Within the Industry 4.0 vision, digital twins (DTs) have gained great attention as a promising approach to improve remote monitoring and control by means of virtual representations of physical objects. However, while DTs are becoming more and more sophisticated and even adopted for mission-critical applications, their resilience assessment has not received the required consideration yet. This article originally proposes chaos engineering to assess and improve the resilience of DTs by testing multiple aspects of industrial environments in a coordinated, automated, and replicable manner. First, the article discusses why and how chaos engineering is promising to improve the resilience of DTs. Then, it identifies and introduces a set of chaos engineering profiles specifically designed to take into account the many aspects an industrial environment is composed of. Finally, it shows the feasibility of assessing the resilience of a proof-of-concept DT through a testbed based on widely-adopted, open-source tools.

The wide adoption of IoT involves a previously unseen level of complexity imposing to multiple stakeholders to deal with and trust the complex integration of applications with a plethora of heterogeneous devices. In this context, Digital Twins (DTs) have emerged as a suitable paradigm for bridging the digital and physical domains by masking the complexity of the latter with established software interfaces made available to applications by the former. In this work, we discuss how DTs can be designed, deployed, and managed to enable end-to-end trustworthiness between applications and the physical domain. Particularly: (i) we identify the key characteristics enabling end-to-end DT trustworthiness, (ii) we evaluate the degree to which available DT platforms support these characteristics, (iii) we highlight a blueprint architecture paving the way to innovative DT platforms natively supporting end-to-end trustworthiness, and (iv) we show the benefits of our proposal with an industrial IoT use case.

Digital Twins (DTs) have recently emerged as a valuable approach for modeling, monitoring, and controlling physical objects in Industrial Internet of Things applications. Measuring the quality of entanglement between the digital and physical counterparts plays a crucial role in the adoption of DTs. In this context, entanglement denotes how well a DT mirrors its counterpart and the extent to which the behavior of the physical counterpart aligns with the commands issued by the DT. In this paper we propose a concise yet expressive and original metric for representing the quality of entanglement, namely Overall Digital Twin Entanglement (ODTE), based on two key factors: timeliness and completeness, i.e., the freshness of the collected data and the ratio between collected and total data, respectively. In addition, the paper describes how we have built our industrial testbed implemented on top of Kubernetes, where we show practical applications of the proposed ODTE metric by highlighting and discussing its benefits in realistic use cases.

The development of the Digital Twin (DT) approach is tilting research from initial approaches that aim at promoting early adoption to sophisticated attempts to develop, deploy, and maintain applications based on DTs. In this context, we propose a highly dynamic and distributed ecosystem where containerized DTs co-evolve with an orchestration middleware. DTs provide digitalized representations of the targeted physical systems, while the orchestration middleware monitors and re-configures the deployed DTs in light of application constraints, available resources, and the quality of cyber-physical entanglement. First, we lay out the reference scenario. Then, we discuss the limitations of current approaches and identify a set of requirements that shape both DTs and the orchestration middleware. Subsequently, we describe a blueprint architecture that meets those requirements. Finally, we report empirical evidence on both the feasibility and the effectiveness of a proof-of-concept implementation of the proposed ecosystem.

The industrial Internet of things (IIoT) has radically modified industrial environments, not only enabling novel industrial applications but also significantly increasing the amount of generated network traffic. Nowadays, a major concern is to support network-intensive industrial applications while ensuring the prompt and reliable delivery of mission-critical traffic flows concurrently traversing the industrial network. To this end, we propose application-aware network traffic management. The goal is to satisfy the requirements of industrial applications through a form of traffic management, the decision making of which is also based on what is carried within packet payloads (application data) in an efficient and flexible way. Our proposed solution targets multi-access edge computing (MEC)-integrated industrial environments, where on-premises and off-premises edge computing resources are used in a coordinated way, as it is expected to be in future Internet scenarios. The technical pillars of our solution are edge-powered in-network processing (eINP) and software-defined networking (SDN). The concept of eINP differs from INP because the latter is directly performed on network devices (NDs), whereas the former is performed on edge nodes connected via high-speed links to NDs. The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through edge computing, either on-premises or in the MEC-enabled cellular network. The reported in-the-field experimental results show the proposal feasibility and its primary tradeoffs in terms of performance and confidentiality.