Digital twins are considered the next step in IoT-based cyber–physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

In the rapidly evolving landscape of Industry 4.0, the demand for skilled professionals well-versed in Internet of Things (IoT) technologies is escalating. However, a significant challenge faced in educational settings is the lack of comprehensive and effective methodologies for imparting practical knowledge and skills in IoT. This paper presents an approach for designing and implementing an Internet of Things (IoT) laboratory in which students may practice and comprehend many components of an IoT environment, such as analysis of sensor data, IoT platform development, and setup of messaging protocols. The teaching methodology adopts a Cyber-Physical System (CPS) framework, which integrates teachers, classrooms, and resources to create a comprehensive learning environment. Bloom's taxonomy is employed to assess the efficacy of the suggested technique in terms of cognitive skills and knowledge acquisition. The evaluation procedure demonstrated the advantages of a hybrid learning environment, which integrated both face-to-face and remote instruction. The case study is conducted in an engineering higher education course on first year students. The findings of this paper lay the groundwork for a remotely deliverable IoT training course, contributing to the advancement of IoT education and equipping students with the necessary skills for the evolving landscape of Industry 4.0.

Vertical farming has gained increased attention in recent years due to its capacity to reduce the environmental impact of agricultural production in terms of water consumption and soil and fertilizer usage. In the literature, many works describe and evaluate applications of vertical farming. However, no work addresses the issue of classifying the KPIs for vertical farming and highlights both the most assessed aspects and the lack of evaluations. The main contribution of this study is to conduct a literature review to identify and classify the KPIs used in vertical farming. To this aim, we first proposed a methodology to define the KPI categories. Then, we identified the KPIs used in the literature, and we classified them according to the defined categories. Finally, we analyzed the obtained results. As a result, a collection of 78 KPIs were compiled and organized into the proposed categories. The analyses on the frequency of the KPIs allow us to conclude that the KPIs related to productivity are the most used as compared to those related to sustainability and quality. Furthermore, very few papers perform a cross-category evaluation. This study underscores the necessity for a more balanced consideration of productivity, quality, and sustainability in the context of vertical farming.