Publishing consuming Linked Sensor Data meetup Cuenca

1st Latin American Linked Data Meetup
Cuenca, Ecuador

Publishing and Consuming
Linked Sensor Data

Jean-Paul Calbimonte

Ontology Engineering Group.
Facultad de Informática, Universidad Politécnica de Madrid.

jp.calbimonte@upm.es

Date: 01/12/2011

Linked Sensor Data 101

Motivation

Ingredients

Linked Sensor Data

Generate

Consume

2

Motivation

From Sensor Networks…

… to the Sensor Web/
Internet of Things…

… to Semantic Sensor Web and …
Linked Sensor Data

3

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

Background – Querying Relational Data Streams

Streaming Data
STREAM
Aurora/Borealis
Cougar Query engines

e1 WINDOW [tnow-2 TO tnow] SLIDE 1 TinyDB
SNEE

e1 e2 CQL
SNEEql Query languages
TinyQL
e1 e2 e3

Transform infinite sequence
e1 e2 e3 e4
of tuples to bounded bag

t t+1 t+2 t+3 t+4 t+5

SELECT attribute FROM stream [NOW -10 MIN]

...
5

Sensor Networks

Source: Antonis Deligiannakis

An example: SmartCities

Environmental sensors

Parking sensors

7 SmartSantander Project

Who are the end users of Sensor Networks?

The climate change expert, or a simple citizen

Source: Dave de Roure

Not only environmental, but many others…

Weather Sensors
GPS Sensors

Sensor Dataset

Satellite Sensors Camera Sensors

Source: H Patni, C Henson, A Sheth 9

The Sensor Web

Universal, web-based access to sensor data

Source: Adapted from Alan Smeaton’s invited talk 10 ESWC2009
at

Make sensors more accessible?

Source: SemsorGrid4Env consortium 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

Data from the Web

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

wave data Environmental
forecasts defenses

Sensors, Mappings and Queries 13

Semantic Sensor Web / Linked Sensor Data (LSD)

A representation of sensor data following
the standards of Linked Data

But what is Linked Data?

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

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
16

Linked Open Data

2011

http://richard.cyganiak.de/2007/10/lod/
17

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.

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.

19

AEMET Linked Data

Sensors

Observations

20

Coastal Channel Observatory and other sources

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

Wind Speed

Wave Height

Tidal Observations

22

Motivation

Flood risk alert: Wave,
South East Real-time Wind, RDF
England data Tide
RDDF

Ontology
Emergency
Meteorological RDF
forecasts
planner
Flood defences
data RDF

...
... Other sources
...

• Detect conditions likely to cause a flood
• Present data model in terms of the user domain: e.g. Flood risk assessment

Example:
• “provide me with the wind speed observations average over the last minute in
the Solent region, if it is higher than the average of the last 2 to 3 hours”
SPARQL

Enabling Ontology-based Access to Streaming Data Sources 23

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/

Sensor Metadata

station

location

sensors

model

properties


Sensor Metadata

• What properties are measured

• Which sensors available

• Where are they located

• How are they configured

• Who is responsible


Sensor Data: Observations

Heterogeneity

Integration


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

SSN ontology modules

System OperatingRestriction
Deployment

Device Process
PlatformSite

Data

Skeleton

MeasuringCapability ConstraintBlock

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

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

A model to bind them all

• W3C 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

32

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"].

33

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

34

Usage: SSN & Domain Ontologies

Upper
DOLCE SWEET
UltraLite

SSG4Env
infrastructure SSN

Schema

Service

External
FOAF Ordnance
Survey

Flood domain
Role Coastal Additional
Defences Regions

35

AEMET Ontology Network

• 83 classes
• 102 object properties
• 80 datatype properties
• 19 instances

Additional domain ontologies

Examples: AWS, qu, Sweet

• http://www.w3.org/2005/Incubator/ssn/ssnx/meteo/
aws
Observed Properties

• http://www.w3.org/2005/Incubator/ssn/ssnx/qu/qu
Features of Interest

• http://sweet.jpl.nasa.gov/ Types of Sensors

Units of Measurement

Time

37

Good practices in URI Definition

Sorry, no clear
practices yet…

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

Sensor High-level API

Source: K. Page & Southampton’s team at SemsorGrid4Env

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
• ...

45

Where is the Data?

GSN server instance

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

timed sp_wind
1 3.4
Queries
2 5.6
3 11.2
4 1.2
5 3.1
.. …

SELECT sp_wind FROM wan7 WHERE sp_wind >10

SELECT sp_wind FROM wan7 [NOW -5 HOUR] WHERE sp_wind >10
I want SPARQL!

46

Where is the Data?

GSN server instance

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

Mappings

ssn:Observation

47

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

48

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"

49

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
SPARQL-Stream
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 { …
50

Query translation

SELECT ?waveheight
FROM STREAM <www.ssg4env.eu/SensorReadings.srdf>
[NOW – 5 HOUR TO NOW]
WHERE {
?WaveObs a ssn:ObservationValue;
qudt:numericalValue ?waveheight;
FILTER (?waveheight>10) }


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

?

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


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

Querying the Observations
SELECT ?waveheight
[NOW -10 MINUTES TO NOW STEP 1 MINUTE]
WHERE {
?WaveObs a sea:WaveHeightObservation;
sea:hasValue ?waveheight; }
http://montblanc.slf.ch :22001/ multidata ?vs [0]= wan
Query
field [0]= sp_wind
translation GSN
SPARQLStream API
Client

Mappings Query
Processing
Sensor
Network

[tuples]
Data
[triples] translation

R2RML Mappings Query processing engines

57

Lessons Learned

• High-level
• 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
• 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

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

Thanks!
Acknowledgments: all those identified in slides, especially those working in LSD at OEG:
Oscar Corcho, Raúl García-Castro, Freddy Priyatna + 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

60

Publishing consuming Linked Sensor Data meetup Cuenca

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