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Early Detection System for Landslides using WSN

This paper proposes a wireless sensor network (WSN) solution to detect the early signals preceding a landslide. The system architecture includes sensor columns with geophones, strain gauges, and pore pressure transducers. The detection, classification, and localization algorithms are used to estimate displacement, identify the slip surface, and determine the movement of sensors. The proposed system is evaluated based on localization error, slip plane estimation, and communication reliability. This WSN solution aims to predict landslides and mitigate their potential impact.

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Early Detection System for Landslides using WSN

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  1. Outline • Background on Landslides • Landslides Prediction • System Architecture • Solution • Evaluation

  2. Landslides • A landslide is an catastrophic event where a block of earthen mass slides downhill. • Cause significant loss of life and billions of dollars each year.

  3. Landslides Prediction • Although a basic understanding of the landslides is available, system that predict the occurrence of a landslide do NOT exist. • Why? • The lack of field measurements over large temporal and spatial scales.

  4. Landslides Prediction • The development of a landslide is a temporal process • takes as long as a year to develop • Movement speed, several cm per month • Landslides are spatial in nature. • Position • Movement direction This paper propose a WSN to detect the early signals preceding a landslide.

  5. System Architecture • Sensor Column • Geophone • Strain Gauge • Pore Pressure Transducer • …

  6. System Architecture • Deployment • A network of sensor columns • Placed in vertical holes drilled over the hill surface • Using sensor columns to detect movements

  7. Solution Outline • Geophones estimate displacement dij(t) • Based on distance matrix D=[dij(t)] • Detection • Determine whether slip surface has formed • Classification • Estimate subset of sensors that moved • Localization • Compute location of slip surface

  8. Example • Detection & Classification & Localization

  9. Detection • Use strain gauges on each sensor column • Can measure changes in their length due to deformation

  10. Classification • Determine which sensors are above and below the slip surface • Basic principles: Distance between two nodes • Below the slip surface should not change • Across the slip surface is likely to change • Above the slip surface would see a small change • The nodes located closest to the known rigid part are unlikely to move. (anchor nodes)

  11. Classification • Voting Algorithm • Note state: {0, 1, U} • Initially: anchors=0, rest=U • Repeat until all nodes classified • Update node i’s state based on votes from neighbors j • If j in U, no vote • If Δdij=0, vote is equal to j’s state • If Δdij≠0, vote is equal to complement of j’s state

  12. Localization • Localize moved nodes using trilateration • Slip surface estimation

  13. Evaluation • Evaluation metrics • Average and std. dev of localization error • Max. distance between actual and estimated slip plane • Abstract network model • Communications are error-free • Nodes do not fail

  14. Prediction Finite Element Analysis Temporal & Spatial Measurement Simple Measurement Summary • Propose a WSN for the prediction of landslides • Design a system to detect the early signals preceding a landslides. WSN Civil Engineering

  15. Remarks • Localization for a specific application (landslides prediction) • NOT for general purpose. • Localization for a group of sensors (sensors above slip surface) • NOT for a single sensor node. • Localization for moving sensor • although there is only a little movement

  16. Thanks!

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