Understanding and improving model-driven IoT systems through accompanying digital twins

Abstract

Developers questioning why their system behaves differently than expected often have to rely on time-consuming and error-prone manual analysis of log files. Understanding the behavior of Internet of Things (IoT) applications is a challenging task because they are not only inherently hard-to-trace distributed systems, but their integration with the environment via sensors adds another layer of complexity. Related work proposes to record data during the execution of the system, which can later be replayed to analyze the system. We apply the model-driven development approach to this idea and leverage digital twins to collect the required data. We enable developers to replay and analyze the system's executions by applying model-to-model transformations. These transformations instrument component and connector (C&C) architecture models with components that reproduce the system's environment based on the data recorded by the system's digital twin. We validate and evaluate the feasibility of our approach using a heating, ventilation, and air conditioning (HVAC) case study. By facilitating the reproduction of the system's behavior, our method lowers the barrier to understanding the behavior of model-driven IoT systems.