Abstract
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.
Highlights
Systems based on the Internet of Things (IoT-based systems) such as ambient assisted living systems (AAL) [1] and smart cyber–physical systems (CPS) [2], by interacting with the physical environment through actuators, face many challenges, pertaining to reliability, safety and resilience requirements
On the basis of this assumption, the contribution of this paper is two-fold: 1. With the assumption that observations are stochastically correlated with the IoT-based system conditions, we develop a novel generic clustering-based algorithm for learning both the Input-Output Hidden Markov Models (IOHMMs) structure Φ and the parameters λ from continuous observations
The approach proposed in this paper considers the modeling of the legitimate behavior of an IoT-based system as an IOHMM and compares this model with that learned from field observations
Summary
Systems based on the Internet of Things (IoT-based systems) such as ambient assisted living systems (AAL) [1] and smart cyber–physical systems (CPS) [2], by interacting with the physical environment through actuators, face many challenges, pertaining to reliability, safety and resilience requirements. The quantitative effectiveness assessment, as quality (e.g., quality of service (QoS) [7,8], quality of experience (QoE) [9]), performance and reliability assessment metrics (e.g., time-tofailure (TTF), remaining useful life (RUL), etc.), can be used to highlight any deterioration of an IoT-based system and trigger analyses leading to corrective actions These assessments do not provide guidance to designers that would help them direct their research and identify the possible causes of drifts in effectiveness, increasing the time needed to resolve them. (a) The learning algorithm is fed with observations corresponding to the effects expected to be produced by an IoT-based system, leading to the learning of a model of the legitimate behavior This model can further be used to quantitatively assess the effectiveness of the system at run-time as done in [5]. On the basis of these models, an algorithm is proposed and used to build a directed dissimilarity graph that highlights differences between both models, thereby helping designers direct their research and identify the possible causes of drifts in effectiveness
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
