Publication Date: 2020-07-29
Approval Date: 2020-03-10
Submission Date: 2020-03-02
Reference number of this document: OGC 19-073r1
Reference URL for this document: http://www.opengis.net/doc/PER/3D-IoT-Platform
Category: OGC Public Engineering Report
Editor: Volker Coors
Title: OGC 3D-IoT Platform for Smart Cities Engineering Report
COPYRIGHT
Copyright © 2020 Open Geospatial Consortium. To obtain additional rights of use, visit http://www.opengeospatial.org/
WARNING
This document is not an OGC Standard. This document is an OGC Public Engineering Report created as a deliverable in an OGC Interoperability Initiative and is not an official position of the OGC membership. It is distributed for review and comment. It is subject to change without notice and may not be referred to as an OGC Standard. Further, any OGC Public Engineering Report should not be referenced as required or mandatory technology in procurements. However, the discussions in this document could very well lead to the definition of an OGC Standard.
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- 1. Subject
- 2. References
- 3. Terms and definitions
- 4. Overview
- 5. Concept of 3D IoT Smart City Platform
- 6. Architecture of the 3D IoT Smart City Platform
- 7. Implementation of the 3D IoT Smart City Platform
- 8. Summary
- Appendix A: Open API Specification of Property Estimator WPS for synthetic air quality Sensor Measurements
- Appendix B: Revision History
- Appendix C: Bibliography
1. Subject
Recent years have seen a significant increase in the use of three-dimensional (3D) data in the Internet of Things (IoT). The goal of the 3D IoT Platform for Smart Cities Pilot was to advance the use of open standards for integrating environmental, building, and IoT data in Smart Cities. Under this initiative a proof of concept (PoC) has been conducted to better understand the capabilities to be supported by a 3D IoT Smart City Platform under the following standards: CityGML, IndoorGML, SensorThings API, 3D Portrayal Service, and 3D Tiles.
1.1. Executive Summary
An OGC Pilot was carried out to bring together city models (indoor or outdoor) and sensor observations so that observable properties could be defined and visualized for both individual and aggregate components of buildings and larger city units. Dynamic sensor observations were provided for measuring Particulate Matter (PM) - the mixture of solid particles and liquid droplets in the air. The observations measured both PM2.5 air quality (fine particulate matter of a mass per cubic meter of air of particles with a size generally less than 2.5 micrometres) and building room occupancy. The PM2.5 air quality was measured and synthesized for outdoor point locations and building room occupancy was synthesized for single room locations. Observations were made available through services that implemented the OGC SensorThings API standard. Features represented using the OGC IndoorGML building model and 3D-Tiles / glTF city model features were provisioned through implementations of the OGC API – Features – Part 1:Core standard. IndoorGML is a profile of the OGC Geography Markup Language (GML) standard. Various clients deployed in the pilot fetched both features and observations keyed to them by a GML identifier (GML ID), written as ‘gmlid’ in markup, so as to allow users to interact with city feature rendered according to their dynamically observed properties. Clients included stand-alone dashboard applications, applications based on 3DPS (3D Portrayal Service), and AR (Augmented Reality) visualization tools.
Web Processing Service (WPS) and OGC API - Processes implementations were deployed to aggregate and/or interpolate observations from multiple locations in order to estimate the observable properties of larger city features such as building floor interiors and the air masses above city streets or blocks. These derived observations could either be visualized directly in client applications or posted to implementations of the SensorThings API as new data.
The pilot demonstrated a viable standards-based distributed architecture for connecting dynamic sensor observations with modeled city features. It also demonstrated the importance of aligning feature / sensor identifiers and other infrastructure data in order to sustain robust Smart City 3D-IoT capabilities.
Smart cities are communities where information technology and data are used to address social, economic, and environmental challenges. Smart Cities solutions are both popular for improving city livability, and necessary for responding to trends such as climate change and increasing urbanization.
The pilot recommends the following future work:
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There is a need to look into optimization of real-time access to geospatial data for indoor maps and navigation. Development of an indoor viewer plugin that can stream data from a server.
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Vuforia SDK is only specific to Microsoft Windows and Apple MacOS. Exploring ARCore and ARkit to handle markerless Augmented Reality (similar to the Pokemon Go game) would be worth investigating as they run on Linux.
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Future development should include a mobile application that retrieves IndoorGML from an OGC API - Features server to display indoor maps in Augmented Reality.
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There is a need to consider improvements for serving representations of buildings, based on zoom level, via an implementation of the draft OGC API – Tiles specification.
1.2. Document contributor contact points
All questions regarding this document should be directed to the editor or the contributors:
Contacts
Name | Organization | Role |
---|---|---|
Charles Chen |
Skymantics |
Contributor |
ChenYu Hao |
GISFCU |
Contributor |
Hyemi Jeong |
Gaia3D |
Contributor |
Josh Lieberman |
OGC |
Contributor |
Ki-Joune Li |
Pusan National University |
Contributor |
Logan Stark |
Skymantics |
Contributor |
Ravi Nishesh |
Cyient |
Contributor |
Steve Liang |
SensorUp |
Contributor |
Theo Braun |
Helyx |
Contributor |
Thunyathep Santhanavanich |
HFT Stuttgart, Steinbeis |
Contributor |
Volker Coors |
HFT Stuttgart, Steinbeis |
Editor |
1.3. Foreword
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. The Open Geospatial Consortium shall not be held responsible for identifying any or all such patent rights.
Recipients of this document are requested to submit, with their comments, notification of any relevant patent claims or other intellectual property rights of which they may be aware that might be infringed by any implementation of the standard set forth in this document, and to provide supporting documentation.
2. References
The following normative documents are referenced in this document.
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OGC: OGC 12-019, OGC® City Geography Markup Language (CityGML) Encoding Standard, 2012
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OGC: OGC 15-078r6, OGC® SensorThings API Part 1: Sensing, 2016
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OGC: OGC 17-079r1, OGC® SensorThings API Part 2: Tasking Core, 2019
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OGC: OGC 09-025r2, OGC® Web Feature Service 2.0 Interface Standard, 2014
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OGC: OGC 14-065, OGC® WPS 2.0 Interface Standard, 2018
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OGC: OGC 08-126, The OpenGIS® Abstract Specification Topic 5: Features, 2009
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OASIS: MQTT Version 3.1.1 Plus Errata 01, 2015
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3. Terms and definitions
For the purposes of this report, the definitions specified in Clause 4 of the OWS Common Implementation Standard OGC 06-121r9 shall apply. In addition, the following terms and definitions apply.
- ● feature
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A feature is an abstraction of real world phenomena (source: OGC 08-126)
- ● WFS model server
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A server component that delivers a representation of a feature such as a building. The interface of the model server is OGC API - Features in this Pilot.
- ● particulate matter
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The mixture of solid particles and liquid droplets in the air (source: UK DEFRA)
- ● Sensor
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An entity capable of observing a phenomenon and returning an observed value. Type of observation procedure that provides the estimated value of an observed property at its output. [OGC 12-000]
Further terms and definitions used in the Pilot are specified in detail the Conceptual Model in section 5 .
3.1. Abbreviated terms
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FoI Feature of Interest
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GL Graphics Library
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glTF GL Transmission Format
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GML Geography Markup Language
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I3S Indexed 3D scenes
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IDW Inverse distance weighting
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IoT Internet of Things
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JSON JavaScript Object Notation
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LOD Level of Detail
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STAPI SensorThings API
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UML Unified Modeling Language
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WFS Web Feature Service
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WPS Web Processing Service
4. Overview
4.1. Background
Smart cities are communities where information technology and data are used to address social, economic, and environmental challenges. Smart City solutions are both popular for improving city livability, and necessary for responding to trends such as climate change and increasing urbanization.
Sejong City, founded in 2007, is the new administrative city of South Korea. The Sejong 5-1 District is the site of a wide-ranging Smart City Initiative, led by the Korea Land and Housing (LH) Corporation. Projects under this initiative include the following:
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AR/VR Service (Smart City Experience Zone)
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Smart Street
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Smart Park
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Smart Facilities
A video of the Sejong City Smart City Initiative is available on YouTube: https://www.youtube.com/watch?v=beSEhFawY_I&feature=youtu.be.