Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India.
Software Research Institute, Technological University of the Shannon, Midlands Midwest, N37HD68 Athlone, Ireland.
Sensors (Basel). 2022 Apr 15;22(8):3039. doi: 10.3390/s22083039.
Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system using Convolutional Neural Network (CNN) with a smaller number of gas sensor elements. We have identified redundant gas sensor elements in a gas sensor array and removed them to reduce the power consumption without significant deviation in the node's performance. The inevitable variation in the performance due to removing redundant sensor elements has been compensated using specialized data pre-processing (zero-padded virtual sensors and spatial augmentation) and CNN. The experiment is demonstrated to classify and quantify the four hazardous gases, viz., acetone, carbon tetrachloride, ethyl methyl ketone, and xylene. The performance of the unoptimized gas sensor array has been taken as a "baseline" to compare the performance of the optimized gas sensor array. Our proposed approach reduces the power consumption from 10 Watts to 5 Watts; classification performance sustained to 100 percent while quantification performance compensated up to a mean squared error (MSE) of 1.12 × 10. Thus, our power-efficient optimization paves the way to "computation on edge", even in the resource-constrained 6G-IoT paradigm.
超低功耗是 6G-IoT 生态系统中的关键性能指标。该生态系统中的传感器节点还能够运行轻量级人工智能 (AI) 模型。在这项工作中，我们通过使用卷积神经网络 (CNN) 实现了具有较少气体传感器元件的气体传感器系统的高性能。我们已经确定了气体传感器阵列中的冗余气体传感器元件，并将其删除，以在不显著降低节点性能的情况下降低功耗。由于去除冗余传感器元件而导致的不可避免的性能变化已通过专门的数据预处理（零填充虚拟传感器和空间增强）和 CNN 得到补偿。该实验用于对四种危险气体（丙酮、四氯化碳、甲乙酮和二甲苯）进行分类和定量。未优化的气体传感器阵列的性能被用作“基线”，以比较优化的气体传感器阵列的性能。我们提出的方法将功耗从 10 瓦降低到 5 瓦；分类性能保持在 100%，而量化性能补偿高达均方误差 (MSE) 的 1.12×10。因此，我们的高能效优化为“边缘计算”铺平了道路，即使在资源受限的 6G-IoT 范式中也是如此。
