Adaptive Sampling with Mobile WSN: Simultaneous robot by Koushil Sreenath, M.F. Mysorewalla, Dan O. Popa, Frank L.

By Koushil Sreenath, M.F. Mysorewalla, Dan O. Popa, Frank L. Lewis

This informative textual content for graduate scholars, researchers and practitioners engaged on cellular instant sensor networks presents theoretical established algorithms with a spotlight in the direction of sensible implementation.

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Additional resources for Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields

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0 0 50 Sample no. Error in a 0 50 Sample no. Error in b 0 50 Sample no. 2-Norm of error 5 0 100 200 300 Distance (units) 2-Norm of error 10 5 0 0 20 40 No. of samples 0 60 10 20 Sample no. P(b) 0 10 20 Sample no. –2 10 20 Sample no. 10 0 10 20 Sample no. 100 200 300 Distance (units) 10 5 0 20 40 No. of samples 60 5 0 0 P(c) 0 0 1 10 20 Sample no. Error in c 0 0 5 0 100 200 300 Distance (units) 10 20 Sample no. 5 0 10 0 –10 50 Sample no. c 1 Error in b 2 0 0 5 0 Error in a 10 10 0 0 0 10 20 Sample no.

6, it is apparent that for raster scan (RS), increasing the grid size reduces the number of samples required to meet the convergence criterion. The number of samples per row is smaller, and hence more rows Greedy AS with grid size = 5, horizon size = 19 Greedy AS with grid size = 5, horizon size = 19 100 100 60 z 80 60 z 80 40 40 100 20 100 20 80 0 0 60 0 20 Sample no. 1 0 0 Error covariances:P(a) 10 10 5 20 Sample no. P(b) 0 20 Sample no. Error in a 10 5 20 Sample no. 0 0 20 Sample no. Error in b 2 –2 5 0 0 5 0 0 20 Sample no.

X0 200 80 0 50 100 150 Sample no. y0 5 P(σ) 0 50 100 150 Sample no. P(x0) 0 200 10 10 5 5 0 50 100 150 Sample no. P(y0) 200 0 50 100 150 Sample no. 200 70 65 30 60 Error in a 55 0 50 100 150 Sample no. Error in σ 10 0 0 –5 50 100 150 Sample no. 200 –10 0 100 150 Sample no. 2-Norm of error 0 10 –5 0 –10 50 100 150 Sample no. 200 –15 0 50 100 150 Sample no. 0 500 Distance (units) 200 1000 20 10 0 0 50 100 150 No. of samples 0 200 30 5 20 100 150 Sample no. 10 200 Error in y0 Error in x0 50 20 0 50 0 30 5 10 0 200 200 2-Norm of error covariance 200 15 2-Norm of error covariance 100 150 Sample no.

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