Adaptive Sampling with Mobile WSN: Simultaneous robot - download pdf or read online

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 presents theoretical established algorithms with a spotlight in the direction of useful implementation.

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

Example text

10,11,14–16,78–83]. One of the problems most commonly discussed in mobile wireless sensor networks (WSNs) is team objectives for exploration and mapping in an unknown environment. Sensor deployment for sampling can be considered a variant of the exploration and mapping problem. The aim in mapping and exploration is to build a global map of the environment by sequentially visiting each location with one or more robots, whereas in information-aware sampling the aim is to choose the best locations that minimize the uncertainty of the field estimate.

Are minimized. Distribute the computational and communication load, and the sampling space among N robots in an efficient manner. 1 Global search AS In the absence of model and measurement uncertainties in localization and field parameters, the sampling task would be a deterministic case, and all the sample locations Xi (1 i N) will provide same information about the unknown X and A. Hence, there would be no difference in random, row-by-row or AS. But in the presence of uncertainties discussed, the error covariance (uncertainty) of states can be used to decide the next best sampling location.

We used the following numerical assumptions: ● ● ● ● ● ● Actual parameters: ½ a s x0 y0 Š ¼ ½ 100 50 40 Initial parameter estimates: ½ 80 40 35 75 Š. 2 3 10 0 0 0 6 0 10 0 07 7. Initial error covariance: P ¼ 6 4 0 0 10 0 5 0 0 0 10 60 Š. Model error covariance: Q ¼ 0. Measurement error covariance: R ¼ 1. Convergence criteria: jj A À A^kþ1 jj < 2:5. 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.

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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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