Digital Twin Evolution for Sustainable Smart Ecosystems
Judith Michael, Istvan David, Dominik Bork
TL;DR
This paper addresses the challenge of evolving digital twins in sustainable smart ecosystems by applying the $7R$ taxonomy to a citizen energy community case. It demonstrates how a digital twin can evolve from monitoring to predictive, AI-driven, and reinforcement-learning-enabled configurations, guided by a structured set of evolutionary imperatives. The work provides actionable action points for software engineers, clarifies the relative software-intensity of each imperative, and discusses organizational processes, operationalizations, and vendor considerations. By bridging software engineering practices with cyber-physical evolution, the paper offers a practical path to more robust and sustainable smart ecosystems.
Abstract
Smart ecosystems are the drivers of modern society. They control infrastructures of socio-techno-economic importance, ensuring their stable and sustainable operation. Smart ecosystems are governed by digital twins -- real-time virtual representations of physical infrastructure. To support the open-ended and reactive traits of smart ecosystems, digital twins need to be able to evolve in reaction to changing conditions. However, digital twin evolution is challenged by the intertwined nature of physical and software components, and their individual evolution. As a consequence, software practitioners find a substantial body of knowledge on software evolution hard to apply in digital twin evolution scenarios and a lack of knowledge on the digital twin evolution itself. The aim of this paper, consequently, is to provide software practitioners with tangible leads toward understanding and managing the evolutionary concerns of digital twins. We use four distinct digital twin evolution scenarios, contextualized in a citizen energy community case to illustrate the usage of the 7R taxonomy of digital twin evolution. By that, we aim to bridge a significant gap in leveraging software engineering practices to develop robust smart ecosystems.