Slip surface localization in wireless sensor networks for landslide prediction

  • Authors:

  • Andreas Terzis;Annalingam Anandarajah;Kevin Moore;I-Jeng Wang

  • Affiliations:

  • Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Colorado School of Mines, Golden, CO;Johns Hopkins University, Baltimore, MD

  • Venue:
  • Proceedings of the 5th international conference on Information processing in sensor networks
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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.