Seismic Data Collection with Shakebox and Analysis Using MapReduce

Bin Tang; Jianchao Han; Mohsen Beheshti; Garrett Poppe; Liv Nguekap; Rashid Siddiqui

doi:10.4236/jcc.2015.35012

Home > Journals > Computer Science & Communications > JCC

Journal of Computer and Communications > Vol.3 No.5, May 2015

Seismic Data Collection with Shakebox and Analysis Using MapReduce ()

Bin Tang, Jianchao Han, Mohsen Beheshti, Garrett Poppe, Liv Nguekap, Rashid Siddiqui

Department of Computer Science, California State Univeristy Dominguez Hills, Carson, CA 90747, USA.

DOI: 10.4236/jcc.2015.35012 PDF HTML XML 2,986 Downloads 3,559 Views Citations

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.

Keywords

Seismic Data, Shakebox, Big Data, Hadoop MapReduce

Share and Cite:

Tang, B. , Han, J. , Beheshti, M. , Poppe, G. , Nguekap, L. and Siddiqui, R. (2015) Seismic Data Collection with Shakebox and Analysis Using MapReduce. Journal of Computer and Communications, 3, 94-101. doi: 10.4236/jcc.2015.35012.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Faulkner, M., Clayton, R., Heaton, T., Mani Chandy, K., Kohler, M., Bunn, J., Guy, R., Liu, A., Olson, M., Cheng, M. and Krause, A. (2014) Community Sense and Response Systems: Your Phone as Quake Detector. Communications of the ACM, 57, 66-75. http://dx.doi.org/10.1145/2622633
[2]	Clayton, R.W., Heaton, T., Chandy, M., Krause, A., Kohler, M., Bunn, J., Guy, R., Olson, M., Faulkner, M., Cheng, M., Strand, L., Chandy, R., Obenshain, D., Liu, A. and Aivazis, M. (2012) Community Seismic Network. Annals of Geophysics, 54.
[3]	Olson, M., Liu, A., Chandy, K.M. and Faulkner, M. (2011) Rapid Detection of Rare Geospatial Events. 5th ACM International Conference on Distributed Event-Based System.
[4]	Faulkner, M., Olson, M., Chandy, R., Krause, J. and Chandy, K.M. (2011) The Next Big One: Detecting Earthquakes and Other Rare Events from Community-Based Sensors. 10th International Conference on Information Processing in Sensor Networks (IPSN).
[5]	Cochran, E.S., Lawrence, J.F., Kaiser, A., Fry, B., Chung, A. and Christensen, C. (2011) Comparison between Low-Cost and Traditional MEMS Accelerometers: A Case Study from the M7.1 Darfield, New Zealand, Aftershock Deployment. Annals of Geophysics, 54, 728-737.
[6]	Cochran, E., Lawrence, J., Christensen, C. and Chung, A. (2009) A Novel Strong-Motion Seismic Network for Community Partici-pation in Earthquake Monitoring. IEEE Inst & Meas, 12, 8-15.
[7]	https://www.globalscaletechnologies.com/t-sheevaplugs.aspx
[8]	Mishra, N., Hao, S., Kohler, M., Go-vindan, R. and Nigbor, R. (2010) ShakeNet: A Tiered Wireless Accelerometer Network for Rapid Deployment in Civil Structures. http://nsl.cs.usc.edu/Papers/?action=download&upname=Mishra10a.pdf
[9]	Lee, C.-H., Birch, D., Wu, C., Silva, D., Tsinalis, O., Li, Y., Yan, S.L., Ghanem, M. and Guo, Y.K. (2013) Building a Generic Platform for Big Sensor Data Application. IEEE International Conference on Big Data. http://dx.doi.org/10.1109/BigData.2013.6691559
[10]	Dean, J. and Ghemawat, S. (2004) Mapreduce: Simplified Data Processing on Large Clusters. 6th Conference on Symposium on Opearting Systems Design & Implementation, 137-150.
[11]	The Apache Hadoop Framework. 2013. http://hadoop.apache.org
[12]	Liu, B., Chen, H.F., Sharma, A., Jiang, G.F. and Xiong, H. (2013) Modeling Heterogeneous Time Series Dynamics to Profile Big Sensor Data in Com-plex Physical Systems. IEEE International Conference on Big Data.
[13]	Guo, T., Papaioannou, T.G. and Aberer, K. (2013) Model-View Sensor Data Management in the Cloud. IEEE International Conference on Big Data. http://dx.doi.org/10.1109/BigData.2013.6691585
[14]	Paek, J., Greenstein, B., Gnawali, O., Jang, K.-Y., Joki, A., Vieira, M., Hicks, J., Estrin, D., Govindan, R. and Kohler, E. (2010) The Tenet Architecture for Tiered Sensor Networks. ACM Transactions on Sensor Networks (TOSN), 6(4). http://dx.doi.org/10.1145/1777406.1777413
[15]	http://boinc.berkeley.edu