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Where and when should sensors move? Sampling using the expected value of information
Sytze de Bruin1, Daniela Ballari, Arnold K Bregt
1Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands. sytze.debruin@wur.nl
Sensors (Basel, Switzerland)
|February 28, 2013
Summary
Environmental sensor networks improve decision-making by iteratively adding data to minimize misclassification costs. This approach optimizes mobile sensor placement for effective environmental accident management.
Area of Science:
- Environmental science
- Geostatistics
- Sensor networks
Background:
- Environmental accidents often occur with insufficient initial data for effective management.
- Optimal sampling strategies are crucial for accurate environmental monitoring and decision-making.
Purpose of the Study:
- To develop and demonstrate an iterative sensor sampling method that maximizes the expected value of information for decision-making.
- To minimize aggregated expected misclassification costs in environmental monitoring.
- To account for measurement errors, class-specific costs, and mobile sensor web constraints.
Main Methods:
- Iterative data acquisition maximizing the global expected value of information.
- Minimization of aggregated expected misclassification costs.
- Incorporation of measurement errors and class omission/commission costs.
- Utilizing cost distances for mobile sensor web constraints.
- Application of indicator kriging for probability computations.
- Employing a genetic algorithm for multi-sensor relocation optimization.
Main Results:
- The proposed iterative sampling method significantly outperformed random sampling and kriging variance minimization in terms of true misclassification costs.
- Demonstrated effectiveness in both static and dynamic environmental phenomena using synthetic examples.
- Successfully computed probabilities of contamination levels exceeding critical thresholds.
- Identified optimal sensor relocation strategies using cost distances and genetic algorithms.
Conclusions:
- The developed iterative sensor sampling strategy enhances environmental accident management by optimizing data acquisition.
- This approach provides a robust framework for minimizing misclassification costs and improving decision-making under uncertainty.
- The method is adaptable to various environmental scenarios and sensor network configurations.
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