Sensor Selection Optimization with Genetic Algorithms

Sensor networks and systems may incorporate sensors and sensor platforms of various quality and security. Current methods of sensor selection fail to produce effective and efficient decisions and to scale up to real-life cases. This paper develops and describes an intelligent optimization technique based on the genetic algorithms, whose execution time could be adjusted to meet real case requirements. The developed technique allows selection optimization based on the aggregation of multiple sensors and sensor platform indicators from quality and security domains. This technique has been implemented, and the empirical study of its execution on Android smartphones has been conducted. The results have been analyzed and prove the technique's efficiency in real-life cases.