Semantic (Social) Sensor Networks

SSSW2012 – The Ninth Summer School on Ontological Engineering
and Semantic Web

Semantic Sensor Networks
(and Social) Semantic Web

Oscar Corcho
(with the help of Jean Paul Calbimonte)

Ontology Engineering Group
Facultad de Informática, Universidad Politécnica de Madrid

ocorcho@fi.upm.es

This is what my talk is going to be about…

Application

Middleware

CQELS REST SPARQL

Linked Streams Linked Data

COAP
Virtual
Sensors
Sensors

Was this a Déjà Vu?

• Key differences
• I am younger (as
demonstrated on Tuesday)
• We are both vice-directors
• But I am vice-director
of a most important
organisation (SSSW vs
DERI)

• Lessons learned:
• Never let an invited
speaker speak before you
• Specially if he is the one
giving a great tutorial ;-)

3

Was this a Déjà Vu?

• Hence the title of my talk could perfectly be…

4

Some work that can be inserted in the picture

Application

Middleware

SPARQL-STR
CQELS REST SPARQL


Data Stream COAP
Virtual
Management
Sensors
Systems Sensors

Ingredients for the Semantic Sensor Web

Core ontological model

Additional domain ontologies

Guidelines for generation of identifiers

Sensor Web programming interfaces (e.g.,
lsm.deri.ie)

Query processing engines

http://www.flickr.com/photos/santos/2252824606/

AEMET Linked Data (http://aemet.linkeddata.es)

7

Semantic Weather (http://www.semanticweather.com)

8

Some Slovenian city sensors (http://sensors.ijs.si/)

9

Coastal Channel Observatory and other sources
(http://webgis1.geodata.soton.ac.uk/flood.html)

Flood risk alert:
South East England
Emergency
I have to make
planner
sense out of all this
data

wave data Environmental
forecasts defenses

10

Should we care? Smart cities

Environmental sensors

Parking sensors

11 SmartSantander Project

Some work that can be inserted in the picture

Application

Middleware

SPARQL-STR CQELS REST SPARQL


Data Stream COAP
Virtual
Management
Sensors
Systems Sensors

Sensor Network Ontologies

 Approximately since 2005: Several proposals
 State of the art on sensor network ontologies in the report below
 Most of them were too project-specific
 Not too much reuse
 No alignment between them
 No ontology design best practices

 2009-2011: W3C SSN-XG incubator group
 Final report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
 SSN Ontology: http://purl.oclc.org/NET/ssnx/ssn
 A good number of internal and external references to SSN Ontology
 http://www.w3.org/2005/Incubator/ssn/wiki/Tagged_Bibliography
 SSN Ontology paper soon at the Journal of Web Semantics

 2011-__ : W3C SSN Community Group

Overview of the SSN ontology

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

SSN Ontology. Sensor and environmental properties

Skeleton

Property

MeasuringCapability Communication
hasMeasurementProperty only
MeasurementCapability MeasurementProperty

Accuracy Resolution Selectivity Frequency Precision Latency

DetectionLimit Drift ResponseTime Sensitivity MeasurementRange

OperatingRestriction EnergyRestriction

hasOperatingProperty only
OperatingRange OperatingProperty

EnvironmentalOperatingProperty MaintenanceSchedule OperatingPowerRange

hasSurvivalProperty only
SurvivalRange SurvivalProperty

EnvironmentalSurvivalProperty SystemLifetime BatteryLifetime

SSN Ontology with other Ontologies

19

A usage example
Upper
DOLCE SWEET
UltraLite

SSG4Env
infrastructure SSN

Schema

Service

External
FOAF Ordnance
Survey

Flood domain
Role Coastal Additional
Defences Regions

García-Castro R, Corcho O, Hill C. A Core Ontological Model for Semantic Sensor Web
Infrastructures. International Journal on Semantic Web and Information Systems Special
Issue on Sensor Networks, Internet of Things and Machine-to-Machine Communications,
Volume 8, Issue 1, 2013
20

AEMET Ontology Network (http://aemet.linkeddata.es/)

• 83 classes, 102 object properties, 80 datatype
properties
• SROIQ(D)

Let’s now talk about metadata and social stuff

Application

Middleware



Data Stream COAP
Virtual
Management
Sensors
Systems Sensors

(Social) Sensor Metadata

Do we have enough reliable metadata about
sensors and observations?

NO!!!! People are messy!!!
Even if they are scientists who do not use
Twitter

Let’s start with Swiss people (you know, they
are always precise ;-))

24

SwissEx

• Global Sensor Networks, deployment for SwissEx.

• 28 Deployments, Aprox. 50 sensors in each deployment
• More than 1500 sensors
• Live updates. Low frequency
• Access to all metadata/not all data
• 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
• Federated metadata management:
• 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

Sensor metadata
25

Sensor Metadata

station

location

sensors

model

properties

Sensors, Mappings and Queries 26

Sensor Data: Observations

Heterogeneity ?
Integration

Sensors, Mappings and Queries 27

Putting some meaning into place

Sensor Metadata

Do we have enough reliable metadata about
sensors and observations?

NO!!!! People are messy!!!
Even if they are scientists who do not use
Twitter

Let’s start with Swiss people (you know, they
are always precise ;-))

Swiss are boring… Let’s now move to the rest of
the world (citizen sensing)

29

Citizen Sensing: Pachube/COSM

?

30

Looking at the Data

Air Pressure

Temperature
31

Classifying Sensor Data

• Idea: given a new time series, find similar ones in a
set of classified time-series

• Querying time series
• e.g. find a sub-sequence in a time series database
• Measuring time series similarity
• e.g. are these time series the same?
• Time series classification
• e.g. classify heart beat series: normal, murmur, etc
32

Challenges: Data Summaries

• Challenges
• Represent the data
approximating with fewer
linear segments:
• Tradeoff between
Accuracy vs Numerosity

Data buckets

33

Data summaries: Linear Approximations

• We care about the angles

π/2 a π/4

a c
d b
a 0

c
-π/4
d
34

Use the representation for Classifying

• Linear approximation
• Compute distribution of the slopes
• K-nearest neighbour classification
• Training-Test datasets:
• SwissExperiment
• AEMET

35

Experiments: SwissEx and AEMET

36

Conclusions

Identifying Sensor Observations
Take a look at the data
Machine Learning Techniques
Time Series Summarization
Clustering
Normalization
Distance Metrics

Work in progress
Use more of the social tagging information!
Test in Pachube

Query processing

Application

Middleware



Data Stream COAP
Virtual
Management
Sensors
Systems Sensors

SPARQL with streaming extensions
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 { …

CQELS
…

40

SPARQL-Stream

SELECT ?windspeed ?tidespeed
FROM NAMED STREAM <http://swiss-experiment.ch/data#WannengratSensors.srdf>
[NOW-10 MINUTES TO NOW-0 MINUTES]
WHERE {
?WaveObs a ssn:Observation;
Aggregates
ssn:observationResult ?windspeed;
ssn:observedProperty sweetSpeed:WindSpeed.Static & Streaming
?TideObs a ssn:Observation;
ssn:observationResult ?tidespeed;
ssn:observedProperty sweetSpeed:TideSpeed. Windows
FILTER (?tidespeed<?windspeed)}
Filters, Functions

Disclaimer: some features NYI

In progress: Benchmarking

41

Queries to Sensor Data

SNEEql
RSTREAM SELECT id, speed, direction FROM wind [NOW];

Data Stream Management System
Esper QL
SELECT wind_speed FROM wind_sensor.win:time(10 min)

Complex Event Processors
GSN RESTful service
http://montblanc.slf.ch:22001/multidata?vs[0]=wind_sensor&field[0]=wind_speed&
from=15/09/2011+05:00:00&to=15/09/2011+15:00:00

Pachube RESTful service
http://api.pachube.com/v2/feeds/14321/datastreams/4?start=2011-09-
02T14:01:46Z&end=2011-09-02T17:01:46Z
Sensor Data Middleware

Querying through ontologies?
43

Differences among all these systems

• Different Query Expressivity
• Windows?
• Union, Filters, Joins?
• Aggregates, Groups?

• Different delivery & query mechanisms
• Pull, Push?
• Continuous queries?, One-off?

• How to merge streaming and static data queries

• Commonalities in RESTful Services

44

Where is the Data?

wan7 timed sp_wind
timed: datetime PK 1 3.4
Esper sp_wind: float
2 5.6
3 11.2
GSN
4 1.2
5 3.1
SNEE .. …

SELECT sp_wind FROM wan7 WHERE sp_wind >10

SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10

45

Where is the Data?

GSN server instance

.. wan7
sensor1
sensor2 timed: datetime PK
Esper sensor3 sp_wind: float
…

Mappings

ssn:Observation

46

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
qudt:numericValue Wan7/WindSpeed/ObsValue{timed}

xsd:decimal
sp_wind

47

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"

48

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; Rewriting 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

49

Rewriting to different technologies
SELECT ?windspeed
FROM NAMED STREAM <http://swiss-
experiment.ch/data#WannengratSensors.srdf>
[NOW-10 MINUTE TO NOW-0 MINUTE]
WHERE { Query
?WaveObs a ssn:Observation; Rewriting
ssn:observationResult ?windspeed;
Algebra
ssn:observedProperty sweetSpeed:WindSpeed.
} representation

SELECT wind_speed_scalar_av, timed FROM
wan7.win:time(10 min)
Esper (CEP)
SELECT wan7.wind_speed_scalar_av AS windspeed,
wan7.timed AS windts FROM wan7[FROM NOW-10
MINUTES TO NOW] SNEE (DSMS)

http://montblanc.slf.ch:22001/multidata?vs[0]=wan7&
field[0]=wind_speed_scalar_av&
from=15/05/2011+05:00:00&to=15/05/2011+15:00:00
GSN (Middleware)
http://api.pachube.com/v2/feeds/14321/datastreams/4?st
art=2011-09-02T14:01:46Z&end=2011-09-02T17:01:46Z
Pachube (Middleware)

50

Query rewriting

SELECT ?waveheight
[NOW – 5 HOUR TO NOW]
WHERE {
?WaveObs a ssn:ObservationValue;
qudt:numericalValue ?waveheight;
FILTER (?waveheight>10) }


Also for RESTful services

• 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

?

52

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

wan7

53

Using the Mappings

π timed,
sp_wind
SELECT ?waveheight

σ
[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
timed: datetime PK qudt:numericalValue Wan7/WindSpeed/ObsValue{timed}
sp_wind: float

xsd:datatype
sp_wind

54

Algebra construction

π timed,
sp_wind
windsensor1
windsensor2 σ sp_wind>10

ω 5 Hour

wan7

55

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
56

The whole picture

Application

Middleware



Data Stream COAP
Virtual
Management
Sensors
Systems Sensors

In Summary, and for the future

• Use ontologies to query sensor data
• Using R2RML mappings (as in what I have been presenting)
• Using native RDF streams (as what Manfred presented on
Tuesday)
• Use extensions to SPARQL to handle data streams
• However, there is a need to standardise all these query
languages
• There is also a need for good benchmarks
• Different underlying systems can provide support to
data streams
• DSMS, CEP, RESTful services
• What is the best one for each purpose? Again, we need to
have good benchmarks

60

Some 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.
• García-Castro R, Corcho O. Five challenges for the Semantic
Sensor Web. Semantic Web Journal 1, 2011
• García-Castro R, Corcho O, Hill C. A Core Ontological Model for
Semantic Sensor Web Infrastructures. International Journal
on Semantic Web and Information Systems Special Issue on Sensor
Networks, Internet of Things and Machine-to-Machine
Communications, Volume 8, Issue 1, 2013
• Calbimonte JP, Jeung H, Corcho O, Aberer K. Enabling Query
Technologies for the Semantic Sensor Web. International
Journal on Semantic Web and Information Systems Special Issue
on Sensor Networks, Internet of Things and Machine-to-Machine
Communications, Volume 8, Issue 1, 2013

Instructions for hands-on session

• It will be focused on understanding how sensor data can be
retrieved
• Disclaimer: a bit of cheating (working on historical data from
weather stations in Spain, and no continuous SPARQL querying)
• For those interested in hardcore stuff, talk to me later

• Quick instructions
• Based on the material that you already used for session 2
• Remember: Javascript, SPARQL queries, etc.
• Download a zip file from http://bit.ly/LHsqLx
• In fact, you will find it already in your desktop as sssw12-ho-
s7.zip
• Unzip it into /var/www (remember ‘sudoing’ and ‘chowning’ or
‘chmoding’)
• Go to http://localhost/sssw12-ho-s7/s7.html (instructions)
• The results will be at http://localhost/sssw12-ho-s7/index.html

63

Semantic (Social) Sensor Networks

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