CNRS, Laboratoire I3S, Université Côte d'Azur (UCA), UMR 7271, 06900 Sophia Antipolis, France.
Telecom Physique, Université de Strasbourg, 67400 Illkirch-Graffenstaden, France.
Sensors (Basel). 2021 Jan 13;21(2):527. doi: 10.3390/s21020527.
IoT-based systems, when interacting with the physical environment through actuators, are complex systems difficult to model. Formal verification techniques carried out at design-time being often ineffective in this context, these systems have to be quantitatively evaluated for effectiveness at run-time, i.e., for the extent to which they behave as expected. This evaluation is achieved by confronting a model of the effects they should legitimately produce in different contexts to those observed in the field. However, this quantitative evaluation is not informative on the drifts in effectiveness, it does not help designers investigate their possible causes, increasing the time needed to resolve them. To address this problem, and assuming that models of legitimate behavior can be described by means of Input-Output Hidden Markov Models (IOHMMs), a novel generic unsupervised clustering-based IOHMM structure and parameters learning algorithm is developed. This algorithm is first used to learn a model of legitimate behavior. Then, a model of the observed behavior is learned from observations gathered in the field. A second algorithm builds a dissimilarity graph that makes clear structural and parametric differences between both models, thus providing guidance to designers to help them investigate possible causes of drift in effectiveness. The approach is validated on a real world dataset collected in a smart home.
基于物联网的系统通过执行器与物理环境交互，是难以建模的复杂系统。在这种情况下，设计时进行的形式验证技术往往效果不佳，因此这些系统必须在运行时进行定量评估，即评估它们在多大程度上按预期运行。这种评估是通过将它们在不同环境中应产生的效果模型与在现场观察到的效果模型进行对比来实现的。然而，这种定量评估不能提供有效性漂移的信息，也不能帮助设计人员调查其可能的原因，从而增加了解决这些问题所需的时间。为了解决这个问题，并假设合法行为的模型可以通过输入输出隐马尔可夫模型（IOHMM）来描述，开发了一种新颖的通用无监督基于聚类的 IOHMM 结构和参数学习算法。该算法首先用于学习合法行为模型。然后，从现场收集的观测数据中学习观察到的行为模型。第二个算法构建了一个不相似性图，清楚地显示了两个模型之间的结构和参数差异，从而为设计人员提供指导，帮助他们调查有效性漂移的可能原因。该方法在智能家居中收集的真实数据集上进行了验证。
