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Generation of geometric Digital Twins of existing buildings relies on point cloud datasets and is still a manual-intensive and time-consuming process. This paper identifies the most frequent object types in buildings, analyses how current commercial software and state-of-the-art research methods to generate these objects. We summarise the main advantages of these methods and highlight limitations that limit these methods from broader adoption by the industry. Later, we identify the open challenges and discuss future directions to enable automating geometric Digital Twin generation.

The Internet of Things (IoT) concept has attracted a lot of attention from the research and innovation community for a number of years already. One of the key drivers for this hype towards the IoT is its applicability to a plethora of different application domains. However, infrastructures enabling experimental assessment of IoT solutions are scarce. Being able to test and assess the behavior and the performance of any piece of technology (i.e., protocol, algorithm, application, service, etc.) under real-world circumstances is of utmost importance to increase the acceptance and reduce the time to market of these innovative developments. This paper describes the federation of eleven IoT deployments from heterogeneous application domains (e.g., smart cities, maritime, smart building, crowd-sensing, smart grid, etc.) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day. The paper summarizes the resources that are made available through a cloud-based platform. The main contributions from this paper are twofold. In the one hand, the insightful summary of the federated data resources are relevant to the experimenters that might be seeking for an experimental infrastructure to assess their innovations. On the other hand, the identification of the challenges met during the testbed integration process, as well as the mitigation strategies that have been implemented to face them, are of interest for testbed providers that can be considering to join the federation. This work is partially funded by the European project “Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT” from the European Union’s Horizon 2020 Programme with the Grant Agreement No. CNECT-ICT-643943. It was also supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2015-0-00849, Federated Interoperable Semantic IoT/Cloud Testbeds and Application). The authors would also like to thank the FIESTA-IoT consortium for fruitful discussions.

Most of the buildings that exist today were built based on 2D drawings. Building information models that represent design-stage product information have become prevalent in the second decade of the 21st century. Still, it will take many decades before such models become the norm for all existing buildings. In the meantime, the building industry lacks the tools to leverage the benefits of digital information management for construction, operation, and renovation. To this end, this paper reviews the state-of-the-art practice and research for constructing (generating) and maintaining (updating) geometric digital twins. This paper also highlights the key limitations preventing current research from being adopted in practice and derives a new geometry-based object class hierarchy that mainly focuses on the geometric properties of building objects, in contrast to widely used existing object categorisations that are mainly function-oriented. We argue that this new class hierarchy can serve as the main building block for prioritising the automation of the most frequently used object classes for geometric digital twin construction and maintenance. We also draw novel insights into the limitations of current methods and uncover further research directions to tackle these problems. Specifically, we believe that adapting deep learning methods can increase the robustness of object detection and segmentation of various types; involving design intents can achieve a high resolution of model construction and maintenance; using images as a complementary input can help to detect transparent and specular objects; and combining synthetic data for algorithm training can overcome the lack of real labelled datasets.

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.