The concept and implementation of Smart Cities is an important approach to improve decision making as well as quality of life of the growing urban population. An essential part of this is the presentation of data from different sources within a digital city model. Wind flow at building scale has a strong impact on many health and energy issues in a city. For the analysis of urban wind, Computational Fluid Dynamics (CFD) has become an established tool, but requires specialist knowledge to prepare the geometric input during a time-consuming process. Results are available only as predefined selections of pictures or videos. In this article, a continuous, semi-automated workflow is presented, which ❶ speeds-up the preparation of CFD simulation models using a largely automated geometry optimization; and ❷ enables web-based interactive exploration of urban wind simulations to a large and diverse audience, including experts and layman. Results are evaluated based on a case study using a part of a district in Stuttgart in terms of: ➀ time saving of the CFD model preparation workflow (85% faster than the manual method), ➁ response time measurements of different data formats within the Smart City platform (3D Tiles loaded 30% faster than geoJSON using the same data representations) and protocols (3DPS provided much higher flexibility than static and 3D container API), as well as ➃ subjective user experience analysis of various visualization schemes of urban wind. Time saving for the model optimization may, however, vary depending on the data quality and the extent of the study area.

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<p><strong>Abstract.</strong> According to the advances in Information &amp; Communication Technology (ICT), nowadays, the use of Internet of Things (IoT) has become a normal part of daily life. It allows interconnections among a wide variety of devices and sensors such as smartphones, smartwatches, automobiles, or any object with a built-in sensor. However, these devices and sensors are developed by numerous different manufacturers which leads to technology lock-in in terms of data formats and protocols. In order of address this heterogeneity, an interoperable sensor protocol is the need of the hour. To address this, we propose a sensor data management system for monitoring <i>pedelec</i> usage and user fitness level. Using a proof-of-concept prototype the study is carried out in downtown of Stuttgart city. The result of the integrated analyzed data is visualized in 3D digital globe CESIUM.</p>

Abstract. The latest coronavirus (namely severe acute respiratory syndrome coronavirus 2 or COVID-19) was first detected in Wuhan, China, and spread throughout the world since December 2019. To tackle this pandemic, we need a tool to trace and predict trends of COVID-19 at global, national, and regional levels rapidly. Several organizations around the world offer access to COVID-19 related data. However, these data sources are heterogeneous in terms of data formats and protocols as different organizations developed them. To address this issue, a standard way to handle these datasets is needed. In this paper, we propose using the OGC SensorThings API to manage the COVID-19 dataset in a standard form and provide access to the general public. As a proof-of-concept, we implemented a COVID-19 data management platform based on the OGC SensorThings standard named COVID-19 SensorThings or in short COVID-STA. For a use case, we developed a real-time interactive web-based dashboard illustrating the COVID-19 dataset based on the COVID-STA. As a result, we proved that the OGC SensorThings API is suitable to use as a general standard for integrating the heterogeneous COVID-19 data.

Devices from the Internet of Things are being increasingly used in everyday life, and they provide a massive amount of data in various formats. While implementing the Smart Cities initiative, these data are integrated and utilized together with the 3D city model for further applications. Based on Open Geospatial Consortium standards, heterogeneous sensor data can be integrated with the Open Geospatial Consortium SensorThings Application Programming Interface. Similarly, the 3D city model data can be stored and exchanged with the Open Geospatial Consortium CityGML format. However, currently, there is no concrete model to integrate these sensor data with the 3D city model using the Open Geospatial Consortium standards. The existing solution for integrating the sensor data into the 3D city model requires an extension or plug-in for adding the data to the CityGML model. In this paper, we introduce the concept of “CityThings” to integrate dynamic sensor data from the Open Geospatial Consortium SensorThings API into the CityGML 3D city models. We demonstrate the implementation of the CityThings concept in the Smart Villages project in the study area of Wüstenrot, Germany, by integrating dynamic sensor data from several systems including solar panels, agro-thermal plants, and weather monitoring sensors to visualize the sensor data with the 3D city model on the web platform. In the future, this concept can be applied to interconnect dynamic sensor data and 3D city model data in other Smart Cities applications.