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Semantic Sensor Network
Ontology: description et
usage
Catherine ROUSSEY
4 septembre 2013
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea

www.irstea.fr

Merci à
slide share,
Jean Paul CALBIMONT,
Oscar CORCHO,
W3C SSN Working Group
2

Plan
•
•
•
•
•
•

Définitions de base: de l‟Ontologie aux ontologies
Motivations
W3C SSN group
SSN ontologies
Use Cases
Projets
3

Définitions:
DONNÉES, INFORMATIONS, CONNAISSANCES
Donnée: un élément d‟information,
percevable,
manipulable
Information: donnée +
sens + contexte
type
Connaissance: information +
stabilité + croyance
abstraction + traitement
généralisation d‟un ensemble d‟information = modèle
toujours propre à une personne
partagée par d‟autres personnes
4

Schéma général
DONNÉES, INFORMATIONS, CONNAISSANCES
Connaissances

Résultat d‟un processus d‟apprentissage: une
généralisation d‟un ensemble d‟information que
l‟on va mémoriser

Information

Sens dans un contexte

Données

Perception

Connaissances en IA
Classes en POO
BD Relationnelle
Données typées
Données

Des traitement particuliers sur les données
qualitatives

Description sous forme d‟attribut (description
quantitative & qualitative ) + méthodes
(traitements)
Données fortement structurées optimisées pour le
stockage
Différent niveau de granularité : information
structurée  non structurées
5

Définition
ONTOLOGIE
Ontologie avec un O majuscule (philosophie):
Une science: une branche de la métaphysique qui a pour objectif
l‟étude de l‟être, c'est-à-dire l'étude des propriétés générales de tout
ce qui est…
Ontologies au pluriel avec un o minuscule (informatique):
Outils informatiques
résultat d‟une modélisation d‟un domaine d‟étude
défini pour un objectif donné
acceptée par une communauté d‟utilisateurs
…
6

Ontologies …
Gruber 1993 : « une ontologie est une spécification explicite d’une
conceptualisation »
•
•

Conceptualisation: modèle abstrait du domaine: quelles entités?
Spécification explicite: les types et leurs contraintes d’usage sont définis
dans un langage…

Exemples:
•
•
•

Un thésaurus : vocabulaire normalisé
Un schéma de BD : un modèle structuré d'un domaine
Un système expert : un modèle du domaine formalisé pour les
inférences, des conditions exprimées à l'aide de formules logiques

Ontologie linguistique, ressource termino-ontologique, ontologie de
domaine, ontologie de haut niveau, un vocabulaire de métadonnées…
Thomas R. Gruber. “A translation approach to portable ontology
specifications”, Knowledge Acquisition, Volume 5, Issue 2, June 1993, Pages 199–
220
7

Motivation: Ontologie
UNE ONTOLOGIE DE CAPTEURS POURQUOI FAIRE ?
Promouvoir un accès universel et uniformisé des données de capteurs
par le web:
• publier les données sur le web
• interroger ces données avec des techno web
• intégrer les données de capteurs avec d'autres données
• traiter ces données (par exemple les nettoyer pour améliorer leur
qualité)
Une ontologie contient un vocabulaire et un schéma de données:
• consensuels,
• publiés sur le web et documentés
• formalisés avec des standards du web (RDF, OWL, SPARQL)
• Avec des contraintes en DL (conditions nécessaires et/ou suffisantes)
= un schéma de données pour le web de données
8

Définition: Le web de données Linked Data
An extension of the
current Web…
… where data are given
well-defined and
explicitly represented
meaning, …
… so that it can be
shared and used by
humans and machines,
...
... better enabling them
to work in cooperation
And clear principles on
how to publish data
9

Publication sur le web de données
4 Principes:
• Use URIs as names for things
• Use HTTP URIs so that people can look up those names.
• When someone looks up a URI, provide useful information, using the
standards (RDF*, SPARQL)
•

Dereferenceable URI

• Include links to other URIs, so that they can discover more things.
10

Motivation: flux et métadonnées
QU'EST CE QUE SONT LES DONNÉES DE CAPTEURS ?
•Flux de données (Data Stream)
•
•
•
•

Données issues de mesure
Données continues, potentiellement infinie
Données avec des estampilles temporelles (time stamped tuple)
Données bruitées (noisy)
(t9, a1, a2, ... , an)

• un réseau produit plusieurs flux hétérogènes
•

Station météo: précipitation, direction du vent

(t8, a1, a2, ... , an)
(t7, a1, a2, ... , an)
...
...
(t1, a1, a2, ... , an)
...
...

•Métadonnées: données sur les données
•
•

Description du réseau de capteurs : localisation, nb de nœuds
Description des nœuds: niveau d'énergie, sondes, paramétrage des sondes
11

Données de capteurs: exemple
12

Données de capteurs: exemple
13

Motivation: Interrogation
Flux de données: requête continue
• fenêtre temporelle
•Les dernières données

(t9, a1, a2, ... , an)
(t8, a1, a2, ... , an)
(t7, a1, a2, ... , an)
...
...
(t1, a1, a2, ... , an)
...
...

Réseau de capteurs:
• ressources limitées: énergie, traitement, stockage
• exécution distribuée des requêtes
• routage, optimisation
Query
• Interrogation
•
•
•

native en utilisant API propre
stockage des flux dans une BD
publication sur le web de données

Window
[t7 - t9]
14

W3C Semantic Sensor Incubator Group
: SSN XG
SSN – XG : mars 2009
41 Participants de 16 organisations : Des grands noms du domaine des
ontologies et des réseaux de capteurs : CSIRO, Wright State University, OGC, DERI, OEG,
Knoesis etc…

Objectifs:
• Proposer un modèle unifié de données de capteurs et de métadonnées
• Etat de l‟art sur les ontologies de capteurs existantes
• Proposer des méthodes de développements applications intelligentes
travaillant sur les données de capteurs
Résultat :
une ontologie qui intègre plusieurs ontologies existantes, validées dans des
projets.

Final Report 28 June 2011
http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
15

Semantic Sensor Network Ontology
Format OWL 2, disponible sur le web et documentée
(!!) Orientée capteur uniquement, compatible avec les standards de OGC
Aligner sur l‟ontologie de haut niveau Dolce Ultra Light (DUL)
 Faciliter l‟intégration avec d‟autres ontologies
 SSN ne s‟utilise jamais seule (!!), chaque application ne réutilise qu‟une sous partie
de l‟ontologie
Ontologie modulaire basé sur des patrons de conception (Design Pattern)
 Importe que les parties nécessaires
 Faciliter l‟évolution de l‟ontologie
 Répond à plusieurs cas d‟usage (4)
 Permettre d‟avoir plusieurs niveaux de description
 « Redondance » voulue et nécessaire
Semantic Sensor Network Ontology: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
M. Compton et al. The SSN ontology of the W3C semantic sensor network incubator
group. Web Semantics: Science, Services and Agents on the World Wide Web
Volume 17, December 2012, pp 25–32
16

SSN 4 Use Cases
17

Modules de SSN
System

OperatingRestriction

Deployment

Process

Device
PlatformSite

Data
Skeleton

MeasuringCapability

ConstraintBlock
18

Modules de SSN
19

Les autres ontologies nécessaires
•
•
•
•

Ontologies d‟unités
Ontologies géographiques de position et de lieux
Classification de tous les types de sondes
Ontologies des phénomènes observés et de leurs propriétés

SSN est une base pour construire une ontologie d‟application
20

Ontology Design Pattern: ODP SSO
STIMULUS SENSOR OBSERVATION

Sensor is anything that observes

What is sensed?
What senses ?

How it senses ?
21

Ontology Design Pattern: SSO in SSN
STIMULUS SENSOR OBSERVATION

Sensor is anything that observes

What is sensed?
What senses ?

How it senses ?
22

DUL et SSN
23

SSN: Sensor property

Skeleton

Property

MeasuringCapability

Communication

hasMeasurementProperty only

MeasurementCapability

Accuracy
DetectionLimit

MeasurementProperty

Resolution
Drift

Selectivity

ResponseTime

Frequency
Sensitivity

Precision

Latency

MeasurementRange

OperatingRestriction

EnergyRestriction

hasOperatingProperty only

OperatingProperty

OperatingRange

EnvironmentalOperatingProperty

MaintenanceSchedule

OperatingPowerRange

hasSurvivalProperty only

SurvivalRange

SurvivalProperty

EnvironmentalSurvivalProperty

SystemLifetime

BatteryLifetime
24

SSN: Sensor property
25

SSN: Sensor property
26

SSN: Deployment
27

SSN: Deployment
28

Données de capteurs : Observation

ssn:isProducedBy
ssn:Sensor
ssn:observedBy

ssn:SensorOutput

ssn:observationResult

ssn:Observation

ssn:hasValue
ssn:ObservationValue

ssn:observes
ssn:featureOfInterest

ssn:observedProperty

quantityValue

ssn:FeatureOfInterest
xsd:datatype
ssn:Property

ssn:hasProperty
29

SSN Observation instance
ssn:observedProperty

ssn:Observation
ssn:observationResult

http://swissex.ch/data#
Wan7/WindSpeed/Observation{timed}

ssn:SensorOutput
ssn:hasValue

http://swissex.ch/data#
Wan7/ WindSpeed/ ObsOutput{timed}

ssn:ObservationValue
qudt:numericValue

http://swissex.ch/data#
Wan7/WindSpeed/ObsValue{timed}

xsd:decimal
sp_wind

ssn:Property
sweetSpeed:WindSpeed
30

Data + Sensor discovery and linking
SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH

WindSpeed : 6.245
At: 2011-1026T21:32:52

Sensor Data
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 ;
31

Métadonnées du capteur
ssn:OperatingRange
ssn:hasOperatingRange

ssn:Deployment
ssn:hasDeployment

ssn:Sensing
ssn:implements

ssn:System

ssn:Sensor

ssn:hasMeasurementCapability

ssn:deployedOnPlatform

ssn:onPlatform
ssn:Device

ssn:MeasurementCapability
ssn:SensingDevice

ssn:Platform
32

Data + Sensor discovery and linking
SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH

Sensor metadata

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"].

station
SSN Use Cases:
Data discovery and linking
Sensor Device selection and discovery
Application et projet
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea

www.irstea.fr
34

SSN Uses Case: data discovery and linking
FLOOD RISK ALERT: SEMSORGRID4ENV
Emergency
planner
Real-time
data

Wave,
Wind,
Tide

Meteorological
forecasts

Detect conditions likely to cause a flood
Example:
• “provide me with the wind speed observations average over the
last minute, if it is higher than the average of the last 2 to 3 hours”
35

SSN Uses Case: data discovery and linking
SEMSORGRID4ENV PROJECT WWW.SEMSORGRID4ENV.EU
Emergency
planner

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
36

SSN Use Cases: Sensor Discovery
SWISSEXPERIMENT
Distributed environment: GSN Davos, GSN Zurich, etc.
•
•

In each site, a number of sensors available
Each one with different schema

Metadata stored in wiki
•

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
37

SSN Use Case: Sensor Discovery
38

Data + Sensor discovery and linking
SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH

Geo
Researcher

Real-time
data

Snow,
Wind,
Radiation.
Lots of stuff

Provide data to create models and compare them to real data
Example:
• “I want to calculate how much snow is lost by evaporation
• So provide me with the snow quantity observations and the air
temperature observations in the station near Geneva over the last
year ”
39

Sensor Metadata
station

location

sensors
40

Data + Sensor discovery and linking
SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH

Sensor metadata

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"].

station
41

Data + Sensor discovery and linking
SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH

WindSpeed : 6.245
At: 2011-1026T21:32:52

Sensor Data
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 ;
Stream and SPARQL:
interrogation sur le sensor
web
J P Calbimonte PhD Thesis
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea

www.irstea.fr

Jean-Paul Calbimonte, Hoyoung Jeung, Óscar Corcho, Karl Aberer: Enabling Query
Technologies for the Semantic Sensor Web. Int. J. Semantic Web Inf. Syst. 8(1): 4363 (2012)
43

Management of heterogeneous data
STATE OF THE ART:

DS
MS

DQP

QP

S-RDF

Ontology-based
Data Access

Heterogeneous
data Integration

R2O +
ODEMapster

Streaming Data
Access

Distributed Query
Processing

SNEE/SNEEql

q

Semantic
Integrator

RDF Streams
Querying

C-SPARQL
extensions
44

Extention SPARQL pour les flux
STATE OF THE ART
SNEEql
RSTREAM SELECT id, speed, direction
FROM wind[NOW];
Streaming SPARQL
PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
SELECT ?sensor ?speed ?direction
FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS
WHERE {
?sensor a fire:WindSensor;
fire:hasMeasurements ?WindSpeed, ?WindDirection.
?WindSpeed a fire:WindSpeedMeasurement;
fire:hasSpeedValue ?speed;
fire:hasTimestampValue ?wsTime.
?WindDirection a fire:WindDirectionMeasurement;
fire:hasDirectionValue ?direction;
fire:hasTimestampValue ?dirTime.
FILTER (?wsTime == ?dirTime)
}
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 { …
45

How to deal with Linked Stream/Sensor Data
Ingredients
• An ontology model
• Good practices in URI definition
• Supporting semantic technology
•
•
•

SPARQL extensions
To handle time and tuple windows
To handle spatio-temporal constraints

• REST APIs to access it
Another example: semantically enriching GSN
A couple of lessons learned
46

Lessons Learned
• Sensor data is yet another good source of data with some special
properties
• Everything that we do with our relational datasets or other data
sources can be done with sensor data
• Manage separately data and metadata of the sensors
• Data should always be separated between realtime-data and
historical-data
• Use the time format xsd:dateTime and the time zone
• Graphical representation of data for weeks or months is not trivial
anyway
47

Ontology-based Streaming Data Access
SPARQLStream algebra(S1 S2 Sm)
q

Query
translation

Client

SPARQLStream (Og)

Stream-to-Ontology
Mappings
R2RML

[triples]

Data
translation

Target query/ request
SNEEql

Query Evaluator

Sensor
Network (S1)
Relational
DB (S2)
Stream
Engine (S3)

[tuples]

Ontology-based Streaming Data Access Service

RDF Store
(Sm)
48

Enabling Ontology-based Access to Stream
Example: “provide me with the wind speed observations over the last
minute in the Solent Region ”

cd:Observation
cd:observationResult
cd:observedProperty
xsd:double
cd:Property
cd:featureOfInterest

cd:Feature

cd:locatedInRegion

cd:Region

PREFIX cd:
<http://www.semsorgrid4env.eu/ontologies/CoastalDefences.owl#>
PREFIX sb: <http://www.w3.org/2009/SSNXG/Ontologies/SensorBasis.owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?windspeed ?windts
FROM STREAM <http://www.semsorgrid4env.eu/ccometeo.srdf>
[ NOW – 1 MINUTE TO NOW – 0 MINUTES ]
WHERE
{
?WindObs a cd:Observation;
cd:observationResult ?windspeed;
cd:observationResultTime ?windts;
cd:observedProperty ?windProperty;
cd:featureOfInterest ?windFeature.
?windFeature a cd:Feature;
cd:locatedInRegion cd:SolentCCO.
?windProperty a cd:WindSpeed.
}
49

Enabling Ontology-based Access to Stream
RDF-Stream

...
...
( <si-1,pi-1, oi-1>, ti-1 ),
( <si, pi, oi>, ti ),
( <si+1,pi+1, oi+1>, ti+1 ),
...
...

Example: “provide me with the wind speed observations over the last minute in
the Solent Region ”

cd:Observation

cd:observationResult

xsd:double

STREAM
<http://www.semsorgrid4env.eu/ccometeo.srdf>
...
...
( <ssg4e:Obs1,rdf:type, cd:Observation>, ti ),
( <ssg4e:Obs1,cd:observationResult,”34.5”>, ti ),
( <ssg4e:Obs2,rdf:type, cd:Observation>, ti+1 ),
( <ssg4e:Obs2,cd:observationResult,”20.3”>, ti+1 ),
...
...
50

Query translation
envdata_westbay
Feature

envdata_chesil

v
envdata_milford
v
envdata_hornsea
v
envdata_rhylflats
v
Timestamp: long

Observation
hasObservation
Result

Mapping

Hs : float
Lon: float
Lat: float

observedProperty

xsd:float

locatedIn
Region

Region

WaveHeightProperty

SPARQL stream
PREFIX cd: <http://www.semsorgrid4env.eu/ontologies/CoastalDefences.owl#>
PREFIX sb: <http://www.w3.org/2009/SSN-XG/Ontologies/SensorBasis.owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?waveheight ?wavets ?lat ?lon
FROM STREAM <http://www.semsorgrid4env/ccometeo.srdf>
WHERE
{
?WaveObs a cd:Observation;
cd:observationResult ?waveheight;
cd:observationResultTime ?wavets;
cd:observationResultLatitude ?lat;
cd:observationResultLongitude ?lon;
cd:observedProperty ?waveProperty;
cd:featureOfInterest ?waveFeature.
?waveFeature a cd:Feature;
cd:locatedInRegion cd:SouthEastEnglandCCO.
?waveProperty a cd:WaveHeight.
}

SNEEql
(SELECT Lon,timestamp,Hs,Lat FROM envdata_rhylflats) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_hornsea) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_milford) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_chesil) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_perranporth) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_westbay) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_pevenseybay)
51

Mapping declaration
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"
52

Data Translation
ssn:observedProperty

ssn:Observation
http://swissex.ch/data#
Wan7/WindSpeed/Observation{timed}

ssn:observationResult

wan7
timed: datetime PK
sp_wind: float

ssn:SensorOutput
ssn:hasValue

http://swissex.ch/data#
Wan7/ WindSpeed/ ObsOutput{timed}

ssn:ObservationValue
qudt:numericValue

http://swissex.ch/data#
Wan7/WindSpeed/ObsValue{timed}

xsd:decimal
sp_wind

ssn:Property
sweetSpeed:WindSpeed
Extention de SSN
Wireless Semantic Sensor
Ontology
Rimel BENDADOUCHE PhD Thesis
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea

www.irstea.fr

Bendadouche et al; SSN 2012
54

Wireless Sensor Network (WSN)
NEEDS AND OBJECTIVES
 Adapt the WSN node behavior to the context:
•
•

Node state
Phenomena state

Context: ”The context is a set of entities states or information
describing an environment where an event occurs”
State: ”The state is a qualitative data, which changes over time
summarizing a set of information”
SSN'12
12/11/2012

 Enhance the lifetime and the good functioning of the network

WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
55

What is a context ?
FLOOD PHENOMENA
FLOOD PHENOMENA STATE:
1. “Normal”
2. “Waiting for rise in water levels”
3. “Rise in water levels”
4. “Flood warning”

NODE (ENERGY) STATE:
1. Strong Energy state
2. Average Energy state
3. Low Energy state
56

Wireless Sensor Network (WSN)
Phenomena state Normal
<weather> node
sends its
measures
<weather>
node sends
nothing

WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
57

WSN and its devices

SSN'12
12/11/2012

WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
58

Communication: Stimulus-WSNnodeCommunication pattern

SSN'12
12/11/2012

WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
59

Communication process

SSN'12
12/11/2012

WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
60

State
OUR EXAMPLE

SSN'12
12/11/2012

WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
61

The use of the WSSN ontology
USING TOOLS

• Develop the WSSN ontology
•

Protégé

• JESS rule engine
•

Derive the state from the sensor data

• Simulate the WSN and its nodes behaviour
•

JADE Simulator

WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
Others projects

Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea

www.irstea.fr
63

Project: Sensei
INTEGRATING THE PHYSICAL WITH THE DIGITAL WORLD OF THE
NETWORK OF THE FUTURE

•
•
•
•

Smart Cities: Transport, energy consumption etc…
the EU's 7 Framework Programme
January 2008  December 2010
19 partners from 11 European countries

http://www.sensei-project.eu/
Zhang, Y., Meratnia, N.and Havinga, P.J.M.(2010) „Ensuring high sensor data quality
through use of online outlier detection techniques‟,Int. J. Sensor Networks, Vol. 7, No.
3, pp.141–151

Bahrepour, Majid and Meratnia, Nirvana and Havinga, Paul J.M. (2010) Fast and Accurate
Residential Fire Detection Using Wireless Sensor Networks. Environmental Engineering
and Management Journal, 9 (2). pp. 215-221. ISSN 1582-9596
64

Project: KNOESIS Semantic Sensor Web

http://knoesis.wright.edu/
J. Pschorr, C. Henson, H. Patni and A. Sheth Sensor Discovery on Linked
Data. Kno.e.sis Center, Wright University, Dayton, USA, 2010.
65

Project: SPITFIRE
SEMANTIC WEB INTERACTION WITH REAL OBJECTS

http://spitfire-project.eu/

SmartServiceProxy
aggregate semantic sensor data into representations of real-world things
called Semantic Entities
provide RESTful direct access to them.
Not yet publicly accessible
66

Project: 52 North
SEMANTIC WEB INTERACTION WITH REAL OBJECTS
http://52north.org/

Sensor Observation Service:
publication of sensor data in RDF
SWEET ontology

Janowicz, K. , Bröring, A., Stasch, C., Schade, S ., Everding, T., & A. Llaves
(2011): A RESTful Proxy and Data Model for Linked Sensor Data.
International Journal of Digital Earth, pp. 1 - 22.

Arne Bröring, Patrick Maué, Krzysztof Janowicz, Daniel Nüst, and Christian
Malewski . Semantically-Enabled Sensor Plug & Play for the Sensor Web
Sensor Plug&Play framework
Sensors 2011, 11(8), pp. 7568-7605.
67

Conclusion & Perspectives
SSN Ontology used in several projects for publishing data sensor on the
web of data…
Some works has to be done:
• good practices in URL definition
• Vizualisation of spatio temporal data
• Distributed reasoning

Follows the Semantic Sensor Network Workshop at ISWC
• SSN13 October 2013 Sydney
• SSN12 http://knoesis.org/ssn2012/
• SSN11 http://ceur-ws.org/Vol-839/
• SSN10 http://ceur-ws.org/Vol-668/
• SSN 2009 http://ceur-ws.org/Vol-522/
• SSN 2006 http://www.ict.csiro.au/ssn06/

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Semantic Sensor Network Ontology: Description et usage

  • 1. Semantic Sensor Network Ontology: description et usage Catherine ROUSSEY 4 septembre 2013 Pour mieux affirmer ses missions, le Cemagref devient Irstea www.irstea.fr Merci à slide share, Jean Paul CALBIMONT, Oscar CORCHO, W3C SSN Working Group
  • 2. 2 Plan • • • • • • Définitions de base: de l‟Ontologie aux ontologies Motivations W3C SSN group SSN ontologies Use Cases Projets
  • 3. 3 Définitions: DONNÉES, INFORMATIONS, CONNAISSANCES Donnée: un élément d‟information, percevable, manipulable Information: donnée + sens + contexte type Connaissance: information + stabilité + croyance abstraction + traitement généralisation d‟un ensemble d‟information = modèle toujours propre à une personne partagée par d‟autres personnes
  • 4. 4 Schéma général DONNÉES, INFORMATIONS, CONNAISSANCES Connaissances Résultat d‟un processus d‟apprentissage: une généralisation d‟un ensemble d‟information que l‟on va mémoriser Information Sens dans un contexte Données Perception Connaissances en IA Classes en POO BD Relationnelle Données typées Données Des traitement particuliers sur les données qualitatives Description sous forme d‟attribut (description quantitative & qualitative ) + méthodes (traitements) Données fortement structurées optimisées pour le stockage Différent niveau de granularité : information structurée  non structurées
  • 5. 5 Définition ONTOLOGIE Ontologie avec un O majuscule (philosophie): Une science: une branche de la métaphysique qui a pour objectif l‟étude de l‟être, c'est-à-dire l'étude des propriétés générales de tout ce qui est… Ontologies au pluriel avec un o minuscule (informatique): Outils informatiques résultat d‟une modélisation d‟un domaine d‟étude défini pour un objectif donné acceptée par une communauté d‟utilisateurs …
  • 6. 6 Ontologies … Gruber 1993 : « une ontologie est une spécification explicite d’une conceptualisation » • • Conceptualisation: modèle abstrait du domaine: quelles entités? Spécification explicite: les types et leurs contraintes d’usage sont définis dans un langage… Exemples: • • • Un thésaurus : vocabulaire normalisé Un schéma de BD : un modèle structuré d'un domaine Un système expert : un modèle du domaine formalisé pour les inférences, des conditions exprimées à l'aide de formules logiques Ontologie linguistique, ressource termino-ontologique, ontologie de domaine, ontologie de haut niveau, un vocabulaire de métadonnées… Thomas R. Gruber. “A translation approach to portable ontology specifications”, Knowledge Acquisition, Volume 5, Issue 2, June 1993, Pages 199– 220
  • 7. 7 Motivation: Ontologie UNE ONTOLOGIE DE CAPTEURS POURQUOI FAIRE ? Promouvoir un accès universel et uniformisé des données de capteurs par le web: • publier les données sur le web • interroger ces données avec des techno web • intégrer les données de capteurs avec d'autres données • traiter ces données (par exemple les nettoyer pour améliorer leur qualité) Une ontologie contient un vocabulaire et un schéma de données: • consensuels, • publiés sur le web et documentés • formalisés avec des standards du web (RDF, OWL, SPARQL) • Avec des contraintes en DL (conditions nécessaires et/ou suffisantes) = un schéma de données pour le web de données
  • 8. 8 Définition: Le web de données Linked Data An extension of the current Web… … where data are given well-defined and explicitly represented meaning, … … so that it can be shared and used by humans and machines, ... ... better enabling them to work in cooperation And clear principles on how to publish data
  • 9. 9 Publication sur le web de données 4 Principes: • Use URIs as names for things • Use HTTP URIs so that people can look up those names. • When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) • Dereferenceable URI • Include links to other URIs, so that they can discover more things.
  • 10. 10 Motivation: flux et métadonnées QU'EST CE QUE SONT LES DONNÉES DE CAPTEURS ? •Flux de données (Data Stream) • • • • Données issues de mesure Données continues, potentiellement infinie Données avec des estampilles temporelles (time stamped tuple) Données bruitées (noisy) (t9, a1, a2, ... , an) • un réseau produit plusieurs flux hétérogènes • Station météo: précipitation, direction du vent (t8, a1, a2, ... , an) (t7, a1, a2, ... , an) ... ... (t1, a1, a2, ... , an) ... ... •Métadonnées: données sur les données • • Description du réseau de capteurs : localisation, nb de nœuds Description des nœuds: niveau d'énergie, sondes, paramétrage des sondes
  • 13. 13 Motivation: Interrogation Flux de données: requête continue • fenêtre temporelle •Les dernières données (t9, a1, a2, ... , an) (t8, a1, a2, ... , an) (t7, a1, a2, ... , an) ... ... (t1, a1, a2, ... , an) ... ... Réseau de capteurs: • ressources limitées: énergie, traitement, stockage • exécution distribuée des requêtes • routage, optimisation Query • Interrogation • • • native en utilisant API propre stockage des flux dans une BD publication sur le web de données Window [t7 - t9]
  • 14. 14 W3C Semantic Sensor Incubator Group : SSN XG SSN – XG : mars 2009 41 Participants de 16 organisations : Des grands noms du domaine des ontologies et des réseaux de capteurs : CSIRO, Wright State University, OGC, DERI, OEG, Knoesis etc… Objectifs: • Proposer un modèle unifié de données de capteurs et de métadonnées • Etat de l‟art sur les ontologies de capteurs existantes • Proposer des méthodes de développements applications intelligentes travaillant sur les données de capteurs Résultat : une ontologie qui intègre plusieurs ontologies existantes, validées dans des projets. Final Report 28 June 2011 http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
  • 15. 15 Semantic Sensor Network Ontology Format OWL 2, disponible sur le web et documentée (!!) Orientée capteur uniquement, compatible avec les standards de OGC Aligner sur l‟ontologie de haut niveau Dolce Ultra Light (DUL)  Faciliter l‟intégration avec d‟autres ontologies  SSN ne s‟utilise jamais seule (!!), chaque application ne réutilise qu‟une sous partie de l‟ontologie Ontologie modulaire basé sur des patrons de conception (Design Pattern)  Importe que les parties nécessaires  Faciliter l‟évolution de l‟ontologie  Répond à plusieurs cas d‟usage (4)  Permettre d‟avoir plusieurs niveaux de description  « Redondance » voulue et nécessaire Semantic Sensor Network Ontology: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn M. Compton et al. The SSN ontology of the W3C semantic sensor network incubator group. Web Semantics: Science, Services and Agents on the World Wide Web Volume 17, December 2012, pp 25–32
  • 16. 16 SSN 4 Use Cases
  • 19. 19 Les autres ontologies nécessaires • • • • Ontologies d‟unités Ontologies géographiques de position et de lieux Classification de tous les types de sondes Ontologies des phénomènes observés et de leurs propriétés SSN est une base pour construire une ontologie d‟application
  • 20. 20 Ontology Design Pattern: ODP SSO STIMULUS SENSOR OBSERVATION Sensor is anything that observes What is sensed? What senses ? How it senses ?
  • 21. 21 Ontology Design Pattern: SSO in SSN STIMULUS SENSOR OBSERVATION Sensor is anything that observes What is sensed? What senses ? How it senses ?
  • 23. 23 SSN: Sensor property Skeleton Property MeasuringCapability Communication hasMeasurementProperty only MeasurementCapability Accuracy DetectionLimit MeasurementProperty Resolution Drift Selectivity ResponseTime Frequency Sensitivity Precision Latency MeasurementRange OperatingRestriction EnergyRestriction hasOperatingProperty only OperatingProperty OperatingRange EnvironmentalOperatingProperty MaintenanceSchedule OperatingPowerRange hasSurvivalProperty only SurvivalRange SurvivalProperty EnvironmentalSurvivalProperty SystemLifetime BatteryLifetime
  • 28. 28 Données de capteurs : Observation ssn:isProducedBy ssn:Sensor ssn:observedBy ssn:SensorOutput ssn:observationResult ssn:Observation ssn:hasValue ssn:ObservationValue ssn:observes ssn:featureOfInterest ssn:observedProperty quantityValue ssn:FeatureOfInterest xsd:datatype ssn:Property ssn:hasProperty
  • 29. 29 SSN Observation instance ssn:observedProperty ssn:Observation ssn:observationResult http://swissex.ch/data# Wan7/WindSpeed/Observation{timed} ssn:SensorOutput ssn:hasValue http://swissex.ch/data# Wan7/ WindSpeed/ ObsOutput{timed} ssn:ObservationValue qudt:numericValue http://swissex.ch/data# Wan7/WindSpeed/ObsValue{timed} xsd:decimal sp_wind ssn:Property sweetSpeed:WindSpeed
  • 30. 30 Data + Sensor discovery and linking SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH WindSpeed : 6.245 At: 2011-1026T21:32:52 Sensor Data 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 ;
  • 32. 32 Data + Sensor discovery and linking SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH Sensor metadata 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"]. station
  • 33. SSN Use Cases: Data discovery and linking Sensor Device selection and discovery Application et projet Pour mieux affirmer ses missions, le Cemagref devient Irstea www.irstea.fr
  • 34. 34 SSN Uses Case: data discovery and linking FLOOD RISK ALERT: SEMSORGRID4ENV Emergency planner Real-time data Wave, Wind, Tide Meteorological forecasts Detect conditions likely to cause a flood Example: • “provide me with the wind speed observations average over the last minute, if it is higher than the average of the last 2 to 3 hours”
  • 35. 35 SSN Uses Case: data discovery and linking SEMSORGRID4ENV PROJECT WWW.SEMSORGRID4ENV.EU Emergency planner 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
  • 36. 36 SSN Use Cases: Sensor Discovery SWISSEXPERIMENT Distributed environment: GSN Davos, GSN Zurich, etc. • • In each site, a number of sensors available Each one with different schema Metadata stored in wiki • 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
  • 37. 37 SSN Use Case: Sensor Discovery
  • 38. 38 Data + Sensor discovery and linking SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH Geo Researcher Real-time data Snow, Wind, Radiation. Lots of stuff Provide data to create models and compare them to real data Example: • “I want to calculate how much snow is lost by evaporation • So provide me with the snow quantity observations and the air temperature observations in the station near Geneva over the last year ”
  • 40. 40 Data + Sensor discovery and linking SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH Sensor metadata 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"]. station
  • 41. 41 Data + Sensor discovery and linking SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH WindSpeed : 6.245 At: 2011-1026T21:32:52 Sensor Data 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 ;
  • 42. Stream and SPARQL: interrogation sur le sensor web J P Calbimonte PhD Thesis Pour mieux affirmer ses missions, le Cemagref devient Irstea www.irstea.fr Jean-Paul Calbimonte, Hoyoung Jeung, Óscar Corcho, Karl Aberer: Enabling Query Technologies for the Semantic Sensor Web. Int. J. Semantic Web Inf. Syst. 8(1): 4363 (2012)
  • 43. 43 Management of heterogeneous data STATE OF THE ART: DS MS DQP QP S-RDF Ontology-based Data Access Heterogeneous data Integration R2O + ODEMapster Streaming Data Access Distributed Query Processing SNEE/SNEEql q Semantic Integrator RDF Streams Querying C-SPARQL extensions
  • 44. 44 Extention SPARQL pour les flux STATE OF THE ART SNEEql RSTREAM SELECT id, speed, direction FROM wind[NOW]; Streaming SPARQL PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#> SELECT ?sensor ?speed ?direction FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS WHERE { ?sensor a fire:WindSensor; fire:hasMeasurements ?WindSpeed, ?WindDirection. ?WindSpeed a fire:WindSpeedMeasurement; fire:hasSpeedValue ?speed; fire:hasTimestampValue ?wsTime. ?WindDirection a fire:WindDirectionMeasurement; fire:hasDirectionValue ?direction; fire:hasTimestampValue ?dirTime. FILTER (?wsTime == ?dirTime) } 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 { …
  • 45. 45 How to deal with Linked Stream/Sensor Data Ingredients • An ontology model • Good practices in URI definition • Supporting semantic technology • • • SPARQL extensions To handle time and tuple windows To handle spatio-temporal constraints • REST APIs to access it Another example: semantically enriching GSN A couple of lessons learned
  • 46. 46 Lessons Learned • Sensor data is yet another good source of data with some special properties • Everything that we do with our relational datasets or other data sources can be done with sensor data • Manage separately data and metadata of the sensors • Data should always be separated between realtime-data and historical-data • Use the time format xsd:dateTime and the time zone • Graphical representation of data for weeks or months is not trivial anyway
  • 47. 47 Ontology-based Streaming Data Access SPARQLStream algebra(S1 S2 Sm) q Query translation Client SPARQLStream (Og) Stream-to-Ontology Mappings R2RML [triples] Data translation Target query/ request SNEEql Query Evaluator Sensor Network (S1) Relational DB (S2) Stream Engine (S3) [tuples] Ontology-based Streaming Data Access Service RDF Store (Sm)
  • 48. 48 Enabling Ontology-based Access to Stream Example: “provide me with the wind speed observations over the last minute in the Solent Region ” cd:Observation cd:observationResult cd:observedProperty xsd:double cd:Property cd:featureOfInterest cd:Feature cd:locatedInRegion cd:Region PREFIX cd: <http://www.semsorgrid4env.eu/ontologies/CoastalDefences.owl#> PREFIX sb: <http://www.w3.org/2009/SSNXG/Ontologies/SensorBasis.owl#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?windspeed ?windts FROM STREAM <http://www.semsorgrid4env.eu/ccometeo.srdf> [ NOW – 1 MINUTE TO NOW – 0 MINUTES ] WHERE { ?WindObs a cd:Observation; cd:observationResult ?windspeed; cd:observationResultTime ?windts; cd:observedProperty ?windProperty; cd:featureOfInterest ?windFeature. ?windFeature a cd:Feature; cd:locatedInRegion cd:SolentCCO. ?windProperty a cd:WindSpeed. }
  • 49. 49 Enabling Ontology-based Access to Stream RDF-Stream ... ... ( <si-1,pi-1, oi-1>, ti-1 ), ( <si, pi, oi>, ti ), ( <si+1,pi+1, oi+1>, ti+1 ), ... ... Example: “provide me with the wind speed observations over the last minute in the Solent Region ” cd:Observation cd:observationResult xsd:double STREAM <http://www.semsorgrid4env.eu/ccometeo.srdf> ... ... ( <ssg4e:Obs1,rdf:type, cd:Observation>, ti ), ( <ssg4e:Obs1,cd:observationResult,”34.5”>, ti ), ( <ssg4e:Obs2,rdf:type, cd:Observation>, ti+1 ), ( <ssg4e:Obs2,cd:observationResult,”20.3”>, ti+1 ), ... ...
  • 50. 50 Query translation envdata_westbay Feature envdata_chesil v envdata_milford v envdata_hornsea v envdata_rhylflats v Timestamp: long Observation hasObservation Result Mapping Hs : float Lon: float Lat: float observedProperty xsd:float locatedIn Region Region WaveHeightProperty SPARQL stream PREFIX cd: <http://www.semsorgrid4env.eu/ontologies/CoastalDefences.owl#> PREFIX sb: <http://www.w3.org/2009/SSN-XG/Ontologies/SensorBasis.owl#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?waveheight ?wavets ?lat ?lon FROM STREAM <http://www.semsorgrid4env/ccometeo.srdf> WHERE { ?WaveObs a cd:Observation; cd:observationResult ?waveheight; cd:observationResultTime ?wavets; cd:observationResultLatitude ?lat; cd:observationResultLongitude ?lon; cd:observedProperty ?waveProperty; cd:featureOfInterest ?waveFeature. ?waveFeature a cd:Feature; cd:locatedInRegion cd:SouthEastEnglandCCO. ?waveProperty a cd:WaveHeight. } SNEEql (SELECT Lon,timestamp,Hs,Lat FROM envdata_rhylflats) UNION (SELECT Lon,timestamp,Hs,Lat FROM envdata_hornsea) UNION (SELECT Lon,timestamp,Hs,Lat FROM envdata_milford) UNION (SELECT Lon,timestamp,Hs,Lat FROM envdata_chesil) UNION (SELECT Lon,timestamp,Hs,Lat FROM envdata_perranporth) UNION (SELECT Lon,timestamp,Hs,Lat FROM envdata_westbay) UNION (SELECT Lon,timestamp,Hs,Lat FROM envdata_pevenseybay)
  • 51. 51 Mapping declaration 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"
  • 52. 52 Data Translation ssn:observedProperty ssn:Observation http://swissex.ch/data# Wan7/WindSpeed/Observation{timed} ssn:observationResult wan7 timed: datetime PK sp_wind: float ssn:SensorOutput ssn:hasValue http://swissex.ch/data# Wan7/ WindSpeed/ ObsOutput{timed} ssn:ObservationValue qudt:numericValue http://swissex.ch/data# Wan7/WindSpeed/ObsValue{timed} xsd:decimal sp_wind ssn:Property sweetSpeed:WindSpeed
  • 53. Extention de SSN Wireless Semantic Sensor Ontology Rimel BENDADOUCHE PhD Thesis Pour mieux affirmer ses missions, le Cemagref devient Irstea www.irstea.fr Bendadouche et al; SSN 2012
  • 54. 54 Wireless Sensor Network (WSN) NEEDS AND OBJECTIVES  Adapt the WSN node behavior to the context: • • Node state Phenomena state Context: ”The context is a set of entities states or information describing an environment where an event occurs” State: ”The state is a qualitative data, which changes over time summarizing a set of information” SSN'12 12/11/2012  Enhance the lifetime and the good functioning of the network WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
  • 55. 55 What is a context ? FLOOD PHENOMENA FLOOD PHENOMENA STATE: 1. “Normal” 2. “Waiting for rise in water levels” 3. “Rise in water levels” 4. “Flood warning” NODE (ENERGY) STATE: 1. Strong Energy state 2. Average Energy state 3. Low Energy state
  • 56. 56 Wireless Sensor Network (WSN) Phenomena state Normal <weather> node sends its measures <weather> node sends nothing WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
  • 57. 57 WSN and its devices SSN'12 12/11/2012 WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
  • 58. 58 Communication: Stimulus-WSNnodeCommunication pattern SSN'12 12/11/2012 WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
  • 59. 59 Communication process SSN'12 12/11/2012 WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
  • 60. 60 State OUR EXAMPLE SSN'12 12/11/2012 WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
  • 61. 61 The use of the WSSN ontology USING TOOLS • Develop the WSSN ontology • Protégé • JESS rule engine • Derive the state from the sensor data • Simulate the WSN and its nodes behaviour • JADE Simulator WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
  • 62. Others projects Pour mieux affirmer ses missions, le Cemagref devient Irstea www.irstea.fr
  • 63. 63 Project: Sensei INTEGRATING THE PHYSICAL WITH THE DIGITAL WORLD OF THE NETWORK OF THE FUTURE • • • • Smart Cities: Transport, energy consumption etc… the EU's 7 Framework Programme January 2008  December 2010 19 partners from 11 European countries http://www.sensei-project.eu/ Zhang, Y., Meratnia, N.and Havinga, P.J.M.(2010) „Ensuring high sensor data quality through use of online outlier detection techniques‟,Int. J. Sensor Networks, Vol. 7, No. 3, pp.141–151 Bahrepour, Majid and Meratnia, Nirvana and Havinga, Paul J.M. (2010) Fast and Accurate Residential Fire Detection Using Wireless Sensor Networks. Environmental Engineering and Management Journal, 9 (2). pp. 215-221. ISSN 1582-9596
  • 64. 64 Project: KNOESIS Semantic Sensor Web http://knoesis.wright.edu/ J. Pschorr, C. Henson, H. Patni and A. Sheth Sensor Discovery on Linked Data. Kno.e.sis Center, Wright University, Dayton, USA, 2010.
  • 65. 65 Project: SPITFIRE SEMANTIC WEB INTERACTION WITH REAL OBJECTS http://spitfire-project.eu/ SmartServiceProxy aggregate semantic sensor data into representations of real-world things called Semantic Entities provide RESTful direct access to them. Not yet publicly accessible
  • 66. 66 Project: 52 North SEMANTIC WEB INTERACTION WITH REAL OBJECTS http://52north.org/ Sensor Observation Service: publication of sensor data in RDF SWEET ontology Janowicz, K. , Bröring, A., Stasch, C., Schade, S ., Everding, T., & A. Llaves (2011): A RESTful Proxy and Data Model for Linked Sensor Data. International Journal of Digital Earth, pp. 1 - 22. Arne Bröring, Patrick Maué, Krzysztof Janowicz, Daniel Nüst, and Christian Malewski . Semantically-Enabled Sensor Plug & Play for the Sensor Web Sensor Plug&Play framework Sensors 2011, 11(8), pp. 7568-7605.
  • 67. 67 Conclusion & Perspectives SSN Ontology used in several projects for publishing data sensor on the web of data… Some works has to be done: • good practices in URL definition • Vizualisation of spatio temporal data • Distributed reasoning Follows the Semantic Sensor Network Workshop at ISWC • SSN13 October 2013 Sydney • SSN12 http://knoesis.org/ssn2012/ • SSN11 http://ceur-ws.org/Vol-839/ • SSN10 http://ceur-ws.org/Vol-668/ • SSN 2009 http://ceur-ws.org/Vol-522/ • SSN 2006 http://www.ict.csiro.au/ssn06/