linked sensor data 101 (fis2011)
DESCRIPTION
TRANSCRIPT
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Date: 09/11/2011
Linked Sensor Data
Oscar Corcho, Jean-Paul Calbimonte, Raúl García-Castro and Freddy Priyatna
Ontology Engineering Group. Facultad de Informática, Universidad Politécnica de Madrid.
4th Future Internet Symposium FIS 2011Vienna, Austria
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Linked Sensor Data 101
Linked Sensor Data
Motivation
Ingredients
Generate
Consume
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Motivation
From Sensor Networks…
… to the Sensor Web/ Internet of Things…
… to Semantic Sensor Web and …
Linked Sensor Data
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Sensors
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http://www.flickr.com/photos/wouterh/2409251427/
• Cheaper• Ubiquitous• Robust• Routing
• Noisy• Processing• Memory• Energy(Limited)
(t9, a1, a2, ... , an)(t8, a1, a2, ... , an)(t7, a1, a2, ... , an)......(t1, a1, a2, ... , an)......
Streaming Data
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Sensor Networks
Source: Antonis Deligiannakis
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An example: SmartCities
6 SmartSantander Project
Environmental sensors
Parking sensors
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Who are the end users of Sensor Networks?
Source: Dave de Roure
The climate change expert, or a simple citizen
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Not only environmental, but many others…
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Weather Sensors
Camera SensorsSatellite Sensors
GPS Sensors
Sensor Dataset
Source: H Patni, C Henson, A Sheth
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The Sensor Web
Universal, web-based access to sensor data
Source: Adapted from Alan Smeaton’s invited talk at ESWC2009
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Make sensors more accessible?
10Source: SemsorGrid4Env consortium
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Should we care as computer scientists?
“Grand Challenge” CS issues:• Heterogeneity• Scale• Scalability• Autonomic behaviour• Persistence, evolution• Deployment challenges• Mobility
Source: Dave de Roure
Anything left for Semantic Web research?
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Vision (after some iterations, and more to come)
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Networked Knowledge
Before 2010 2010-2015 2015-2020 Beyond 2020
Today Incremental Incremental-Visionary
Visionary
Interoperability
Middleware Sensor
ontologies
Intra-network cross-layer integration and optimization
Sensor Internet
Inter-network cross-layer integration and optimization
Information & Context
Relational database integration
Sensor network data warehouses
Stream aggregation Query processing
and reasoning on sensor networks
Event modelling
Database-stream integration
Sensor actuation (In-network processing)
QoS models
QoS-based information integration of DB and streams
Discovery Centralised non-semantic registries (sensorbase.org)
Semantic discovery of sensors and sensor data
Distributed registries Sensor network
location transparency
Identity & Trust & Privacy
RFID tags No privacy
mgmnt
URIs User-centric privacy
and policies
Virtual sensor networks through dynamic policies
Provenance Data provenance (where, what and who)
Data transformation processes (how)
Process and problem solving understanding (why)
Problem solving interpretation and explanation
RWI Working Group on IoT: Networked KnowledgeGluhak et al, 2011. An Architectural Blueprint for a Real-World Internet', Future Internet Assembly
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Semantic Sensor Web / Linked Sensor Data (LSD)
A representation of sensor data following the standards of Linked Data
But what is Linked Data?
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What is Linked Data?
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An extension of the current Web…
data are given well defined and explicitly represented meaning
So that it can be shared and used By humans and machines
And clear principles on how to publish data
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The four principles (Tim Berners Lee, 2006)
http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
Use URIs as names of thingsUse HTTP URIsProvide useful information when URI is dereferencedLink to other URIs
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Semantic Sensor Web / Linked Sensor Data (LSD)
• Early references…• Sheth A, Henson C, and Sahoo S, Semantic Sensor Web, IEEE
Internet Computing, 2008.• Sequeda J, Corcho O. Linked Stream Data: A Position Paper.
Proceedings of the 2nd International Workshop on Semantic Sensor Networks, 2009.
• Le-Phuoc D, Parreira JX, Hauswirth M. Challenges in Linked Stream Data Processing: A Position Paper. Proceedings of the 3rd International Workshop on Semantic Sensor Networks, 2010.
A representation of sensor data following the standards of Linked Data
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Let’s check some examples
• Meteorological data in Spain: automatic weather stations• http://aemet.linkeddata.es/
• Live sensors in Slovenia• http://sensors.ijs.si/
• Channel Coastal Observatory in Southern UK• http://webgis1.geodata.soton.ac.uk/flood.html
• And some more from DERI Galway, Knoesis, CSIRO, etc.
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AEMET Linked Data
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Observations
Sensors
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JSI Sensors
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Coastal Channel Observatory and other sources
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• Work with Flood environmental sensor data.• SemSorGrid4Env project www.semsorgrid4env.eu.
Wave Height
Tidal Observations
Wind Speed
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Ingredients for Linked Sensor Data
Core ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines
http://www.flickr.com/photos/santos/2252824606/
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Since aprox. 2005: Several proposalsProject specificReuse?Alignment?Best practices?
2009-2011: W3C SSN-XG incubator groupSSN Ontology: http://purl.oclc.org/NET/ssnx/ssn
Sensor Network Ontologies
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Skeleton
Device
Deployment
PlatformSite
System
Process
ConstraintBlockMeasuringCapability
OperatingRestriction
Data
SSN ontology modules
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Skeleton
Device
Deployment
PlatformSite
System
System
onPlatform only
hasSubsystem only, someSurvivalRang
e
hasSurvivalRange only
OperatingRangehasOperatingRange only
hasDeployment only
DeploymentRelatedProcess
Deployment
deploymentProcesPart only
deployedSystem only
Platform
deployedOnPlatform only
attachedSystem only
Device
Sensor
SensingDevice
Sensing
implements some
observes only
hasMeasurementCapability only
inDeployment only
SensorInput
detects only
isProxyFor onlyObservationValu
e
SensorOutput
hasValue some
isProducedBy some
Process
Process
hasInput only
hasOutput only, some
Input
Output
Observation
observedBy only
featureOfInterest only
observationResult only
Property
observedProperty onlyhasProperty only, some
isPropertyOf some
sensingMethodUsed only
includesEvent some
FeatureOfInterest
ConstraintBlock
Condition
inCondition only
MeasuringCapability
MeasurementCapability
forProperty only
OperatingRestriction
inCondition only
Data
Overview of the SSN ontologies
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CommunicationMeasuringCapability
MeasurementCapability
MeasurementProperty
hasMeasurementProperty only
Accuracy
DetectionLimit
Drift
Frequency
MeasurementRange
Precision
Resolution
ResponseTime
Selectivity
Sensitivity
Latency
Skeleton
EnergyRestrictionOperatingRestriction
OperatingRange
OperatingProperty
hasOperatingProperty only
EnvironmentalOperatingProperty
MaintenanceSchedule
SurvivalRange
SurvivalProperty
hasSurvivalProperty only
EnvironmentalSurvivalProperty
SystemLifetime
BatteryLifetime
OperatingPowerRange
Property
SSN Ontology: Measurement Capabilities
Core ontological model
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Example
swissex:Sensor1 rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes [rdf:type sweetSpeed:WindSpeed].
swissex:Sensor2 rdf:type ssn:Sensor; ssn:onPlatform swissex:Station1; ssn:observes [rdf:type sweetTemp:Temperature].
swissex:Station1 :hasGeometry [ rdf:type wgs84:Point;
wgs84:lat "46.8037166"; wgs84:long "9.7780305"].
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station
senso
r1
senso
r2
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Example
swissex:WindSpeedObservation1 rdf:type ssn:Observation; ssn:featureOfInterest [rdf:type sweetAtmoWind:Wind]; ssn:observedProperty [rdf:type sweetSpeed:WindSpeed]; ssn:observationResult [rdf:type ssn:SensorOutput; ssn:hasValue [qudt:numericValue "6.245"^^xsd:double]]; ssn:observationResultTime [time:inXSDDatatime "2011-10-26T21:32:52"]; ssn:observedBy swissex:Sensor1 ;
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WindSpeed : 6.245
At: 2011-10-26T21:32:52
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Usage: SSN & Domain Ontologies
SWEET
Service
Coastal Defences
Ordnance Survey
Additional Regions
Role
DOLCE UltraLite
Schema
FOAF
Upper
External
SSG4Env infrastructure
Flood domain
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SSN
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AEMET Ontology Network
• 83 classes• 102 object properties• 80 datatype properties• 19 instances
Additional domain ontologies
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Ingredients for Linked Sensor Data
Core ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines
http://www.flickr.com/photos/santos/2252824606/
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Good practices in URI Definition
Sorry, no clear practices yet…
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Good practices in URI Definition
• URIs for:• Observations• Sensors• Features of interest• Properties• Time periods
• Debate: observation or sensor-centric?• Observation-centric seems to be the winner• Sensor-centric, check [Sequeda and Corcho, 2009]
• Example:
http://aemet.linkeddata.es/resource/Observation/at_1316382600000_of_08130_on_VV10m
when sensor property
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Ingredients for Linked Sensor Data
Core ontological modelAdditional domain ontologiesGuidelines for generation of identifiersSensor Web programming interfacesQuery processing engines
http://www.flickr.com/photos/santos/2252824606/
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Sensor High-level API
Source: K. Page & Southampton’s team at SemsorGrid4Env
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Sensor High-level API
Source: K. Page & Southampton’s team at SemsorGrid4Env
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Queries to Sensor Data
C-SPARQLREGISTER QUERY WindSpeedAndDirection ASPREFIX fire:
<http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?sensor ?speed ?directionFROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC
SLIDE 1 MSEC]WHERE { … 36
SNEEqlRSTREAM SELECT id, speed, direction FROM wind [NOW];
Streaming SPARQLPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?WindSpeedFROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MSWHERE { ?sensor fire:hasMeasurements ?WindSpeed FILTER (?WindSpeed<30)}
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GSN & Swiss-Experiment
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• Global Sensor Networks, deployment for SwissEx.
• Distributed environment: GSN Davos, GSN Zurich, etc.• In each site, a number of sensors available• Each one with different schema
• Metadata stored in wiki
Sensor observations
Sensor metadata
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Where is the Data?
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GSN
GSN server instance
wan7
timed: datetime PKsp_wind: float
..sensor1sensor2sensor3…
Virtual
senso
rs
ssn:Observation
Mappings
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Creating Mappings
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wan7
timed: datetime PKsp_wind: float
ssn:ObservationValue
qudt:numericValue
xsd:decimal
http://swissex.ch/data#Wan7/WindSpeed/ObsValue{timed}
sp_wind
ssn:SensorOutput
ssn:Observation
ssn:hasValue
ssn:observationResulthttp://swissex.ch/data#
Wan7/WindSpeed/Observation{timed}
http://swissex.ch/data#Wan7/ WindSpeed/ ObsOutput{timed}
ssn:Property
ssn:observedProperty
sweetSpeed:WindSpeed
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Querying the ObservationsSELECT ?waveheightFROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [NOW -10 MINUTES TO NOW STEP 1 MINUTE]WHERE { ?WaveObs a sea:WaveHeightObservation; sea:hasValue ?waveheight; }
Query translation
Query ProcessingC
lient
Mappings
SPARQLStream
[tuples]
Sensor Network
Data translation[triples]
GSN API
:Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7"; rr:subjectMap [ rr:template "http://swissex.ch/ns#WindSpeed/Wan7/{timed}"; rr:class ssn:ObservationValue; rr:graph ssg:swissexsnow.srdf ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate ssn:hasQuantityValue ]; rr:objectMap[ rr:column "sp_wind" ] ];
R2RML Mappings
http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &field [0]= sp_wind
Query processing engines
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Conclusions
Ingredients for Linked Sensor DataCore ontology
Domain ontologiesGuidelines for identifiersAPIs
Query processing engines
Work in progress & examples
Challenges: generate & consume LSD
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Thanks!
Questions, please.
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Acknowledgments: all those identified in slides + the SemsorGrid4Env team (Alasdair Gray, Kevin Page, etc.), the AEMET team at OEG-UPM (Ghislain Atemezing, Daniel Garijo, José Mora, María Poveda, Daniel Vila, Boris Villazón) + Pablo Rozas (AEMET)
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Where is the Data?
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GSN
GSN server instance
wan7
timed: datetime PKsp_wind: float
..sensor1sensor2sensor3…
Virtual
senso
rs
ssn:Observation
Mappings
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Creating Mappings
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wan7
timed: datetime PKsp_wind: float
ssn:ObservationValue
qudt:numericValue
xsd:decimal
http://swissex.ch/data#Wan7/WindSpeed/ObsValue{timed}
sp_wind
ssn:SensorOutput
ssn:Observation
ssn:hasValue
ssn:observationResulthttp://swissex.ch/data#
Wan7/WindSpeed/Observation{timed}
http://swissex.ch/data#Wan7/ WindSpeed/ ObsOutput{timed}
ssn:Property
ssn:observedProperty
sweetSpeed:WindSpeed
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R2RML
• RDB2RDF W3C Group, R2RML Mapping language:• http://www.w3.org/2001/sw/rdb2rdf/r2rml/
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:Wan4WindSpeed a rr:TriplesMapClass; rr:tableName "wan7"; rr:subjectMap [ rr:template "http://swissex.ch/ns#WindSpeed/Wan7/{timed}"; rr:class ssn:ObservationValue; rr:graph ssg:swissexsnow.srdf ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate ssn:hasQuantityValue ]; rr:objectMap[ rr:column "sp_wind" ] ]; .
<http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 > a ssn:ObservationValue<http://swissex.ch/ns#/WindSpeed/Wan7/2011-05-20:20:00 > ssn:hasQuantityValue " 4.5"
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Data Access
• GSN Web Services• GSN URL API
• Compose the query as a URL:
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http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &field [0]= sp_wind &from =15/05/2011+05:00:00& to =15/05/2011+10:00:00&c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10
SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10 ?
Calbimonte, J-P., Corcho O., Gray, A. Enabling Ontology-based Access to Streaming Data Sources. In ISWC 2010.
SPARQL-Stream
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Using the Mappings
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SELECT ?waveheightFROM STREAM <www.ssg4env.eu/SensorReadings.srdf> [NOW – 5 HOUR TO NOW]WHERE { ?WaveObs a ssn:ObservationValue; qudt:numericalValue ?waveheight; FILTER (?waveheight>10) }
wan7
timed: datetime PKsp_wind: float
xsd:datatype
ssn:ObservationValue
qudt:numericalValue
sp_wind
http://swissex.ch/data#Wan7/WindSpeed/ObsValue{timed}
timed,sp_wind
π
ω
σsp_wind>10
5 Hour
wan7
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Algebra expressions
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timed,sp_wind
π
ω
σ sp_wind>10
5 Hour
wan7
http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan7 &field [0]= sp_wind &from =15/05/2011+05:00:00& to =15/05/2011+10:00:00&c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10
SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10