1. 4th Future Internet Symposium FIS 2011
Vienna, Austria
Linked Sensor Data 101
Oscar Corcho, Jean-Paul Calbimonte,
Raúl García-Castro and Freddy Priyatna
Ontology Engineering Group.
Facultad de Informática, Universidad Politécnica de Madrid.
jp.calbimonte@upm.es
Date: 09/11/2011

2. Linked Sensor Data 101
Motivation
Ingredients
Linked Sensor Data
Generate
Consume
2

3. Motivation
From Sensor Networks…
… to the Sensor Web/
Internet of Things…
… to Semantic Sensor Web and …
Linked Sensor Data
3

4. Sensors
(t9, a1, a2, ... , an)
(t8, a1, a2, ... , an)
Streaming (t7, a1, a2, ... , an)
• Cheaper Data
...
...
• Ubiquitous (t1, a1,
...
a2, ... , an)
• Robust ...
• Routing
• Noisy
• Processing
• Memory
• Energy
(Limited)
http://www.flickr.com/photos/wouterh/2409251427/
4

5. Sensor Networks
Source: Antonis Deligiannakis

6. An example: SmartCities
Environmental sensors
Parking sensors
6 SmartSantander Project

7. Who are the end users of Sensor Networks?
The climate change expert, or a simple citizen
Source: Dave de Roure

8. Not only environmental, but many others…
Weather Sensors
GPS Sensors
Sensor Dataset
Satellite Sensors Camera Sensors
Source: H Patni, C Henson, A Sheth 8

9. The Sensor Web
Universal, web-based access to sensor data
Source: Adapted from Alan Smeaton’s invited talk 9 ESWC2009
at

10. Make sensors more accessible?
Source: SemsorGrid4Env consortium 10

11. Should we care as computer scientists?
“Grand Challenge” CS issues:
• Heterogeneity
• Scale
• Scalability
• Autonomic behaviour
• Persistence, evolution
• Deployment challenges
• Mobility
Anything left for Semantic Web research?
Source: Dave de Roure

12. Vision (after some iterations, and more to come)
Networked Before 2010 2010-2015 2015-2020 Beyond 2020
Knowledge
Today Incremental Incremental- Visionary
Visionary
Interoperability  Middleware  Intra-network cross-  Inter-network
 Sensor ontologies layer integration and cross-layer
optimization integration and
 Sensor Internet optimization
Information &  Relational  Stream aggregation  Database-stream  QoS-based
Context database  Query processing and integration information
integration reasoning on sensor  Sensor actuation integration of
 Sensor network networks (In-network DB and streams
data warehouses  Event modelling processing)
 QoS models
Discovery  Centralised non-  Semantic discovery of
semantic registries sensors and sensor
(sensorbase.org) data
 Distributed registries
 Sensor network location
transparency
Identity & Trust  RFID tags  URIs  Virtual sensor
& Privacy  No privacy mgmnt  User-centric privacy and networks through
policies dynamic policies
Provenance  Data provenance  Data transformation  Process and  Problem solving
(where, what and processes (how) problem solving interpretation
who) understanding and explanation
RWI Working Group on IoT: Networked Knowledge (why)
12

13. Semantic Sensor Web / Linked Sensor Data (LSD)
A representation of sensor data following
the standards of Linked Data
But what is Linked Data?

14. What is Linked Data?
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
14

15. The four principles (Tim Berners Lee, 2006)
Use URIs as names of things
Use HTTP URIs
Provide useful information when URI is dereferenced
Link to other URIs
http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
15

16. Semantic Sensor Web / Linked Sensor Data (LSD)
A representation of sensor data following
the standards of Linked Data
• 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.

17. 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.
17

18. AEMET Linked Data
Sensors
Observations
18

19. JSI Sensors
19

20. Coastal Channel Observatory and other sources
• Work with Flood environmental sensor data.
• SemSorGrid4Env project www.semsorgrid4env.eu.
Wind Speed
Wave Height
Tidal Observations
20

21. Ingredients for Linked Sensor Data
Core ontological model
Additional domain ontologies
Guidelines for generation of identifiers
Sensor Web programming interfaces
Query processing engines
http://www.flickr.com/photos/santos/2252824606/

22. Sensor Network Ontologies
 Since aprox. 2005: Several proposals
 Project specific
 Reuse?
 Alignment?
 Best practices?
 2009-2011: W3C SSN-XG incubator group
 SSN Ontology: http://purl.oclc.org/NET/ssnx/ssn

23. SSN ontology modules
System OperatingRestriction
Deployment
Device Process
PlatformSite
Data
Skeleton
MeasuringCapability ConstraintBlock

24. Overview of the SSN ontologies
Deployment deploymentProcesPart only System OperatingRestriction
hasSubsystem only, some hasSurvivalRange only
SurvivalRange
DeploymentRelatedProcess
hasDeployment only
System
OperatingRange
Deployment deployedSystem only hasOperatingRange only
deployedOnPlatform only Process
inDeployment only Device hasInput only
Input
PlatformSite onPlatform only Device Process
Platform Output
attachedSystem only hasOutput only, some
Data Skeleton
isProducedBy some implements some
Sensor
Sensing
hasValue some sensingMethodUsed only
SensorOutput
detects only
SensingDevice observes only
ObservationValue SensorInput
isProxyFor only
Property
includesEvent some isPropertyOf some
observedProperty only
observationResult only
observedBy only hasProperty only, some
Observation FeatureOfInterest
featureOfInterest only
MeasuringCapability ConstraintBlock
hasMeasurementCapability only forProperty only
inCondition only inCondition only
MeasurementCapability Condition

25. SSN Ontology: Measurement Capabilities
Skeleton
Property
MeasuringCapability Communication
hasMeasurementProperty only
MeasurementCapability MeasurementProperty
Accuracy Resolution Selectivity Frequency Precision Latency
DetectionLimit Drift ResponseTime Sensitivity MeasurementRange
OperatingRestriction EnergyRestriction
Core ontological model
hasOperatingProperty only
OperatingRange OperatingProperty
EnvironmentalOperatingProperty MaintenanceSchedule OperatingPowerRange
hasSurvivalProperty only
SurvivalRange SurvivalProperty
EnvironmentalSurvivalProperty SystemLifetime BatteryLifetime

26. Example
swissex:Sensor1
rdf:type ssn:Sensor;
ssn:onPlatform swissex:Station1;
ssn:observes [rdf:type sweetSpeed:WindSpeed].
swissex:Sensor2
station
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"].
26

27. 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 ;
WindSpeed : 6.245
At: 2011-10-
26T21:32:52
27

28. Usage: SSN & Domain Ontologies
Upper
DOLCE SWEET
UltraLite
SSG4Env
infrastructure SSN
Schema
Service
External
FOAF Ordnance
Survey
Flood domain
Role Coastal Additional
Defences Regions
28

29. AEMET Ontology Network
• 83 classes
• 102 object properties
• 80 datatype properties
• 19 instances
Additional domain ontologies

30. Ingredients for Linked Sensor Data
Core ontological model
Additional domain ontologies
Guidelines for generation of identifiers
Sensor Web programming interfaces
Query processing engines
http://www.flickr.com/photos/santos/2252824606/

31. Good practices in URI Definition
Sorry, no clear
practices yet…

32. 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

33. Ingredients for Linked Sensor Data
Core ontological model
Additional domain ontologies
Guidelines for generation of identifiers
Sensor Web programming interfaces
Query processing engines
http://www.flickr.com/photos/santos/2252824606/

34. Sensor High-level API
Source: K. Page & Southampton’s team at SemsorGrid4Env

35. Sensor High-level API
Source: K. Page & Southampton’s team at SemsorGrid4Env

36. Queries to Sensor Data
SNEEql
RSTREAM SELECT id, speed, direction FROM wind [NOW];
Streaming SPARQL
PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
SELECT ?WindSpeed
FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS
WHERE {
?sensor fire:hasMeasurements ?WindSpeed
FILTER (?WindSpeed<30)
}
C-SPARQL
REGISTER QUERY WindSpeedAndDirection AS
PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
SELECT ?sensor ?speed ?direction
FROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1
MSEC]
WHERE { …
36

37. GSN & Swiss-Experiment
• 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 Sensor observations
schema
• Metadata stored in wiki
Sensor metadata
37

38. Where is the Data?
GSN server instance
.. wan7
sensor1
sensor2 timed: datetime PK
GSN sensor3 sp_wind: float
…
Mappings
ssn:Observation
38

39. Creating Mappings
ssn:observedProperty
ssn:Observation ssn:Property
http://swissex.ch/data#
ssn:observationResult Wan7/WindSpeed/Observation{timed} sweetSpeed:WindSpeed
wan7 ssn:SensorOutput
timed: datetime PK http://swissex.ch/data#
sp_wind: float ssn:hasValue Wan7/ WindSpeed/ ObsOutput{timed}
ssn:ObservationValue
http://swissex.ch/data#
qudt:numericValue Wan7/WindSpeed/ObsValue{timed}
xsd:decimal
sp_wind
39

40. Querying the Observations
SELECT ?waveheight
FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>
[NOW -10 MINUTES TO NOW STEP 1 MINUTE]
WHERE {
?WaveObs a sea:WaveHeightObservation; :22001/ multidata ?vs [0]= wan7 &
http://montblanc.slf.ch
field [0]= sp_wind
sea:hasValue ?waveheight; }
Query
:Wan4WindSpeed a rr:TriplesMapClass; translation GSN
rr:tableName "wan7";
SPARQLStream API
rr:subjectMap [ rr:template
"http://swissex.ch/ns#WindSpeed/Wan7/{timed}";
Client
rr:class ssn:ObservationValue; rr:graph
ssg:swissexsnow.srdf ]; Mappings Query
rr:predicateObjectMap [ rr:predicateMap [ Processing
rr:predicate ssn:hasQuantityValue ]; Sensor
rr:objectMap[ rr:column "sp_wind" ] ]; Network
[tuples]
Data
[triples] translation
R2RML Mappings Query processing engines
40

41. Conclusions
Ingredients for Linked Sensor Data
Core ontology
Domain ontologies
Guidelines for identifiers
APIs
Query processing engines
Work in progress & examples
Challenges: generate & consume LSD

42. Thanks!
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)
Questions, please.
jp.calbimonte@upm.es
42

43. Where is the Data?
GSN server instance
.. wan7
sensor1
sensor2 timed: datetime PK
GSN sensor3 sp_wind: float
…
Mappings
ssn:Observation
43

44. Creating Mappings
ssn:observedProperty
ssn:Observation ssn:Property
http://swissex.ch/data#
ssn:observationResult Wan7/WindSpeed/Observation{timed} sweetSpeed:WindSpeed
wan7 ssn:SensorOutput
timed: datetime PK http://swissex.ch/data#
sp_wind: float ssn:hasValue Wan7/ WindSpeed/ ObsOutput{timed}
ssn:ObservationValue
http://swissex.ch/data#
qudt:numericValue Wan7/WindSpeed/ObsValue{timed}
xsd:decimal
sp_wind
44

45. R2RML
• RDB2RDF W3C Group, R2RML Mapping language:
• http://www.w3.org/2001/sw/rdb2rdf/r2rml/
: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"
45

46. Data Access
• GSN Web Services
• GSN URL API
• Compose the query as a URL:
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
?
SPARQL-Stream
Calbimonte, J-P., Corcho O., Gray, A. Enabling Ontology-based Access to Streaming Data Sources. In ISWC 2010.
46

47. Using the Mappings
π timed,
sp_wind
SELECT ?waveheight
σ
FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>
[NOW – 5 HOUR TO NOW]
sp_wind>10
WHERE {
?WaveObs a ssn:ObservationValue;
qudt:numericalValue ?waveheight; ω 5 Hour
FILTER (?waveheight>10) }
wan7
wan7 ssn:ObservationValue
http://swissex.ch/data#
timed: datetime PK qudt:numericalValue Wan7/WindSpeed/ObsValue{timed}
sp_wind: float
xsd:datatype
sp_wind
47

48. Algebra expressions
π timed, 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&
sp_wind c_vs [0]= wan7 & c_field [0]= sp_wind & c_min [0]=10
σ sp_wind>10
ω 5 Hour
SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10
wan7
48