A Quantitative Array Optimization Method for the Electronic Nose System Based on Edge Computing and MEMS Sensors

Abstract

The selection of the sensor array represents a pivotal aspect of the system design for the electronic nose (E-nose). In practical applications, achieving an optimal balance between array size and system performance is often challenging. Therefore, realizing a high-performance E-nose with a minimum number of sensors is necessary, particularly for portable E-noses with limited size and power. This letter proposes a cost-effectiveness ratio (CER) as an array optimization criterion to address these issues. The CER is defined for quantifying costs and benefits as a basis for array optimization. Applying the designed array optimization criterion to the portable E-nose system, which comprises eight MEMS sensors, achieves an 80% prediction accuracy while reducing the number of sensors by nearly 40%. In addition, the concept of extreme sensor number is proposed to illustrate the existence of limit values for the number of sensors in the process of array optimization. This study offers a foundation for quantitative metrics for sensor array optimization, which serves as a crucial reference for the design of size- and power-sensitive portable E-nose systems.