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A Model for Low Power, High Speed and Energy Efficient Early Landslide Detection System Using IoT

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Abstract

A landslide, also called as landslip appear to be the most threatening disaster of all time especially in the hillside regions. It involves enormous surface movements including debris flows, slopes failure, rock fall etc. It mainly occurs when the land slopes become unstable. Other measures that also lead to landslides are Ground water changes, Earthquakes, Floods, Volcano eruptions and Heavy rainfalls. People among these hill areas doesn’t know about the disaster that’s about to happen massively. The perfect way to avoid such hazards is by predicting it at initial phase with maximum accuracy. There are many wired and wireless supervising systems available to detect landslides which require higher cost and maintenance. But we have a suitable solution for this with Internet of Things (IoT) based approach. It improves objects control and detection remotely between various networks thereby creating possibilities for direct communication between physical and computer-based world. By this approach Landslides can be predicted at the initial phase. If there is a higher chance of Landslide then an alert will be sent to disaster management sector immediately to take necessary precautions so that enormous precious lives can be protected. This paper proposes a model for IoT based Landslide detection mechanisms in detail.

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References

  1. Musaev, A., Wang, D., & Pu, C. (2015). LITMUS: A multi-service composition system for landslide detection. IEEE Transactions on Services Computing, 8(5), 715–726.

    Article  Google Scholar 

  2. Shibayama, T., & Yamaguchi, Y. (2014). A landslide detection based on the change of scattering power components between multi-temporal PolSAR data. In 2014 IEEE geoscience and remote sensing symposium (pp. 2734–2737). IEEE.

  3. Teja, G. R., Harish, V. K. R., Khan, D. N. M., Krishna, R. B., Singh, R., & Chaudhary, S. (2014). Land slide detection and monitoring system using wireless sensor networks (WSN). In 2014 IEEE international advance computing conference (IACC) (pp. 149–154). IEEE.

  4. Prabha, R., Ramesh, M. V., Rangan, V. P., Ushakumari, P. V., & Hemalatha, T. (2017). Energy efficient data acquisition techniques using context aware sensing for landslide monitoring systems. IEEE Sensors Journal, 17(18), 6006–6018.

    Article  Google Scholar 

  5. Wang, Y., Liu, Z., Wang, D., Li, Y., & Yan, J. (2017). Anomaly detection and visual perception for landslide monitoring based on a heterogeneous sensor network. IEEE Sensors Journal, 17(13), 4248–4257.

    Google Scholar 

  6. Giorgetti, A., Lucchi, M., Tavelli, E., Barla, M., Gigli, G., Casagli, N., et al. (2016). A robust wireless sensor network for landslide risk analysis: system design, deployment, and field testing. IEEE Sensors Journal, 16(16), 6374–6386.

    Article  Google Scholar 

  7. Wang, B. C. (2013). A landslide monitoring technique based on dual-receiver and phase difference measurements. IEEE Geoscience and Remote Sensing Letters, 10(5), 1209–1213.

    Article  Google Scholar 

  8. Jin, Y. Q., & Xu, F. (2011). Monitoring and early warning the debris flow and landslides using VHF radar pulse echoes from layering land media. IEEE Geoscience and Remote Sensing Letters, 8(3), 575–579.

    Article  Google Scholar 

  9. Bianchini, S., Cigna, F., Del Ventisette, C., Moretti, S., & Casagli, N. (2012). Detecting and monitoring landslide phenomena with TerraSAR-X persistent scatterers data: The Gimigliano case study in Calabria Region (Italy). In 2012 IEEE international geoscience and remote sensing symposium (pp. 982–985). IEEE.

  10. Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505–1515.

    Article  Google Scholar 

  11. Gupta, R., Das, C., Roy, A., Ganguly R., & Datta A. (2018). Arduino based temperature and humidity control for condensation on wettability engineered surfaces. In Emerging trends in electronic devices and computational techniques (pp. 1–6). IEEE.

  12. Tao, M., Hong, X., Qu, C., Zhang, J., & Wei, W. (2018). Fast access for ZigBee-enabled IoT devices using raspberry Pi. In 2018 Chinese control and decision conference (pp. 4281–4285). IEEE.

  13. Pahlevan, M., & Obermaisser, R. (2018). Evaluation of time-triggered traffic in time-sensitive networks using the OPNET simulation framework. In 26th Euromicro international conference on parallel, distributed and network-based processing (pp. 283–287). IEEE.

  14. Lin, Y.-D., Atov, I., & Pescape, A. (2018). Network testing and analytics. In IEEE communications magazine (pp. 170–170). IEEE.

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Correspondence to R. Dhanagopal.

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Dhanagopal, R., Muthukumar, B. A Model for Low Power, High Speed and Energy Efficient Early Landslide Detection System Using IoT. Wireless Pers Commun 117, 2713–2728 (2021). https://doi.org/10.1007/s11277-019-06933-7

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  • DOI: https://doi.org/10.1007/s11277-019-06933-7

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