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Instrumentation for Measurement of Geotechnical Parameters for Landslide Prediction Using Wireless Sensor Networks

  • Mohammed Moyed AhmedEmail author
  • Gorre Narsimhulu
  • D. Sreenivasa Rao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

Landslides are the most serious geological disasters in our country, because of its short time of occurrence, causing heavy casualties, and huge economic losses. Due to the complexity of geographical conditions and some of the influence of technical factors such as ground displacement, groundwater conditions, and pore water pressure, a real-time dynamic monitoring of soil parameters is necessary to understand landslide dynamics and to provide an early warning mechanism. In this paper, we discuss an instrumentation model developed based on the digital geotechnical sensors and NI wireless sensor network platform with LabVIEW software, for monitoring and prediction of landslides.

Keywords

Wireless sensor networks Clustering Geo-sensors Early warning system 

Notes

Acknowledgements

This research is supported in part by AICTE project under Research Promotion Scheme titled “Development of Early Warning System for Landslide Prediction”—DEWSLP-2017, JNTUHCEH, ECE Dept. No. 8–4/RFID/RPS/Policy-1/2016-17.

References

  1. 1.
    Sekhar, L., Kuriakose, Æ.G., Sankar, Æ., Muraleedharan, C.: History of landslide susceptibility and a chorology of landslide-prone areas in the Western Ghats of Kerala, India. Environ. Geol. 57, 1553–1568 (2009).  https://doi.org/10.1007/s00254-008-1431-9CrossRefGoogle Scholar
  2. 2.
    Akyildiz, I.F., et al.: Wireless sensor networks: a survey, Comput. Netw. 38 (4), 393–422 (2002)CrossRefGoogle Scholar
  3. 3.
    Arnhardt, C, Fernández-Steeger, T.M, Azzam, R.: Sensor fusion of position- and micro-sensors (MEMS) integrated in a wireless sensor network for movement detection in landslide areas. Geophys. Res. Abstr. 12, EGU2010-8828 (2010)Google Scholar
  4. 4.
    Di Maio, C., Vassallo, R.: Geotechnical characterization of a landslide in a Blue Clay slope. Landslides 8(1), 17–32 (2011)Google Scholar
  5. 5.
    Ramesh, M.V., Raj, R., Freeman, J., Kumar, S., Rangan, P.V.: Factors and approaches towards energy optimized wireless sensor networks to detect rainfall induced landslides. In: Arabnia, H.R., Clincy, V.A., Yang, L.T. (eds.) Proceedings of the 2007 International Conference on Wireless Networks (ICWN 2007), Las Vegas, NV, USA, pp. 435–438, 25–28 June 2007Google Scholar
  6. 6.
    Lambot, S., Weihermuller, L., Huisman, J.A., et al.: Mapping spatial variation in surface soil water content: comparison of ground penetrating radar J. J. Hydrol. 42, W11403 (2006)Google Scholar
  7. 7.
    National Instruments. LabVIEW manual. http://www.ni.com/labview//
  8. 8.
    Peak, Y.L., Wen, Z.: Crop water based wireless sensor network condition monitoring system research and design. Agric. Eng. 25(2), 60–67 (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mohammed Moyed Ahmed
    • 1
    Email author
  • Gorre Narsimhulu
    • 1
  • D. Sreenivasa Rao
    • 1
  1. 1.Department of ECECollege of Engineering, Jawaharlal Nehru Technological University Hyderabad (JNTUH)HyderabadIndia

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