Researches on the Internet of Things (IoT) and cloud computing have been pervasive in both the academic and industrial world. IoT and cloud computing are seen as cornerstones to digital transformation in the industry. However, restricted by limited resources and the lack of expertise in information and communication technologies, small- and medium-sized enterprises (SMEs) have difficulty in achieving digitalization of their business. In this paper, we propose a reference framework for SMEs to follow as a guideline in the journey of digital transformation. The framework features a three-stage procedure that covers business, technology, and innovation, which can be iterated to drive product and business development. A case study about digital transformation taking place in the vertical plant wall industry is detailed. Furthermore, some solution design principles that are concluded from real industrial practice are presented. This paper reviews the digital transformation practice in the vertical plant wall industry and aims to accelerate the pace of SMEs in the journey of digital transformation.

Historic buildings require good maintenance to sustain their function and preserve embodied heritage values. Previous studies have demonstrated the benefits of digitalization techniques in improving maintenance and managing threats to historic buildings. However, there still lacks a solution that can consistently organize data collected from historic buildings to reveal operating conditions of historic buildings in real-time and to facilitate various data analytics and simulations. This study aims to provide such a solution to help achieve preventive conservation. The proposed solution integrates Internet of Things and ontology to create digital twins of historic buildings. Internet of Things enables revealing the latest status of historic buildings, while ontology provides a consistent data schema for representing historic buildings. This study also gives a reference implementation by using public cloud services and open-source software libraries, which make it easier to be reused in other historic buildings. To verify the feasibility of the solution, we conducted a case study in the City Theatre, Norrköping, Sweden. The obtained results demonstrate the advantages of digital twins in providing maintenance knowledge and identifying potential risks caused by fluctuations of relative humidity.

Predicting the trajectories of pedestrians is critical for developing safe advanced driver assistance systems and autonomous driving systems. Most existing models for pedestrian trajectory prediction focused on a single dataset without considering the transferability to other previously unseen datasets. This leads to poor performance on new unseen datasets and hinders leveraging off-the-shelf labeled datasets and models. In this paper, we propose a transferable model, namely the “Spatial-Temporal-Spectral (STS) LSTM” model, that represents the motion pattern of pedestrians with spatial, temporal, and spectral domain information. Quantitative results and visualizations indicate that our proposed spatial-temporal-spectral representation enables the model to learn generic motion patterns and improves the performance on both source and target datasets. We reveal the transferability of three commonly used network structures, including long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and Transformers, and employ the LSTM structure with negative log-likelihood loss in our model since it has the best transferability. The proposed STS LSTM model demonstrates good prediction accuracy when transferring to target datasets without any prior knowledge, and has a faster inference speed compared to the state-of-the-art models. Our work addresses the gap in learning knowledge from source datasets and transferring it to target datasets in the field of pedestrian trajectory prediction, and enables the reuse of publicly available off-the-shelf datasets.

Plant Factory is a newly emerging industry aiming at transforming crop production to an unprecedented model by leveraging industrial automation and informatics. However, today’s plant factory and vertical farming industry are still in a primitive phase, and existing industrial cyber-physical systems are not optimal for a plant factory due to diverse application requirements on communication, computing and artificial intelligence. In this paper, we review use cases and requirements for future plant factories, and then dedicate an architecture that incorporates the communication and computing domains to plant factories with a preliminary proof-of-concept, which has been validated by both academic and industrial practices. We also call for a holistic co-design methodology that crosses the boundaries of communication, computing and artificial intelligence disciplines to guarantee the completeness of solution design and to speed up engineering implementation of plant factories and other industries sharing the same demands.