Volcanic earthquake timing using wireless sensor networks

Abstract

Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for active volcano monitoring. This paper studies the problem of picking arrival times of primary waves (i.e., P-phases) received by seismic sensors, one of the most critical tasks in volcano monitoring. Two fundamental challenges must be addressed. First, it is virtually impossible to download the real-time high-frequency seismic data to a central station for P-phase picking due to limited wireless network bandwidth. Second, accurate P-phase picking is inherently computationintensive, and is thus prohibitive for many low-power sensor platforms. To address these challenges, we propose a new P-phase picking approach for hierarchical volcano monitoring WSNs where a large number of inexpensive sensors are used to collect fine-grained, real-time seismic signals while a small number of powerful coordinator nodes process collected data and pick accurate P-phases. We develop a suite of new in-network signal processing algorithms for accurate P-phase picking, including lightweight signal pre-processing at sensors, sensor selection at coordinators as well as signal compression and reconstruction algorithms. Testbed experiments and extensive simulations based on real data collected from a volcano show that our approach achieves accurate Pphase picking while only 16% of the sensor data are transmitted.

Distributed tomography with adaptive mesh refinement in sensor networks

“…So to obtain high resolution and real time imaging we need large number of sensor stations which has the capability of performing in-network computing and also avoid costly data collection. The authors in Liu et al (2013) have discussed the use of low-cost sensor for P-phase detection of earthquake. The earthquake hypo-centre detection forms the basic step for seismic tomography and we extend this further to obtain innetwork imaging.…”

Secured File Sharing using Trust Mechanism in Wireless Sensor Network

“…It provides aftermath of earthquakes on buildings very precisely. Sensor nodes deployed in different parts of a building which provides the effects of earthquake strength to the building [3].…”

Distributed Randomized Kaczmarz and Applications to Seismic Imaging in Sensor Network

“…For instance, in volcano monitoring system, a seismic vibration sensor (geophone) sample in a range of 16-24 bit at 50-200 Hz generating a high volume of raw data [28]. Preprocessing steps such as detecting an earthquake, finding its origin time and location can be performed in-situ [20]. Using these pre-processing steps, discretization of raw data leads us to the formation of a partial least squares problem at each node [26].…”