IIT Bombay 19 th Dec th Dec 2008 Tracking Dynamic Boundary Fronts using Range Sensors Subhasri Duttagupta (Ph. D student), Prof. Krithi Ramamritham Dept of Computer Sc. & Engg, Indian Institute of Technology, Bombay, India

IIT Bombay 19 th Dec th Dec 2008 Early Warning System For Landslide Prediction using Sensor Networks Traffic Management on Highways

IIT Bombay 3 19 th Dec 2008 Tracking Boundary Fronts Compute confidence band with high accuracy. Compute confidence band with high accuracy.  δ Width of the band Estimate band with minimum communication overheads Estimate band with minimum communication overheads n, δ Boundary Front Tracking When is the tornado going to hit the city? [Manfredi et al. 2005] n = number of observations k, loss of coverage

IIT Bombay 4 19 th Dec 2008 Combining Spatial and Temporal Estimation at a location Feedback improves the accuracy of Temporal Estimation yes Spatial Estimation no Multiple Observations Temporal Estimation Feedback from Spatial change > threshold Observation Spatial Estimation How to estimate Temporal Estimation When to update

IIT Bombay 5 19 th Dec 2008 Placement of Estimation Points Goal: Minimize LOC of interpolated band Goal: Minimize LOC of interpolated band Start with a small set of equidistant points and perform spatial estimation at these points Start with a small set of equidistant points and perform spatial estimation at these points Add more estimation points in the region of high variance (variance implies spatial variation) Add more estimation points in the region of high variance (variance implies spatial variation) regions with high variance Prediction Error Function can represent Prediction Error Function can represent LOC without the knowledge of actual boundary

IIT Bombay 6 19 th Dec 2008 Comparison of DBTR, SE, TE DBTR performs better by 2-4 % DBTR performs better by 2-4 % DBTR utilizes benefits of both the techniques DBTR utilizes benefits of both the techniques Difference in accuracy does not change with δ. Difference in accuracy does not change with δ. Spatial Estimation provides more accuracy for lower δ Spatial Estimation provides more accuracy for lower δ Temporal Estimation has better accuracy for larger δ Temporal Estimation has better accuracy for larger δ

IIT Bombay 7 19 th Dec 2008 Conclusions Tracking dynamic boundary fronts using range sensors Tracking dynamic boundary fronts using range sensors DBTR tracks both spatial and temporal variations with low communication overheads DBTR tracks both spatial and temporal variations with low communication overheads Spatial estimation technique uses kernel smoothing to reduce the effect of noise Spatial estimation technique uses kernel smoothing to reduce the effect of noise Temporal estimation technique uses Kalman filter model- based approach updates estimate before the boundary moves out of confidence band Temporal estimation technique uses Kalman filter model- based approach updates estimate before the boundary moves out of confidence band

IIT Bombay 8 19 th Dec 2008 DBTR: Dynamic Boundary Tracking Spatial variations captured using spatial estimation Temporal variations captured using temporal estimation Interpolation over estimates at k estimation points

IIT Bombay 9 19 th Dec 2008 Sensing nodes Cluster heads TE(x p1 ) actual boundary x p1 TE(x p2 ) x p2 h neighborhood Location of Spatial Estimation (SE) and Temporal Estimation (TE) SE(x p1, x p2 ) SE(x p1 )