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Subset Sensor Selection Optimization: A Genetic Algorithm Approach with Innovative Set Encoding Methods
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  • Aurelien Meray ,
  • Roger Boza ,
  • Masudur R. Siddiquee ,
  • Cesar Reyes ,
  • M. Hadi Amini ,
  • Nagarajan Prabakar
Aurelien Meray
Florida International University

Corresponding Author:[email protected]

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Roger Boza
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Masudur R. Siddiquee
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Cesar Reyes
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M. Hadi Amini
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Nagarajan Prabakar
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The study presents a new approach for solving the sensor subset selection problem using set encoding and genetic algorithms, aiming to minimize the number of sensors while maintaining accurate spatial estimation. The proposed method, tested on groundwater data from the Savannah River Site, introduces novel crossover and mutation methods, outperforming a previous greedy method with an R2 higher than 0.98 for reduced sensor counts.
2023Published in IEEE Sensors Journal on pages 1-1. 10.1109/JSEN.2023.3322596