1. 4th International Workshop on Semantic Sensor Networks
ISWC 2011
Semantic Sensor Data Search in a
Federated Sensor Network
Jean-Paul Calbimonte1, Hoyoung Jeung2,
Oscar Corcho1 and Karl Aberer2
1Ontology Engineering Group.
Facultad de Informática, Universidad Politécnica de Madrid.
2Distributed Information Systems Laboratory
School of Computer Science & Communication Systems, EPFL.
jp.calbimonte@upm.es
Date: 23/10/2011

2. Outline
• Introduction
• Sensor data
• Sensor metadata
• Ontological representation
• Mapping sensor streams
• Sensor data search
• Query translation
• GSN URL API
• Conclusions
2

3. Swiss-Experiment
• FP7 Network of Excellence
Environmental and GeoScience research
Swiss Alps
Geo
Researcher
... Snow,
Real-time Wind,
... data
... Radiation.
Lots of stuff
I want data to
create my
•How much snow is lost to evaporation?
models and
•Snow redistribution by wind
compare
• Wind erosion of sand
• ...
3

4. Swiss-Experiment Infrastructure
• 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
• Federated metadata management:
• Sensor metadata
Jeung H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes, N., Papaioannus, T.,
Lehning, M.Effective Metadata Management in federated Sensor Networks. in SUTC,
2010
4

5. Sensor Metadata
station
location
sensors
model
properties
5

6. Sensor Metadata
• What properties are measured
• Which sensors available
• Where are they located
• How are they configured
• Who is responsible
6

7. Sensor Data: Observations
Heterogeneity
Integration
7

8. W3C SSN-XG Ontology
• SSN Ontology
ssn:isProducedBy
ssn:SensorOutput
ssn:Sensor
ssn:observedBy
ssn:observationResult ssn:hasValue
ssn:Observation ssn:ObservationValue
ssn:observes
ssn:featureOfInterest
quantityValue
ssn:observedProperty
ssn:FeatureOfInterest
xsd:datatype
ssn:Property ssn:hasProperty
8

9. W3C SSN-XG Ontology
ssn:OperatingRange
ssn:hasOperatingRange
ssn:Deployment
ssn:Sensing ssn:hasDeployment
ssn:System
ssn:implements ssn:deployedOnPlatform
ssn:Sensor ssn:onPlatform
ssn:Device ssn:Platform
ssn:hasMeasurementCapability
ssn:MeasurementCapability
ssn:SensingDevice
9

10. 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"].
10

11. 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
11

12. Where is the Data?
GSN server instance
.. wan7
sensor1
sensor2 timed: datetime PK
GSN sensor3 sp_wind: float
…
Mappings
ssn:Observation
12

13. 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
13

14. 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"
14

15. Searching for Sensors
• Transformed wiki metadata to SSN instances in RDF
• Generated R2RML mappings for all sensors
• Numbers: 28 Deployments
• Aprox. 50 sensors in each deployment. More than 1500
sensors
SELECT DISTINCT?lat ?long ?platformName ?deploymentName
WHERE {
?sensor ssn:observes [a prop:MotionProperty];
?system ssn:hasSubSystem ?sensor;
ssn:onPlatform ?platform;
ssn:hasDeployment ?deployment.
?deployment foaf:name ?deploymentName.
?platform dul:hasLocation [swissex:hasGeometry ?link];
foaf:name ?platformName.
?link omgeo:within(46.3 8.7 47.2 9.8);
geo-pos:lat ?lat;
geo-pos:long ?long. }
15

16. Sensor Data Portal Application
• Problems
• Too many sensors
• Too Heterogeneous
• Any sensors available in this region?
• Sensors that measure wind speed?
• How about getting the data?
16

17. Playing with it
17

18. Querying the Observations
• Implementation of Ontology-based querying over
GSN
• Using the R2RML mappings
• Fronting GSN with SPARQL-Stream queries
• Query & Data translation process
18

19. Query & Data Translation
SELECT ?waveheight
FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>
[NOW -10 MINUTES TO NOW]
WHERE {
?WaveObs a ssn:Observation;
sea:hasValue ?waveheight; }
Query
q translation
Target query
Sensor Networks
SPARQLStream
q’
mappings Query
Client
Processing
d’
d
Data [tuple] [tuple]
[triple] translation
Ontology-based sensor query service
19

20. 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.
20

21. 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
21

22. 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
22

23. Algebra construction
π timed,
sp_wind
windsensor1
windsensor2 σ sp_wind>10
ω 5 Hour
wan7
23

24. Static optimization
π timed, π timed, π timed,
sp_wind windvalue windvalue
σ sp_wind>10 σ windvalue>10 σ windvalue>10
ω 5 Hour ω 5 Hour ω 5 Hour
wan7 windsensor1 windsensor2
Sensors, Mappings and Queries 24

25. 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
25

26. Translation overhead
Sensors, Mappings and Queries 26

27. Conclusions
• Use of the SSN Ontology
• Sensor metadata
• Sensor observations
• Using R2RML mappings for virtual sensors
• We can translate to query languages and APIs
• Abstract expressions for representing queries
• Applications
• Improved sensor search
• Federated environment
• Schema heterogeneity
27

28. TODO
• Explore expressivity of queries
• Expose as Linked Stream Data
• Integrate with stored RDF data
• Apply to other environments
• Complex-event-processing engines
• OGC SOS Services
• Pachube
• etc
• Experimentation
• Latency
• Performance
• Tuple rates
• Query complexity
28

29. Thanks!
http://code.google.com/p/semanticstreams/
Questions, please.
jp.calbimonte@upm.es
29