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 offers theoretical established algorithms with a spotlight in the direction of sensible implementation.
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Additional info for Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields
Qualitatively, a multi-agent AS problem for spatio-temporal fields can be posed as follows: We wish to describe an unknown non-linear spatio-temporal field variable via a parametric approximation Z ¼ Z(A, X, t) depending on an unknown parameter vector A, position vector X and time t. The field is recovered by using N robotic vehicles, sampling the field with localization and sensing uncertainty. We wish to ● ● ● Decide what sampling locations X ki , 1 i N, 1 k n, should be chosen such that the uncertainty in estimating the unknown parameter vector is minimized.
Once such scenario involved the deployment of multiple underwater vehicles for the environmental monitoring of large bodies of water, such as oceans, harbours, lakes, rivers and estuaries [13,78,86]. Predictive models and maps can be created by repeated measurements of physical characteristics, such as water temperature, dissolved oxygen, current strength and direction, or bathymetry. However, because the sampling volume could be large, only limited CH003 15 February 2011; 19:30:48 32 Adaptive sampling with mobile WSN measurements are available.
15 for the three sampling schemes for individual parameter estimates, error covariance of individual parameters, error in individual parameter estimates, error in individual error covariance, 2-norm of parameter estimates and 2-norm of error covariance of parameters. 4 with cases on using different grid sizes also. As in the previous sections, GAS leads to a much smaller sampling distance, while AS requires the least number of samples. 12 Applying minimum error covariance criterion for different parameters results in specific areas of the Gaussian to be sampled CH003 15 February 2011; 19:30:55 48 Adaptive sampling with mobile WSN Raster scan with grid size = 5 120 100 z 80 60 100 40 80 20 0 60 20 40 40 20 60 80 x (units) σ a 110 Error covariances:P(a) 60 50 90 50 100 150 Sample no.