Review on Smart Gas Sensing Technology

Area of Science:

• Environmental Science
• Materials Science
• Computer Science

Background:

• Internet-of-Things (IoT) technology drives demand for advanced gas sensors in smart homes and wearables.
• Traditional gas sensors suffer from cross-sensitivity and low selectivity in complex environments.
• Smart gas sensing methods enhance traditional techniques with sensor arrays, signal processing, and machine learning.

Purpose of the Study:

• To provide a comprehensive review of smart gas sensing technology.
• To outline the framework including sensor arrays, signal processing, and pattern recognition.
• To summarize recent advancements, challenges, and future directions.

Main Methods:

• Utilizing diverse gas sensor arrays with varied materials.
• Implementing signal processing for drift compensation and feature extraction.
• Applying machine learning algorithms like Support Vector Machine (SVM) and Artificial Neural Network (ANN) for gas pattern recognition.

Main Results:

• Summarized implementation, evaluation, and comparison of various smart gas sensing solutions.
• Highlighted key challenges: repeatability, reusability, circuit integration, miniaturization, and real-time sensing.
• Explored proposed solutions and future research directions.

Conclusions:

• Smart gas sensing offers a robust solution to limitations of traditional gas sensors.
• Future directions include addressing current challenges and exploring brain-like sensing approaches.
• This review provides a foundation for developing next-generation intelligent gas sensing systems.