The rapid advancement of Internet of Things (IoT) necessitates the development of optimized nanoparticle-based Chemiresistive Sensor (CRS) arrays that are energy-efficient, specific, and sensitive. This study introduces an 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 two 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 employed optimization strategy elevates the detection capability of CRS arrays while also pushing it closer to theoretical limits, promising significant implications for the development of low-cost, easily fabricable next-generation IoT sensor terminals.
@article{arxiv.2403.01642,
title = {Blue and Green-Mode Energy-Efficient Nanoparticle-Based Chemiresistive Sensor Array Realized by Rapid Ensemble Learning},
author = {Zeheng Wang and James Scott Cooper and Muhammad Usman and Timothy van der Laan},
journal= {arXiv preprint arXiv:2403.01642},
year = {2024}
}