Synthesizing the Internet of Things (IoT) with building information modeling (BIM) can improve the performance of the data collection. In this regard, BIM endeavors to enable real-time monitoring conditions of buildings. This paper is focused on the indoor positioning system (IPS) as a key enabling technology for IoT applications, which uses smart and non-smart mobile devices (object tags and beacons) with the aim of positioning and objects tracking that lead to a smart approach in the field of facility management (FM). Hence, we have surveyed the joint use of IPS and BIM in FM based on the concept of enterprise BIM (EBIM). EBIM forms the basis for the future strategic real estate management using virtual models and open standards. As a result, we gained the ability to collect positioning data continuously, save them in a BIM database, and present them on two-dimensional (2D) maps. This is a part of an ongoing study that aims to use data collection effectively for FM as an organizational function in large and complex buildings. Hence, for this purpose, we have considered St. Olavs Hospital, one of the biggest healthcare centers in Norway, as a case study. The effectiveness of data collection by IoT devices installed in buildings and how the combination of BIM and IoT technology can support a holistic view of the status of the buildings, which subsequently can enhance data usage efficiency and FM development, will be demonstrated.

Operators in industrial manufacturing environments are under pressure to cope with the ever increasing flexibility and complexity of work. Transitioning towards data-driven smart manufacturing environments in the context of Industry 4.0 with wearable

Smart city solutions make a high-level technological innovation in a city to expand their citizens' quality of life by several different technological management strategies (such as resource management and data management) among end-users, city planners, and technological devices (e.g., sensors, IoT devices, etc.). Currently, data management architectures have been offered by some researchers to organize obtained data in smart cities, including Centralized Data Management (CDM) and Distributed-to-Centralized Data Management (D2C-DM). In addition, the D2C-DM architecture can provide several advantages from the combined advantages of distributed (e.g., Fog) and centralized (e.g., Cloud) technologies, such as reducing network traffic and their latencies, upgrading the security levels and so on. In this paper, we propose several novel contributions. First, we design a D2C-DM with Fog, cloudlet, and Cloud technologies to organize huge amounts of the data production in smart city scenarios, from physical devices (including IoT and sensors devices) to non-physical devices (including third-party applications, and other databases). Second, we tailor our proposed D2C-DM architecture with the Zero Emission Neighborhoods (ZEN) scenario to manage its different data types, including context, research, and Key Performance Indicator (KPI) data. Finally, the advantages of this D2C-DM architecture are discussed.

To support smart cities in aligning information technology (IT) and business strategies to achieve urban digitalization, this study aims to present an Enterprise Architecture Framework (EAF) to facilitate the digitalization of urban environments. Technology Acceptance Model (TAM) was employed to design a research model, and a mixed-mode methodology was employed. Quantitative data from survey questionnaires was used to gather data from practitioners in enterprises within Norway and Ireland that currently adopt Enterprise Architecture (EA) in a smart city project to empirically validate the developed EAF. Additionally, the developed EAF layers were validated through a qualitative focus group workshop with participants that utilize the developed EAF to provide digital services in smart cities. The findings suggest that the developed EAF can aid enterprises that provided digital services in identifying and assessing their digitalization initiatives and how EA benefits can be realized. Drawing on the TAM, the developed research model presents factors that influence practitioner’s adoption of EA towards the digitalization of urban environments. More importantly, this study provides empirically validated research on EAF adoption which is scarce.