant role in characterizing and predicting seismic events. The present contribution introduces QuakeSense, an opensource earthquake and weather monitoring system. The implemented IoT system is configured as a Long Range (LoRa)based star topology with a fully energyautonomous sensor node. The system leverages some of the most useful features of two emerging IoT technologies, e.g., LoRa and Message Queue Telemetry Transport (MQTT), and enables the near realtime monitoring of seismic events through a webbased interface. An experimental campaign has been carried out to verify the current consumption and, therefore, the battery lifetime of the sensor node. Moreover, LoRa parameters have been extensively tested as to evaluate performances in several configurations. The obtained results in terms of latency and Packet Delivery Ratio (PDR) demonstrated the reliability of the proposal.

Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature Extraction Method Detecting earthquakes using smartphones or IoT devices in realtime is an arduous and challenging task, not only because it is constrained with the hard realtime issue but also due to the similarity of earthquake signals and the nonearthquake signals (i.e., noise or other activities). Moreover, the variety of human activities also makes it more difficult when a smartphone is used as an earthquake detecting sensor. To that end, in this article, we leverage a machine learning technique with earthquake features rather than traditional seismic methods. First, we split the detection task into two categories including static environment and dynamic environment. Then, we experimentally evaluate different features and propose the most appropriate machine learning model and features for the static environment to tackle the issue of noisy components and detect earthquakes in realtime with less false alarm rates. The experimental result of the proposed model shows promising results not only on the given dataset but also on the unseen data pointing to the generalization characteristics of the model. Finally, we demonstrate that the proposed model can be also used in the dynamic environment if it is trained with different dataset.