ingredients for semantic sensor networks

Ingredients for the Semantic Sensor Web

Jožef Stefan Institute

Ljubljana, Slovenia

September 23rd 2011

Oscar CorchoFacultad de Informática, Universidad Politécnica de Madrid

Campus de Montegancedo sn, 28660 Boadilla del Monte, Madrid

http://www.oeg-upm.net

[email protected]

Phone: 34.91.3366605

Fax: 34.91.3524819

Index

PART I. Motivation

From Sensor Networks…

… to the Sensor Web / Internet of Things…

… to Semantic Sensor Web and Linked Stream/Sensor Data

Sensor Networks

• Increasing availability of cheap, robust, deployable sensors as ubiquitous information sources

Source: Antonis Deligiannakis

An example: SmartCities

4

Santander

Parking sensor nodes

Environmentalsensor nodes

Sensor Networks and Streaming Data

5

• Streaming Data

(t9, a1, a2, ... , an)

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

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

...

...

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

...

...

Streaming Data

Window [t7 - t9]

• Continuously appended data

• Potentially infinite

• Time-stamped tuples

• Continuous queries

• Latest used in queries

• Time and tuple-based windows

• Cheap, Noisy, Unreliable (depends)

• Low computational, power resources, storage

• Distributed query execution

• Routing, OptimizationQuery

Enabling Semantic Integration of Streaming Data Sources

• Sensor Networks

Who are the end users of sensor networks?

Source: Dave de Roure

The climate change expert, or a simple citizen

Not only environmental sensors, but many others…

7

Weather Sensors

Camera SensorsSatellite Sensors

GPS SensorsSensor Dataset

Source: H Patni, C Henson, A Sheth

How do we make these sensors more accessible?

8Source: SemsorGrid4Env consortium

9

The Sensor Web (related to Internet of Things)

• Universal, web-based access to sensor data

• Some sensor network properties:• Networked• Mostly wireless• Each network with some

kind of authority and administration

• Sometimes noisy

Source: Adapted from Alan Smeaton’s invited talk at ESWC2009

Should we care as computer scientists?

• They are mostly useful for environmental scientists, physicists, geographers, seismologists, … [continue for more than 100 disciplines]• Hence interesting for those computer scientists interested on

helping these users… We are many ;-)

• But they are also interesting for “pure” computer scientists• They address an important set of “grand challenge”

Computer Science issues including: • Heterogeneity• Scale• Scalability• Autonomic behaviour• Persistence, evolution• Deployment challenges

• MobilitySource: Dave de Roure

A semantic perspective on these challenges

• Sensor data querying and (pre-)processing• Data heterogeneity• Data quality• New inference capabilities required to deal with sensor

information

• Sensor data model representation and management• For data publication, integration and discovery• Bridging between sensor data and ontological

representations for data integration• Ontologies: Observations and measurements, time series,

etc.• Event models

• User interaction with sensor data

Vision (after some iterations, and more to come)

12

Networked Knowledge

Before 2010 2010-2015 2015-2020 Beyond 2020

Today Incremental Incremental-Visionary VisionaryInteroperability 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 transparencyIdentity & 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

Semantic Sensor Web / Linked Stream-Sensor Data (LSD)

• A representation of sensor/stream data following the standards of Linked Data

But what is Linked Data?

What is Linked Data?

14

• 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

15

The four principles (Tim Berners Lee, 2006)

1. Use URIs as names for things

2. Use HTTP URIs so that people can look up those names.

3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL)

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

• http://www.w3.org/DesignIssues/LinkedData.html

http://www.ted.com/talks/tim_berners_lee_on_the_next_web.htmlhttp://www.ted.com/talks/tim_berners_lee_on_the_next_web.html

http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html

Semantic Sensor Web / Linked Stream-Sensor Data (LSD)

• A representation of sensor/stream data following the standards of Linked Data• Adding semantics allows the search and exploration of sensor

data without any prior knowledge of the data source• Using the principles of Linked Data facilitates the integration of

stream data to the increasing number of Linked Data collections

• Early references…• Amit Sheth, Cory Henson, and Satya Sahoo, "Semantic Sensor

Web," IEEE Internet Computing, July/August 2008, p. 78-83• Sequeda J, Corcho O. Linked Stream Data: A Position Paper.

Proceedings of the 2nd International Workshop on Semantic Sensor Networks, SSN 09

• 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, SSN 10

Let’s check some examples

• Meteorological data in Spain: automatic weather stations• http://aemet.linkeddata.es/• Paper under open review at the Semantic Web Journal

• http://www.semantic-web-journal.net/content/transforming-meteorological-data-linked-data

• 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

http://aemet.linkeddata.es/

http://sensors.ijs.si/



http://webgis1.geodata.soton.ac.uk/flood.html

http://webgis1.geodata.soton.ac.uk/flood.html

AEMET Linked Data

18

JSI Sensors

19

Coastal Channel Observatory and other sources

20Sensors, Mappings and Queries

• Work with Flood environmental sensor data.• SemSorGrid4Env project www.semsorgrid4env.eu.

http://www.semsorgrid4env.eu/

PART II

• How to create, publish and consume Linked Stream Data

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

Several efforts since approx. 2005State of the art on sensor network ontologies in the report below

In 2009, a W3C incubator group was started, which has just finishedLots of good people thereFinal report: http://www.w3.org/2005/Incubator/ssn/XGR-ssn-

20110628/Ontology: http://purl.oclc.org/NET/ssnx/ssnA good number of internal and external references to SSN

Ontologyhttp://www.w3.org/2005/Incubator/ssn/wiki/

Tagged_BibliographySSN Ontology paper submitted to Journal of Web Semantics

SSN ontologies. History

Skeleton

Device

Deployment

PlatformSite

System

Process

ConstraintBlockMeasuringCapability

OperatingRestriction

Data

Overview of the SSN ontology modules

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

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. Sensor and environmental properties

A usage example

SWEET

Service

Coastal Defences

Ordnance Survey

Additional Regions

Role

DOLCE UltraLite

Schema

FOAF

Upper

External

SSG4Env

infrastructure

Flood domain

27

SSN

AEMET Ontology Network

• 83 classes• 102 object properties• 80 datatype properties• 19 instances• SROIQ(D)

Good practices in URI Definition

Sorry, no clear practices yet…

Good practices in URI Definition

• We have to identify…• Sensors• Features of interest• Properties• Observations

• Debate between being observation or sensor-centric• Observation-centric seems to be the winner• For some details of sensor-centric, check [Sequeda and

Corcho, 2009]

Semantically Integrating Streaming and Stored Data

Queries to Sensor/Stream Data

SNEEqlRSTREAM SELECT id, speed, direction FROM wind[NOW];

Streaming SPARQLPREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>SELECT ?sensor ?speed ?directionFROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MSWHERE { ?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-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 { …

33

SPARQL-STR v1


SELECT ?waveheight

FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>

[FROM NOW -10 MINUTES TO NOW STEP 1 MINUTE]

WHERE {

?WaveObs a sea:WaveHeightObservation;

sea:hasValue ?waveheight; }

Query

translation

Query ProcessingC

lient

Stream-to-Ontology

mappings

SPARQLStream

[tuples]

Sensor Network

Data

translation[triples]

SNEEql

conceptmap-def WaveHeightMeasurement

virtualStream <http://ssg4env.eu/Readings.srdf>

uri-as concat('ssg4env:WaveSM_',

wavesamples.sensorid,wavesamples.ts)

attributemap-def hasValue

operation constant

has-column wavesamples.measured

dbrelationmap-def isProducedBy

toConcept Sensor

joins-via condition equals

has-column sensors.sensorid

has-column wavesamples.sensorid

conceptmap-def Sensor

uri-as concat('ssg4env:Sensor_',sensors.sensorid)

attributemap-def hasSensorid

operation constant

has-column sensors.sensorid

S2O Mappings

SELECT measured FROM wavesamples [NOW -10 MIN]

Source: Enabling Ontology-based Access to Streaming Data Sources. Calbimonte JP, Corcho O, Gray AJG. ISWC 2010

SPARQL-STR v2

Query

translation

Query Evaluator

Clie

nt

Stream-to-Ontology

Mappings (R2RML)

SPARQLStream (Og)

[tuples]

Stream Engine (S3)

Ontology-based Streaming Data Access Service

Relational DB (S2)

Sensor Network (S1)

RDF Store (Sm)

SPARQLStream algebra(S1 S2 Sm)

Data

translation

q

[triples]

SNEEql, GSN API

GSN

Source: PlanetData deliverable D1.1 (to be published in Sep 30th 2011) www.planetdata.eu

Creating Mappings


wan7

timed: datetime PK

sp_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

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"

http://www.w3.org/2001/sw/rdb2rdf/r2rml/

38Red de Ontologías para el Camino de Santiago

Query Transformation Semantics

• Conjunctive Queries

• Mappingconjunctive

query

expression

over streaming sources

Algebra expressions transformed to GSN API


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

Algebra construction


timed,

sp_wind

π

ω

σ sp_wind>10

5 Hour

wan7

windsensor1

windsensor2

Static optimization


timed,

sp_wind

π

ω

σ sp_wind>10

5 Hour

wan7

timed,

windvalue

π

ω

σ windvalue>10

5 Hour

windsensor1

timed,

windvalue

π

ω

σ windvalue>10

5 Hour

windsensor2

Data Modeling: stRDF

– stRDF– Temporal/spatial data are represented by linear constraints,

representing as literals of type strdf:semiLinearPointSet.

– OGC simple feature geometries (points, polylines, polygons etc.) using the Well-known Text representation

2nd Year Review Meeting - Brussels, 16-17 Nov. 2010 43

floodInstances:ModelledFloodIngressDataset

rdf:type info:Dataset;

rdfs:label "Modelled flood ingress Dataset" ;

time:hasTemporalExtent "[NOW,NOW+12]"^^RegistryOntology:TemporalInterval;

Services:coversRegion AdditionalRegions:CoastalDefencePartnershipModelledArea;

Services:includesFeatureType CoastalDefences:FloodPlain ;

Services:includesPropertyType CoastalDefences:WaterDepth.

AdditionalRegions:CoastalDefencePartnershipModelledArea

rdf:type space:Region;

Services:hasSpatialExtent "POLYGON((625145.2823357487 5624227.2548582135, 625145.2823357487

5637255.203057151, 647383.6564885917 5637255.203057151, 647383.6564885917 5624227.2548582135,

625145.2823357487 5624227.2548582135))"^^RegistryOntology:WKT.

Source: Our NKUA partners at SemsorGrid4Env

Querying: stSPARQL

Find all WMS services with FOI flood plain that cover the Coastal Defence Partnership modelled area and provide valid information for the next 12 hours

select distinct ?ENDPOINT where { ?SERVICE rdf:type Services:WebService . ?SERVICE Services:hasEndpointReference ?ENDPOINT . ?SERVICE Services:hasServiceType Services:WMS . ?SERVICE Services:hasDataset ?DATASET .

?DATASET Services:includesFeatureType CoastalDefences:FloodPlain. ?DATASET time:hasTemporalExtent ?TIME .

filter(?TIME contains “[NOW,NOW+12]"^^RegistryOntology:TemporalInterval) . ?DATASET Services:coversRegion ?SERVICEREGION . ?SERVICEREGION Services:hasSpatialExtent ?SERVICEREGIONGEO . AdditionalRegions:CoastalDefencePartnershipModelledArea Services:hasSpatialExtent ?COSTALGEO . filter(?SERVICEREGIONGEO contains ?COSTALGEO) }

2nd Year Review Meeting - Brussels, 16-17 Nov. 2010 44Source: Our NKUA partners at SemsorGrid4Env

Implementation: STRABON

• Support for stRDF and SPARQL, plus• Topological operators in spatial filters

• DISJOINT, TOUCH, EQUALS, CONTAINS, COVERS, COVERED BY, OVERLAP

• Construct Spatial Geometries• e.g. ?geo1 union ?geo2

• Projection operation• e.g. ?geo[1,2]

• Rename operator• Conversion Functions for exporting geometries:

• e.g. ToWKT(?geo) AS ?geoAsWKT

• Library that returns SPARQL results as a KML document

45Source: Our NKUA partners at SemsorGrid4Env

Sensor High-level API

Source: Kevin Page and rest of Southampton’s team at SemsorGrid4Env

API definition

Source: Kevin Page and rest of Southampton’s team at SemsorGrid4Env

SwissEx


• 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• 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 observations

Sensor metadata

Getting things done

• Transformed wiki metadata to SSN instances in RDF• Generated R2RML mappings for all sensors• Implementation of Ontology-based querying over

GSN• Fronting GSN with SPARQL-Stream queries• Numbers:

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


Sensor Metadata


station

location

model

sensors

properties

Sensor Data: Observations


Heterogeneity

Integration

SPARQL-STR + GSN

Lessons Learned

• High-level (part I)• 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

• Practical lessons learned (part II)• 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

Ingredients for the Semantic Sensor Web

Jožef Stefan Institute

Ljubljana, Slovenia

September 23rd 2011

Oscar Corcho

Acknowledgments: all those identified in slides + the SemsorGrid4Env team (Jean Paul Calbimonte, 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)

ingredients for semantic sensor networks

Technology

appended data

motivationfrom sensor

theendusers of sensor

sensor web internet

managementfor data publication

linked streamsensor

streaming data5 streaming

current web