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Additional info for Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields
Chemotaxis and anemotaxis are the most common techniques used for estimating chemical distributions. Chemotaxis relies on the local gradient of the chemical agent concentration, whereas anemotaxis-based approaches require that the agent move in the upwind direction. The robots used for source localization are equipped with anemometric sensors and gas sensors, and in some cases with vision. Several algorithms such as step-by-step, zigzag and active sampling can be found in the literature [10,15,16,81–83].
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. 5 0 0 0 20 Sample no. Error in c 1 0 0 20 Sample no. 5 2 20 80 x (units) 0 2-Norm of error b 4 60 –1 0 20 Sample no. 500 1000 Distance (units) 10 2-Norm of error a 100 40 40 y (units) 20 80 x (units) 5 20 40 40 10 60 5 0 0 10 20 No. of samples 30 2-Norm of error covariance 20 0 80 0 0 60 y (units) 0 10 5 0 0 500 1000 Distance (units) 0 10 20 No.
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 . 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.