Digital Twin Evolution for Sustainable Smart Ecosystems

TL;DR

This paper explores how digital twins in smart ecosystems can evolve in response to changing conditions, using a taxonomy to guide software practitioners in managing their evolution effectively.

Contribution

It introduces the 7R taxonomy for digital twin evolution and demonstrates its application through four case scenarios in a citizen energy community.

Findings

The 7R taxonomy effectively categorizes digital twin evolution scenarios.

Case studies illustrate practical application of the taxonomy.

Provides actionable insights for managing digital twin evolution.

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.