Dembski Jerzy, Wiszniewski Bogdan, Kołakowska Agata
Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland.
Sensors (Basel). 2025 Sep 5;25(17):5526. doi: 10.3390/s25175526.
In this paper, three alternative solutions to the problem of detecting and cleaning anomalies in soil signal time series, involving the use of artificial neural networks deployed on in situ data measurement end devices, are proposed and investigated. These models are designed to perform calculations on MCUs, characterized by significantly limited computing capabilities and a limited supply of electrical power. Training of neural network models is carried out based on data from multiple sensors in the supporting computing cloud instance, while detection and removal of anomalies with a trained model takes place on the constrained end devices. With such a distribution of work, it is necessary to achieve a sound compromise between prediction accuracy and the computational complexity of the detection process. In this study, neural-primed heuristic (NPH), autoencoder-based (AEB), and U-Net-based (UNB) approaches were tested, which were found to vary regarding both prediction accuracy and computational complexity. Labeled data were used to train the models, transforming the detection task into an anomaly segmentation task. The obtained results reveal that the UNB approach presents certain advantages; however, it requires a significant volume of training data and has a relatively high time complexity which, in turn, translates into increased power consumption by the end device. For this reason, the other two approaches-NPH and AEB-may be worth considering as reasonable alternatives when developing in situ data cleaning solutions for IoT measurement systems.
本文提出并研究了三种用于检测和清理土壤信号时间序列异常问题的替代解决方案，这些方案涉及在原位数据测量终端设备上部署人工神经网络。这些模型旨在在微控制器上执行计算，其特点是计算能力显著受限且电力供应有限。神经网络模型的训练基于支持计算云实例中的多个传感器的数据，而使用训练好的模型进行异常检测和去除则在受限的终端设备上进行。通过这样的工作分配，有必要在预测精度和检测过程的计算复杂度之间达成合理的平衡。在本研究中，测试了神经启发式（NPH）、基于自动编码器（AEB）和基于U-Net（UNB）的方法，发现它们在预测精度和计算复杂度方面存在差异。使用标记数据训练模型，将检测任务转化为异常分割任务。所得结果表明，UNB方法具有一定优势；然而，它需要大量的训练数据且时间复杂度相对较高，这反过来又会导致终端设备功耗增加。因此，在为物联网测量系统开发原位数据清理解决方案时，另外两种方法——NPH和AEB——可能值得作为合理的替代方案加以考虑。
