Securing Spatial Data Infrastructures in the Context of Smart Cities

Chaturvedi, Kanishk; Matheus, Andreas; Nguyen, Son H.; Kolbe, Thomas H.

doi:10.1109/cw.2018.00078

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…This access control layer allows the stakeholder to configure whether a set of users are allowed to retrieve the observations or not. Such additional security layers can be set up on the OGC-based web services by using an approach proposed by Chaturvedi et al [90]. In this way, multiple stakeholders can set up the instances of the InterSensor Service for their respective platforms and ensure secure access to their information with the proper access control (see Figure 8).…”

Section: Using the Intersensor Service In Smart City Projectsmentioning

confidence: 99%

Towards Establishing Cross-Platform Interoperability for Sensors in Smart Cities

Chaturvedi

Kolbe

2019

Sensors

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Typically, smart city projects involve complex distributed systems having multiple stakeholders and diverse applications. These applications involve a multitude of sensor and IoT platforms for managing different types of timeseries observations. In many scenarios, timeseries data is the result of specific simulations and is stored in databases and even simple files. To make well-informed decisions, it is essential to have a proper data integration strategy, which must allow working with heterogeneous data sources and platforms in interoperable ways. In this paper, we present a new lightweight web service called InterSensor Service allowing to simply connect to multiple IoT platforms, simulation specific data, databases, and simple files and retrieving their observations without worrying about data storage and the multitude of different APIs. The service encodes these observations “on-the-fly” according to the standardized external interfaces such as the OGC Sensor Observation Service and OGC SensorThings API. In this way, the heterogeneous observations can be analyzed and visualized in a unified way. The service can be deployed not only by the users to connect to different sources but also by providers and stakeholders to simply add further interfaces to their platforms realizing interoperability according to international standards. We have developed a Java-based implementation of the InterSensor Service, which is being offered free as open source software. The service is already being used in smart city projects and one application for the district Queen Elizabeth Olympic Park in London is shown in this paper.

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Section: Using the Intersensor Service In Smart City Projectsmentioning

confidence: 99%

Towards Establishing Cross-Platform Interoperability for Sensors in Smart Cities

Chaturvedi

Kolbe

2019

Sensors

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“…3DCityDB Web-Map-Client (Yao et al, 2018) based on CesiumJS and CityGML via WFS are used to build a Smart District Data Infrastructure (SDDI) in the district Queen Elizabeth Olympic Park in London. (Chaturvedi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Extraction and Visualization of 3d Building Models in Urban Areas for Flood Simulation

Kilsedar

Fissore

Pirotti

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Floods pose a risk that is likely to worsen in the future due to climate change. Therefore, it is essential that decision makers and domain experts have the tools to evaluate the effects of floods. We developed a tool that visualizes the earth and buildings in three dimensions to simulate floods so that effective strategies can be developed to enhance resilience and mitigate the effects of floods. We opted to use open standards and free and open source software (FOSS) for Web to maximize interoperability, replicability, reusability, and accessibility. As a result of the literature review, we decided to use CityGML and CesiumJS for three-dimensional geospatial data visualization. However, as CityGML data is not available for the cities that our project focuses on, we developed software called shp2city that converts Esri shapefile to CityGML data in LOD1 or LOD2. Moreover, as CityGML data cannot be immediately used with CesiumJS, we used 3DCityDB to store, represent, and manage the CityGML data; 3DCityDB Importer/Exporter to export the CityGML data in KML/COLLADA/glTF format to be used within the 3DCityDB Web-Map-Client that is based on CesiumJS for visualization. Finally, we simulated floods to aid in the informed decision-making process regarding adaptation measures and mitigation of flooding effects.</p>

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