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BIM Models and IoT for Sustainable and Smart Cities Sensing...
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BIM Models and IoT for Sustainable and Smart Cities Sensing Approaches

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 11316

Special Issue Editors

Prof. Dr. Joao Ferreira

E-Mail Website
Guest Editor
INOV INESC Inovação, Instituto de Novas Tecnologias, 1000-029 Lisboa, Portugal
Interests: IoT; Big Data; Smart cities; ITS; Vehicle electric
Special Issues, Collections and Topics in MDPI journals
Prof. Ricardo Resende

E-Mail Website
Guest Editor
Department of Architecture and Urbanism, University , School of Technologies and ArchitectureUniversity Institute of Lisbon, Lisbon, Portugal
Interests: Building Information Modelling; VR and AR application to Construction Industry

Special Issue Information

Dear Colleagues,

The Guests Editors of this Special Issue of Sensors are inviting submissions on the subject area of “BIM Models and IoT for Sustainable and Smart Cities Sensing Approaches.” BIM models associating native building information and data gathered from multiple sources open new challenges in the IoT and sensor information representation, raising a new interdisciplinary research area. BIM models are evolving to receive dynamic information from sensors and users and integrate data analytics. The storage of building sensing information in BIM models can be used for real-time facilities management, future improvements in building sustainability, disaster reaction, or to connect and feed large-scale city models. The aim of this Special Issue is to improve our understanding of how BIM and sensor data applications can contribute to new opportunities in the IoT.

Topics of interest for publication may include, but are not limited to:

  • IoT building applications for a sustainable approach;
  • IoT sensor data visualization in 3D models;
  • IoT smart systems; ·Indoor Location Systems;
  • Cloud approaches for BIM models and security;
  • New BIM models features;
  • Real-time IoT data analysis on the cloud, including localization, personalization, and contextualization of IoT data using BIM models information;
  • Application of AI strategies to physical context aware analytics.

Prof. Joao Ferreira
Prof. Ricardo Resende
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

18 pages, 8270 KiB  
Article
BIM in People2People and Things2People Interactive Process
by Bruno Mataloto, João C. Ferreira, Ricardo Resende, Rita Moura and Sílvia Luís
Sensors 2020, 20(10), 2982; https://doi.org/10.3390/s20102982 - 24 May 2020
Cited by 11 | Viewed by 3984
Abstract
In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and [...] Read more.
In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals. Full article
(This article belongs to the Special Issue BIM Models and IoT for Sustainable and Smart Cities Sensing Approaches)
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21 pages, 7565 KiB  
Article
Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors
by Yaxin Li, Wenbin Li, Shengjun Tang, Walid Darwish, Yuling Hu and Wu Chen
Sensors 2020, 20(1), 293; https://doi.org/10.3390/s20010293 - 4 Jan 2020
Cited by 40 | Viewed by 5894
Abstract
To generate indoor as-built building information models (AB BIMs) automatically and economically is a great technological challenge. Many approaches have been developed to address this problem in recent years, but it is far from being settled, particularly for the point cloud segmentation and [...] Read more.
To generate indoor as-built building information models (AB BIMs) automatically and economically is a great technological challenge. Many approaches have been developed to address this problem in recent years, but it is far from being settled, particularly for the point cloud segmentation and the extraction of the relationship among different elements due to the complicated indoor environment. This is even more difficult for the low-quality point cloud generated by low-cost scanning equipment. This paper proposes an automatic as-built BIMs generation framework that transforms the noisy 3D point cloud produced by a low-cost RGB-D sensor (about 708 USD for data collection equipment, 379 USD for the Structure sensor and 329 USD for iPad) to the as-built BIMs, without any manual intervention. The experiment results show that the proposed method has competitive robustness and accuracy, compared to the high-quality Terrestrial Lidar System (TLS), with the element extraction accuracy of 100%, mean dimension reconstruction accuracy of 98.6% and mean area reconstruction accuracy of 93.6%. Also, the proposed framework makes the BIM generation workflows more efficient in both data collection and data processing. In the experiments, the time consumption of data collection for a typical room, with an area of 45–67 m 2 , is reduced to 4–6 min with an RGB-D sensor from 50–60 min with TLS. The processing time to generate BIM models is about half minutes automatically, from around 10 min with a conventional semi-manual method. Full article
(This article belongs to the Special Issue BIM Models and IoT for Sustainable and Smart Cities Sensing Approaches)
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