InterSensor Service · InterSensor Service operates on arbitrary data sources External files (CSV,...
Transcript of InterSensor Service · InterSensor Service operates on arbitrary data sources External files (CSV,...
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Technische Universität MünchenLehrstuhl für Geoinformatik
InterSensor ServiceManaging Heterogeneous Sensor Observations
in Smart Cities
Kanishk Chaturvedi, Thomas H. Kolbe
Chair of GeoinformaticsTechnische Universität München
Geospatial Sensor Webs ConferenceMünster, GermanySeptember 4, 2018
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Technische Universität MünchenLehrstuhl für Geoinformatik
Smart Cities and Interoperability
► Most smart city projects involve distributed systems
● With different stakeholders (e.g. owners, operators, solution
providers, citizens, visitors), agents and communities
● Having different interest, goals, and tasks
● And different roles and rights
► In distributed environment, it is unlikely that stakeholders
● would be willing to use a common platform
● Would be willing to share proprietary data to others
► Key requirement
● Standardized services/interfaces
● Standardized information models and data formats
► Interoperability plays a key role!!
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Technische Universität MünchenLehrstuhl für Geoinformatik
Smart City Initiatives focusing on Interoperability
► Future City Pilot Phase 1
www.opengeospatial.org/projects/initiatives/fcp1
► ESPRESSO
www.opengeospatial.org/projects/initiatives/espresso
► Smart City Interoperability Reference Architecture (SCIRA)
www.opengeospatial.org/projects/initiatives/scira
► City Enabler for Digital Urban Services (CEDUS)
www.cedus.eu/
► Smart District Data Infrastructure (SDDI)
www.gis.bgu.tum.de/en/projects/smart-district-data-infrastructure/
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Technische Universität MünchenLehrstuhl für Geoinformatik
Energy demandestimation
Noise dispersion simulation Flood simulation
Urban Analytics Toolkit
. . .Pedestrian flow simulation
Actors
Citizens
Municipality
. . .
Real estate firms
Applications
Crowd management
Energetic buildingrefurbishment
. . .
Air quality monitoring
Citizen engagement
Virtual 3D City model
Virtual District Model
Networks
IoT and Sensor data
. . .Satellite sensors
Solar potential analysis
City Dashboard
Utility service providers
a
Transportation service providers
Weather sensors
Resource Registry
Building Information Model
Smart District Data Infrastructure (SDDI)
InterSensor Service 4
Open,
standardized
service
interfaces
Open,
standardized
service
interfaces
Open,
standardized
service
interfaces
Open,
standardized
service
interfaces
04.09.2018
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Technische Universität MünchenLehrstuhl für Geoinformatik
SDDI - Queen Elizabeth Olympic Park
InterSensor Service 5
► A sporting complex built for the 2012 Summer Olympics
► Four key themes:
● Resource Efficient Buildings
● Energy Systems
● Smart Park / Future Living
● Data Architecture and Management
► Partners
● Technical University of Munich
● Imperial College London
● virtualcitySYSTEMS GmbH
● London Legacy Development Corporation
200haPlanned sporting event legacy
• District energy systems: enhanced
efficiencies & resilience
• Citywide interest: innovation profile
boosted
• Smart Park functionality: user facing
solutions for carbon/cost reductions &
improved quality of life
90haPublic building as anchor stakeholder
• Trust: closer relationships
• KPI’s: for impact measurement &
comparison
• EV/P integration: optimisation in real
application
• Sustainable water management:
integrated green spots, roofs & urban
realm
• Energy innovation: fibre optics to
push inter-seasonal storage capability
100haIndustrial docks regeneration
• District interactive map: for
communication with citizens & to
monitor sustainability outcomes
• Behaviour change: users become
aware of climate issues
• Utility masterplan: emerging
understanding of need for longer term
planning
130haRetrofit only, limited investment
• Citizen engagement: Smart Citizen
Network Board & Smart Data Atlas
• Green infrastructure: integration
across buildings & public realm
• Administration capacity building:
demonstrating the value of sustainable
leadership
• New partnership model:
public/private partnership for mobility
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Technische Universität MünchenLehrstuhl für Geoinformatik
Sensors/IoT are an integral part of SDDI - QEOP
► Different data sources
● Are used for different purposes/applications
● have platforms and APIs
● have data formats and interfaces
► How to work with all of data sources in an interoperable way?
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Platform 1
OpenSensors
Platform 2
Wunderground
Platform 3
Engie C3ntinel
Platform 4
ThingSpeak
Weather Stations
Smart Meters
Indoor Sensor
Bat Sensors
Platform 5
SensorThings
Platform 6
Twitter API
Platform 7
CSV File
Environment Sensor
Tweets Events
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Technische Universität MünchenLehrstuhl für Geoinformatik
Solution - OGC Sensor Web Enablement
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Observations
OGC SensorML<sensorDescription>
<!--Identifier--><!--Geographic position--><!--List of properties--><!--Sensor Owner--> <!--Other metadata -->
</sensorDescription>
Sensor Description
OGC Observations & Measurements<OM_Observation><!--Observation property--><!--Observation time--><!--Observation value-->
</OM_Observation>
Weather Stations
Smart Meters
Indoor Sensor
Bat Sensors
Events
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Technische Universität MünchenLehrstuhl für Geoinformatik
OGC Sensor Web Enablement Interfaces
► OGC Sensor Observation Services (SOS)
● Open standard, part of OGC Sensor Web Enablement (SWE)
● Allows querying real-time sensor data and sensor data timeseries.
● Observation responses are encoded according to the O&M standard
► OGC SensorThings API
● Very lightweight standard to interconnect the IoT devices, data and
applications over the web
● Built on the OGC SWE and O&M standards
● Provides REST services and compact data encodings in JSON format
► 52°North Timeseries API
● REST-ful web binding to the OGC Sensor Observation Service.
● Allows querying and visualizing sensor locations and their observations on
the so-called Helgoland client
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Technische Universität MünchenLehrstuhl für Geoinformatik
OGC SWE is the way forward!
InterSensor Service 9
Platform 1
OpenSensors
Platform 2
Wunderground
Platform 3
Engie C3ntinel
Platform 4
ThingSpeak
Weather Stations
Smart Meters
Indoor Sensor
Bat Sensors
Platform 5
SensorThings
Platform 6
Twitter API
Platform 7
CSV File
Environment Sensor
Tweets Events
Application 1 Application 2 Sensor Data Visualization
OGC SOS OGC SensorThings API 52°North Timeseries API
How to encode the data according
to the standardized interfaces?
04.09.2018
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Technische Universität MünchenLehrstuhl für Geoinformatik
Setting up OGC SWE Interfaces
► The implementations always require a data
storage for retrieval of sensor observations
► The timeseries data always needs to be
imported to the database (e.g. SOS
database)
► Challenges
● Not all of the stakeholders would be willing to
inject their data into another data storage for
sensor web
● Requires regular maintenance for the sensor
web data storage
● Involved complexity in moving the
infrastructure from one server to another, or
to cloud
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Importing observations in
regular intervals
Platform 3
Engie C3ntinel
Smart Meters
Application 1
OGC SOS
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Technische Universität MünchenLehrstuhl für Geoinformatik
OGC SWE Interfaces in Distributed Scenarios
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Platform 3
Engie C3ntinel
Smart Meters
Application 1
OGC SOS
Platform 3
Engie C3ntinel
Smart Meters
Intermediate service
Application 1
OGC SOS
Establishes connection
Retrieves sensor data
Encodes according to the OGC standards
Sensor storage leads to complexities
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Technische Universität MünchenLehrstuhl für Geoinformatik
InterSensor Service
► Very basic and lightweight service
► InterSensor Service operates on arbitrary data sources
● External files (CSV, Excel sheets)
● Cloud based services (Google Spreadsheet, Google Fusion Tables)
● External databases (JDBC connections)
● External IoT Platforms (Thingspeak, OpenSensors etc..)
► Encodes data “on-the-fly” according to standardized interfaces
● OGC Sensor Observation Service (Core profile)
● OGC SensorThings API
● 52°North Timeseries API
► Based on the Spring Framework
► Free and Open Source
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www.github.com/tum-gis/InterSensorService
www.intersensorservice.org (Coming soon!!)
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Technische Universität MünchenLehrstuhl für Geoinformatik
04.09.2018 InterSensor Service 13
Application A Application B Application C
InterSensor Service
External files Sensor Platforms and APIs External Databases Dynamizers
Data Adapters
Standardized External Interfaces
OGCSensor Observation Service
(SOAP-Style)
OGCSensor Things API
(REST)
52° North Timeseries API
(REST)
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Technische Universität MünchenLehrstuhl für Geoinformatik
Data Model
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Standardized External Interfaces
Data Adapters
DataSource
- id: int
- dataSourceConnection: DataSourceConnection
- coordinates: geometry [0..1]
- timeseriesList: arrayList<Timeseries> [1..*]
Timeseries
- id: int
- name: String
- desctiption: String
- dataSourceType: String
- observationProperty: String
- firstObservation: timestamp
- lastObservation: timestamp
- observationType: String
- unitOfMeasure: String
Observ ation
- time: timestamp
- intValue: int [0..1]
- doubleValue: double [0..1]
- stringValue: String [0..1]
- geomValue: geometry [0..1]
- uriValue: String [0..1]
- booleanValue: boolean [0..1]
1..*
1
1..*1
InterSensor Service
Data source Details
TimeseriesMetadata
Observations
Static information about the connection details
Dynamic data retrieved from the data source
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Technische Universität MünchenLehrstuhl für Geoinformatik
Applying the InterSensor Service in QEOP
InterSensor Service 15
Platform 1
OpenSensors
Platform 2
Wunderground
Platform 3
Engie C3ntinel
Platform 4
ThingSpeak
Weather Stations
Smart Meters
Indoor Sensor
Application 1 Application 2 Sensor Data Visualization
Bat Sensors
Platform 5
SensorThings
Platform 6
Twitter API
Platform 7
CSV File
Environment Sensor
Tweets Events
ISS ISS ISS ISS ISS ISS ISS
OGC SOS OGC SensorThings API 52°North Timeseries API
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Technische Universität MünchenLehrstuhl für Geoinformatik
Getting started with the InterSensor Service
04.09.2018 InterSensor Service 16
www.github.com/tum-gis/InterSensorService
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Technische Universität MünchenLehrstuhl für Geoinformatik
Demo - Multiple Sensor locations on Helgoland client
04.09.2018 InterSensor Service 17
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Technische Universität MünchenLehrstuhl für Geoinformatik
Demo - Multiple Sensor observations on Helgoland client
04.09.2018 InterSensor Service 18
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Technische Universität MünchenLehrstuhl für Geoinformatik
Demo – Visualizing Geo-tagged tweets with CityGML objects
04.09.2018 InterSensor Service 19
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Technische Universität MünchenLehrstuhl für Geoinformatik
Future Work
► Supporting more data sources
● Timeseries and Non-relational Databases
● Cloud based systems (Google Fusion Tables)
● Moving objects (such as GPS Exchange Formats, KML, CZML)
► Complete support for standardized interfaces
● Development of the Service Provider Interface (SPI) for the 52°North
Timeseries API
● Complete querying capabilities of the SensorThings API
► Support of Docker containers
● To quickly set up instances of the service and move them to the Cloud
environment
► Supporting write access?
► InterSensor Service as an additional service
● To access dynamic attributes with the CityGML Dynamizers and 3DCityDB
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