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Sensors 2012, 12(12), 16274-16290; doi:10.3390/s121216274
Article

Where and When Should Sensors Move? Sampling Using the Expected Value of Information

1,* , 2
 and 1
Received: 27 September 2012; in revised form: 14 November 2012 / Accepted: 20 November 2012 / Published: 26 November 2012
(This article belongs to the Special Issue Workshop Sensing A Changing World 2012)
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Abstract: In case of an environmental accident, initially available data are often insufficient for properly managing the situation. In this paper, new sensor observations are iteratively added to an initial sample by maximising the global expected value of information of the points for decision making. This is equivalent to minimizing the aggregated expected misclassification costs over the study area. The method considers measurement error and different costs for class omissions and false class commissions. Constraints imposed by a mobile sensor web are accounted for using cost distances to decide which sensor should move to the next sample location. The method is demonstrated using synthetic examples of static and dynamic phenomena. This allowed computation of the true misclassification costs and comparison with other sampling approaches. The probability of local contamination levels being above a given critical threshold were computed by indicator kriging. In the case of multiple sensors being relocated simultaneously, a genetic algorithm was used to find sets of suitable new measurement locations. Otherwise, all grid nodes were searched exhaustively, which is computationally demanding. In terms of true misclassification costs, the method outperformed random sampling and sampling based on minimisation of the kriging variance.
Keywords: iterative sampling; adaptive sampling; infill sampling; decision analysis; environmental monitoring; geostatistics; mobile sensors iterative sampling; adaptive sampling; infill sampling; decision analysis; environmental monitoring; geostatistics; mobile sensors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

de Bruin, S.; Ballari, D.; Bregt, A.K. Where and When Should Sensors Move? Sampling Using the Expected Value of Information. Sensors 2012, 12, 16274-16290.

AMA Style

de Bruin S, Ballari D, Bregt AK. Where and When Should Sensors Move? Sampling Using the Expected Value of Information. Sensors. 2012; 12(12):16274-16290.

Chicago/Turabian Style

de Bruin, Sytze; Ballari, Daniela; Bregt, Arnold K. 2012. "Where and When Should Sensors Move? Sampling Using the Expected Value of Information." Sensors 12, no. 12: 16274-16290.


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