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

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

ISBN-10: 184919257X

ISBN-13: 9781849192576

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

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

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For training an RBF neural network, different strategies exist. In the simplest case, the hidden layer of the network is not optimized, which means that there is no optimal learning strategy for ci and si. But there are several strategies for placing the centre locations before learning of other parameters begins. One such strategy is the random centre placement, which is considered a reasonable initialization method for some of the advanced learning schemes [73]. A straightforward improvement of random selection of centres is the application of clustering CH003 15 February 2011; 19:30:48 28 Adaptive sampling with mobile WSN techniques discussed in the previous section.

The advantage is that the robot covers less distance that saves time and number of samples. Also, it reduces the complexity of the algorithm because only the anticipated error covariance of sampling neighbouring locations is calculated. The heuristic GAS algorithm helps to answer the following question: Given a parametric field variable Z ¼ Z(A, X) depending on an unknown parameter vector A and position vector X, and a robotic vehicle/sensor node navigating the field with localization uncertainty, what sampling locations Xi (where 1 i N), in the immediate vicinity of the current sampling location, should be chosen such that a trade-off between the uncertainty in estimating the unknown parameter vector and the sampling distance is minimized?

5, the 2-norm of error covariance of all parameter estimates is used as information measure, and the AS algorithm produces the combined effect to select appropriate locations. 1. 1 – namely that minimizing the error covariance of parameters b and c requires more distance and number of samples to achieve the same convergence threshold. This is due to the fact that sampling along extreme x- and y-axes by minimizing uncertainty in b and c does not provide enough information about rest of the parameters.

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Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields by Koushil Sreenath, M.F. Mysorewalla, Dan O. Popa, Frank L. Lewis


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