Community sense-and-response systems: Your phone as seismometer

Can inexpensive accelerometers, such as those in cell phones and other commodity devices, be used for earthquake detection and monitoring? Detecting rare, disruptive events using community-held sensors is a promising opportunity, but also presents difficult challenges. Rare events are often difficult or impossible to model and characterize a priori, yet we wish to maximize detection performance. Further, heterogeneous, community-operated sensors may differ widely in quality and communication constraints. We present a principled approach towards detecting rare events that learns sensor-specific decision thresholds online, in a distributed way. We perform aggregation by sparsification: roughly, a sparsifying basis is a linear transformation that aggregates measurements from groups of sensors that tend to co-activate, and each event is observed by only a few groups of sensors. We show how a simple class of sparsifying bases provably improves detection with noisy binary sensors, even when only qualitative information about the network is available. We then describe how detection can be further improved by learning a better sparsifying basis from network observations or simulations. We present an implementation of our approach in the Community Seismic Network (CSN), a community sensing system with the goal of rapidly detecting earthquakes using cell phone accelerometers, consumer USB devices and cloud-computing based sensor fusion. We experimentally evaluate our approach based on a pilot deployment of the CSN system. Our results, including data from shake table experiments, provide evidence towards the feasibility of rapid earthquake detection using a dense network of cell phones.