TITLE:
Seismic Data Collection with Shakebox and Analysis Using MapReduce
AUTHORS:
Bin Tang, Jianchao Han, Mohsen Beheshti, Garrett Poppe, Liv Nguekap, Rashid Siddiqui
KEYWORDS:
Seismic Data, Shakebox, Big Data, Hadoop MapReduce
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.5,
May
25,
2015
ABSTRACT: In this paper we study a seismic sensing platform
using Shakebox, a low-noise and low-power 24- bit wireless accelerometer sensor.
The advances of wireless sensor offer the potential to monitor earthquake in California
at unprecedented spatial and temporal scales. We are exploring the possibility of
incorporating Shakebox into California Seismic Network (CSN), a new earthquake monitoring
system based on a dense array of low-cost acceleration seismic sensors. Compared
to the Phidget/Sheevaplug sensors currently used in CSN, the Shakebox sensors have
several advantages. However, Shakebox sensor collects 4K Bytes of seismic data per
second, giving around 0.4G Bytes of data in a single day. Therefore how to process
such large amount of seismic data becomes a new challenge. We adopt Hadoop/MapReduce,
a popular software framework for processing vast amounts of data in-parallel on
large clusters of commodity hardware. In this research, the test bed-generated seismic
data generation will be reported, the map and reduce function design will be presented,
the application of MapReduce on the testbed-generated data will be illustrated,
and the result will be analyzed.