Fusion-based volcanic earthquake detection and timing in wireless sensor networks

Volcano monitoring is of great interest to public safety and scientific explorations. However, traditional vol-canic instrumentation such as broadband seismometers are expensive, power-hungry, bulky, and difficult to install. Wireless sensor networks (WSNs) offer the potential to monitor volcanoes at unprecedented spa-tial and temporal scales. However, current volcanic WSN systems often yield poor monitoring quality due to the limited sensing capability of low-cost sensors and unpredictable dynamics of volcanic activities. In this paper, we propose a novel quality-driven approach to achieving real-time, distributed, and long-lived volcanic earthquake detection and timing. By employing novel in-network collaborative signal processing algorithms, our approach can meet stringent requirements on sensing quality (low false alarm/missing rate, short detection delay, and precise earthquake onset time) at low power consumption. We have implemented our algorithms in TinyOS and conducted extensive evaluation on a testbed of 24 TelosB motes as well as simulations based on real data traces collected during 5.5 months on an active volcano. We show that our approach yields near-zero false alarm/missing rate, less than one second of detection delay, and millisec-ond precision earthquake onset time while achieving up to 6-fold energy reduction over the current data collection approach