The proliferation of IoT sensors and their deployment in various industries and applications has brought about numerous analysis opportunities in this Big Data era. However, drift of those sensor measurements poses major challenges to automate data analysis and the ability to effectively train and deploy models on a continuous basis. In this paper we study and test several approaches from the literature with regard to their ability to cope with and adapt to sensor drift under realistic conditions. Most of these approaches are recent and thus are representative of the current state-of-the-art. The testing was performed on a publicly available gas sensor dataset exhibiting drift over time. The results show substantial drops in sensing performance due to sensor drift in spite of the approaches. We then discuss several issues identified with current approaches and outline directions for future research to tackle them.
翻译:在 " 大数据时代 " 中,IoT传感器的扩散及其在各种行业和应用中的部署带来了许多分析机会,然而,这些传感器测量的漂移对数据分析自动化和持续有效培训和部署模型的能力提出了重大挑战。在本文件中,我们研究和测试文献中关于它们在现实条件下应对和适应感官漂流能力的若干方法。这些方法大多是最近才采用,因此代表了目前的最新技术。测试是在一个公开的气体传感器数据集上进行的,显示随着时间的推移不断漂移。结果显示,尽管采用这些方法,感官漂流导致感官性能大幅下降。然后,我们讨论与当前方法有关的若干问题,并概述今后研究的方向,以解决这些问题。