ACM Home Page
Please provide us with feedback. Feedback
Slip surface localization in wireless sensor networks for landslide prediction
Full text PdfPdf (274 KB)
Source Information Processing In Sensor Networks archive
Proceedings of the 5th international conference on Information processing in sensor networks table of contents
Nashville, Tennessee, USA
SESSION: Main track--sensor tasking and data retrieval table of contents
Pages: 109 - 116  
Year of Publication: 2006
ISBN:1-59593-334-4
Authors
Andreas Terzis  Johns Hopkins University, Baltimore, MD
Annalingam Anandarajah  Johns Hopkins University, Baltimore, MD
Kevin Moore  Colorado School of Mines, Golden, CO
I-Jeng Wang  Johns Hopkins University, Baltimore, MD
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 13,   Downloads (12 Months): 62,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1127777.1127797
What is a DOI?

ABSTRACT

A landslide occurs when the balance between a hill's weight and the countering resistance forces is tipped in favor of gravity. While the physics governing the interplay between these competing forces is fairly well understood, prediction of landslides has been hindered thus far by the lack of field measurements over large temporal and spatial scales necessary to capture the inherent heterogeneity in a landslide.We propose a network of sensor columns deployed at hills with landslide potential with the purpose of detecting the early signals preceding a catastrophic event. Detection is performed through a three-stage algorithm: First, sensors collectively detect small movements consistent with the formation of a slip surface separating the sliding part of hill from the static one. Once the sensors agree on the presence of such a surface, they conduct a distributed votingalgorithm to separate the subset of sensors that moved from the static ones. In the second phase, moved sensors self-localize through a trilateration mechanism and their displacements are calculated. Finally, the direction of the displacements as well as the locations of the moved nodes are used to estimate the position of the slip surface. This information along with collected soil measurements e.g. soil pore pressures) are subsequently passed to a Finite Element Model that predicts whether and when a landslide will occur.Our initial results from simulated landslides indicate that we can achieve accuracy in the order of cm in the localization as well as the slip surface estimation steps of our algorithm. This accuracy persists as the density and the size of the sensor network decreases as well as when considerable noise is present in the ranging estimates. As for our next step, we plan to evaluate the performance of our system in controlled environments under a variety of hill configurations.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
A. Anandarajah. HOPDYNE: A finite element computer program for the analysis of static, dynamic and earthquake soil and soil-structure systems. Technical report, Civil Engineering Report, Johns Hopkins University, 1990.
 
2
A. Anandarajah. Numerical Methods for Seismic Analysis of Dams. In International Workshop on Seismic Stability of Tailings Dams, Nov. 2003.
 
3
A. Anandarajah, J. Zhang, and C. Ealy. Calibration of dynamic analysis methods from field test data. Journal of Soil Dyn. Earthquake Engineering, 2005.
 
4
A. Carrara and F. Guzzetti. Use of GIS technology in the prediction and monitoring of landslide hazard. Natural Hazards, pages 117--135, 1999.
 
5
K. K. Chintalapudi, A. Dhariwal, R. Govindan, and G. Sukhatme. Ad-hoc localization using ranging and sectoring. In Proceedings of the IEEE INFOCOM 2004, pages 2662--2672, Nov. 2004.
 
6
Crossbow Corporation. Stargate Gateway (SPB400). Available at: http://www.xbow.com/Products/Product pdf files/ Wireless pdf/Stargate Datasheet.pdf, 2004.
 
7
M. J. Crozier. Landslides: causes, consequences & environment. Croom Helm, 1986.
 
8
E. Dietrich, R. Reisss, M. Hsu, and D. Montgomery. A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrological Processes, pages 383--400, 1995.
 
9
T. Fukuzono. Creep model of Kanto loam and its application to time prediction of landslide. Landslides. (Eds: Chacon, J., Irigaray, C. and Fernandez, T.), pages 221--233, 1996.
 
10
W. V. Gassen and D. Cruden. Momentum transfer and friction in the debris of rock avalanches. Can. Geotech, pages 623--628, 1989.
 
11
P. Huber. Robust estimation of a location parameter. Annals of Mathematical Statistics, pages 73--101, 1964.
 
12
J. N. Hutchinson. A sliding-consolidation model for ow slides. Ca. Geotech, pages 115--126, 1986.
 
13
X. Lan, C. Zhou, L. Wang, H. Zhang, and R. H. Li. Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang Watershed, Yunnan, China. Eng. Geology, pages 109--128, 2004.
 
14
S. McDougall and O. Hungr. A model for the analysis of rapid landslide motion across three-dimensional terrain. Can. Geotech, pages 1084--1097, 2004.
15
 
16
 
17
K. Plarre, P. R. Kumar, and T. I. Seidman. Increasingly correct message passing algorithms for heat source detection in sensor networks. In Proceedings of the First IEEE International Conference on Sensor ad Ad Hoc Networks (SECON 2004), 2004.
18
 
19
C. Veder. Landslides and Their Stabilization. Springer-Verlag, 1996.
 
20
O. Zienkiewicz and R. Taylor. The finite element method. MacGraw-Hill, 1989.


Collaborative Colleagues:
Andreas Terzis: colleagues
Annalingam Anandarajah: colleagues
Kevin Moore: colleagues
I-Jeng Wang: colleagues