Blue and Green-Mode Energy-Efficient Chemiresistive Sensor Array
  Realized by Rapid Ensemble Learning
Zeheng Wang James Cooper Muhammad Usman Timothy van der Laan
The rapid advancement of Internet of Things (IoT) necessitates the
development of optimized Chemiresistive Sensor (CRS) arrays that are both
energy-efficient and capable. This study introduces a novel optimization
strategy that employs a rapid ensemble learning-based model committee approach
to achieve these goals. Utilizing machine learning models such as Elastic Net
Regression, Random Forests, and XGBoost, among others, the strategy identifies
the most impactful sensors in a CRS array for accurate classification: A
weighted voting mechanism is introduced to aggregate the models' opinions in
sensor selection, thereby setting up wo distinct working modes, termed "Blue"
and "Green". The Blue mode operates with all sensors for maximum detection
capability, while the Green mode selectively activates only key sensors,
significantly reducing energy consumption without compromising detection
accuracy. The strategy is validated through theoretical calculations and Monte
Carlo simulations, demonstrating its effectiveness and accuracy. The proposed
optimization strategy not only elevates the detection capability of CRS arrays
but also brings it closer to theoretical limits, promising significant
implications for the development of low-cost, easily fabricable next-generation
IoT sensor terminals.