Study of the Network Impact on Earthquake Early Warning in the Quake-Catcher Network Project (original) (raw)
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Informatics
This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a Software-Defined Wide Area Network (SD-WAN)-based hole-punching technology consisting of MEMS-based, low-cost accelerometers hosted by the general public. In contrast with most centralised cloud-based approaches, a node-level decentralised data-processing is used to generate warnings with the support of a modified Propagation of Local Undamped Motion (PLUM)-based EEW algorithm. With several hypothetical earthquake scenarios, experiments were conducted to evaluate the system latencies of the proposed decentralised EEW architecture and its performance was compared with traditional centralised EEW architecture. The results from sixty simulations show that the SD-WAN-bas...
Earthquake monitoring using volunteer smartphone-based sensor networks
We introduce here the Earthquake Network project which implements a world-wide smartphone-based sensor network for the detection of earthquakes. Thanks to the accelerometric sensor, smartphones possibly detect the waves of a quake and report the event to a cloud computing infrastructure. In this work, we propose a solution to the detection problem based on statistical modelling the arrival times of the smartphone reports.
Peer-to-peer (P2P) earthquake warning system based on collaborative sensing
2009
We describe a completely new concept of earthquake warning. The collaborative system is based on distributed computers that are interconnected by a network like the Internet. Modern computers may have multiple sensors to detect movements. These sensors where integrated to detect a shock movement before the hard disk may be hurt. Another sensor for movement is the hard disk itself, due to its extreme precise spatial resolution, the readout process is very sensible to minor acceleration of the disk. We can collect all this data to understand the movement of the computer system. Since there are many other sources of acceleration beside an earth quake, we have to use the collective detection of many independent systems. For fast and efficient detection we describe a P2P solution to solve this part. The fact that earthquakes generate different types of movement makes it in principle feasible to predict a major movement a few ten seconds before the disaster happens. I.
2011
The Quake-Catcher Network (QCN) uses low-cost sensors connected to volunteer computers across the world to monitor seismic events. The location and density of these sensors' placement can impact the accuracy of the event detection. Because testing different special arrangements of new sensors could disrupt the currently active project, this would best be accomplished in a simulated environment. This paper presents an accurate and efficient framework for simulating the low cost QCN sensors and identifying their most effective locations and densities. Results presented show how our simulations are reliable tools to study diverse scenarios under different geographical and infrastructural constraints.
A novel strong-motion seismic network for community participation in earthquake monitoring
IEEE Instrumentation & Measurement Magazine, 2000
S eismic networks provide crucial data to scientists and the public about recent earthquakes, both large and small. These networks record waves that propagate away from the earthquake source and provide a host of information about the earthquake including magnitude, location, and how much slip occurs during an earthquake. Included in the details of each seismogram is information about the rocks and sediments which the seismic waves travel. By increasing the density of seismic stations, we can rapidly detect and locate earthquakes to provide an advance alert, improve our understanding of earthquake rupture and the associated seismic hazard, and generate in real-time, state-ofhealth information.
The Next Big One: Detecting Earthquakes and other Rare Events from Community-based Sensors
Can one use cell phones for earthquake early warning? 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.
A Virtual Subnetwork Approach to Earthquake Early Warning
Bulletin of the Seismological Society of America, 2002
Progress has been made toward the goal of earthquake early warning in Taiwan. By applying the concept of a virtual subnetwork (VSN) to the Taiwan Central Weather Bureau seismic network, the earthquake rapid-reporting time has been reduced to about 30 sec or less by a new system called VSN. This represents a significant step toward realistic earthquake early-warning capability. The VSN system described here was put into operation from December 2000 to June 2001. A total of 54 earthquakes (100% correct detection) were detected and processed successfully during this period. Comprehensive earthquake reports are issued mostly in less than 30 sec, with an average of about 22 sec after the origin time. The 22-sec reporting time will offer more than 20 sec of early-warning time to cities at distances greater than 145 km from the source, for which the shear-wave strong-shaking arrival time is about 44 sec. This 20 sec is an adequate amount of time to carry out numerous preprogrammed emergency-response measures prior to the arrival of strong shaking.
A statistical approach to crowdsourced smartphone-based earthquake early warning systems
Stochastic Environmental Research and Risk Assessment, 2016
The Earthquake Network research project implements a crowdsourced earthquake early warning system based on smartphones. Smartphones, which are made available by the global population, exploit the Internet connection to report a signal to a central server every time a vibration is detected by the on-board accelerometer sensor. This paper introduces a statistical approach for the detection of earthquakes from the data coming from the network of smartphones. The approach allows to handle a dynamic network in which the number of active nodes constantly changes and where nodes are heterogeneous in terms of sensor sensibility and transmission delay. Additionally, the approach allows to keep the probability of false alarm under control. The statistical approach is applied to the data collected by three subnetworks related to the cities of Santiago de Chile, Iquique (Chile) and Kathmandu (Nepal). The detection capabilities of the approach are discussed in terms of earthquake magnitude and detection delay. A simulation study is carried out in order to link the probability of detection and the detection delay to the behaviour of the network under an earthquake event.
Monitoring Earthquake-Induced Loading with Camera Networks
2004
The Pervasive Infrastructure Sensor Networks project at Carnegie Mellon University (see www.ices.cmu.edu/sensornets) is looking to match CMU’s strength as having been called one of the “most wired” and later “most wireless” campuses. The goal is to be the “most sensed” campus. We’re physically deploying a network of thousands of sensor nodes across the campus. Our approach to building such a large pervasive sensor network relies heavily on the existing computer infrastructure. We’ll be able to economically create such a large network using the CMU Critter Sensor, which leverages computer resources by attaching a sensor to a host desktop computer to use its processing, disk, and networking resources. It also leverages existing system administration support. We’re developing the system in conjunction with applications researchers who are interested in physical infrastructures such as building management and energy monitoring. We’ve collected several gigabytes of sensor data since June...