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Enrichment of Sensor Descriptions and Measurements Using Semantic Technologies Student: Alexandra Moraru Mentor: Prof. Dr. Dunja Mladeni Environmental Monitoring automation Traffic integration Monitoring Interoperability Building

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Enrichment of Sensor Descriptions and Measurements Using Semantic Technologies

Student: Alexandra Moraru Mentor: Prof. Dr. Dunja Mladenić

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Environmental Monitoring Traffic Monitoring Health Monitoring Industrial Process Monitoring Building Monitoring

Images source: M. Botts, G. Percivall, C.Reed, J. Davidson, OGC SWE: Overview And High Level Architecture

Interoperability

integration

automation

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Motivation

  • Understanding and managing of sensor data

– We want a way of doing sensing that can make the data available to any application that needs that specific data [1] – Challenges:

  • associate meaning to sensor data
  • computer understandable representation
  • many communities participate in sensor deployments
  • Semantic Technologies

– identified as key enabling technologies for sensor networks (W3C) – semantic enrichment can be considered as a first step

[1]John Cox, Turning the world into a sensor network, Network World, August 11, 2010.

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Contributions

  • Semantic enrichment of sensor descriptions and

measurements

– Definition of a framework – Instantiation of framework components – Examples of applications

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Part I Framework Definition

  • Introduction
  • Problem Description
  • Framework Components

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Introduction (1/2)

  • Sensor

– material or device which changes its properties according to a physical stimulus – can be attached to more complex devices – sensor nodes

  • computing and communication capabilities
  • embedded into physical objects
  • wired and wireless networks

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Introduction (2/2)

  • Internet of Things – world-wide network of heterogeneous smart
  • bjects

– sensors, actuators, RFIDs, MEMS – based on standard communication protocols – focused on establishing connectivity

  • Web of Things – integrating smart objects into the Web

– a.k.a Sensor Web, Physical Web – based on standards like HTML, XML, RSS – focused on application layer

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Problem Description (1/2)

  • Integration of sensor data from different systems
  • Provide machine understandable representation of

data

– Describe the meaning of data and the context in which it was collected

  • Environment characteristics
  • Sensor properties

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Problem Description (2/2)

  • Apply semantic technologies to sensor web

– Enriching the sensor data

  • enrichment of data generally refers to adding information
  • semantic enrichment refers to associating semantic tags

– Publishing annotated sensor data

  • enables the development of new applications
  • through standardized web services, application specific methods –

requires prior knowledge of the infrastructure used

  • following Linked Open Data (LOD) principles

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Conceptual Framework

  • Framework for semantic enrichment of sensor data

– automatizing the process enriching sensor descriptions and measurements

Data Consumers Enrichment Components Ontology Collection Semantic Repository

  • f Sensor

Data

Query End-Point Semantic Browsers Inference Engines

Sensor Descriptions and Measurements

Descriptions Enrichment Measurements preprocess and enrichment Ontology Extension

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Framework Components (1/3)

  • Sensor Descriptions and Measurements

– Sensor description refer to the metadata defining sensor characteristics – Sensor measurements – numerical values quantifying the changes of sensor properties

  • Ontology Collection

– set of ontologies necessary for describing sensor characteristics and providing context for sensor measurements.

Ontology Collection Sensor Descriptions and Measurements

Ontology Extension

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Framework Components (2/3)

  • Enrichment Components

– sensor descriptions are enriched with semantic concepts – sensor measurements are processed to generate new features which are then enriched by semantics.

  • Steps of the enrichment process

– Analysis of the sensor descriptions and measurements – Selection of ontologies – Extension of the selected ontologies with concepts specific to the domain of application – Implementation of enrichment components

Enrichment Components Ontology Collection Sensor Descriptions and Measurements

Descriptions Enrichment Measurements preprocess and enrichment Ontology Extension

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Framework Components (3/3)

  • Semantic Repository of Sensor

Data

– contains the enriched sensor descriptions and measurements.

Enrichment Components Data Consumers

Descriptions Enrichment Measurements preprocess and enrichment

Ontology Collection Semantic Repository

  • f Sensor

Data

Query End-Point Semantic Browsers Inference Engines

Sensor Descriptions and Measurements

Ontology Extension

  • Data Consumers

– query end-points – semantic browsers – Inference engines

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Part II Instantiation of Framework Components

  • Sources of Sensor Data

– Non-standardized – Standardized

  • Ontology Collection

– OWL ontologies – Cyc ontology

  • Architecture
  • Enrichment Components
  • Semantic Repository of Sensor Data

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Sources of Sensor Data (1/2)

  • Non-standardized dataset

– data collected from a sensor network for monitoring environmental conditions

  • temperature, humidity, luminance and pressure

– centralized MySQL database server,

  • both the meta-data and sensor measurements.

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Sources of Sensor Data (2/2)

  • Standardized dataset

– contains description and measurements of sensors in the area of

  • cean tides and currents
  • air temperature, water temperature, water level, currents, wind, air pressure,

salinity

  • sensor description (SensorML)
  • sensor measurements (O&M)

– 751 sensor nodes, 1379 sensors measuring 14 types of properties – downloaded and processed offline

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Ontology Collection (1/3)

  • OWL ontologies

– W3C Semantic Sensor Network ontology (infrastructure)

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Ontology Collection (2/3)

  • Additional (external) OWL ontologies

– Basic GeoWGS84 Vocabulary, provides namespaces for representing coordinates – Geonames, provides geographical names in RDF representation

  • findNearbyPlacename web service

– W3C time ontology

  • defining time intervals for sensor measurements

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Ontology Collection (3/3)

  • Research Cyc

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Architecture

SensorML descriptios OWL Ontologies SSN Ontology

RDF descriptions

  • f sensors

SPARQL Endpoint SESAME

ResearchCyc

JAXB API Jena Framework

OntoGen

O&M measurements

OntoGenUI OntoGenUI

MySql Database JDBC

Pubby Data Publishing

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Enrichment Components (1/4)

  • Enrichment of sensor data

– Input: sensor data + ontologies – manually creating rules

  • extract the information from the datasets
  • attach the corresponding semantic concepts

– for the non-standardized dataset -> OWL ontologies – for the standardized dataset -> both collections of ontologies, separately – Output: RDF representation of the original dataset

  • annotated with semantic concepts from the ontology collection

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Enrichment Components (2/4)

  • Non-standardized dataset – Database

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Enrichment Components (3/4)

  • Standardized dataset

– platforms described in SensorML -> instances of Platform. – the networks to which these platforms belong to -> instances of Deployment. – the platforms components -> instances of SensingDevice. – the observed properties of sensing devices -> instances of the subclasses extending Property

  • related to the sensed domain by the using the relation isPropertyOf and the

subclasses extending FeatureOfInterest.

– the geographical locations of the platforms, given by latitude and longitude coordinates -> lat and long relations from the GeoWGS84 vocabulary.

  • findNearbyPlaceName web service (fro GeoNames) -> finds the name of the

closest populated place to the platform location

– Computation based enrichment of measurements

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Enrichment Components (4/4)

  • Standardized dataset – Enrichment of measurements

– data mining tool for processing the sensor measurements and for extracting knowledge from the raw measurements – enriched measurements annotated according to the collection of OWL

  • ntologies
  • exported in RDF format

– permits the user to take advantage of knowledge extracted from the raw measurements

  • Features generated from the raw sensor measurements

– wind and sea conditions for sailors according to the Beaufort scale

  • 26 nominal values, such as: Calm, Flat, Fresh Breeze, etc.

– migraines caused by atmospheric pressure according to pressure values published in medical studies

  • risk of headache: NoHeadache, Headache and HighHeadache
  • time of day intervals: early morning, late evening, etc.

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Semantic Repository of Sensor Data

  • Implementation of Semantic Repository of Sensor Data

– Sesame, framework for processing RDF data

  • store, parse, query, perform inference on RDF data
  • Java API (storing and updating the enriched sensor data)
  • for each dataset a separate repository

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Part III Applications

  • Sensor search
  • Data Publishing

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Sensor Search (1/3)

  • Finding specific sensors, from which one could be interested in

gathering data

  • Searching directly through sensor measurements for explicit

values or different events.

  • Formulating queries for retrieving the results we need

– SPARQL end-points

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Sensor Search (2/3)

  • Which are the sensors measuring temperature located in the

Vič region of the city of Ljubljana?

  • http://localhost:8080/openrdf-workbench/repositories/sensorlab/query

SELECT DISTINCT ?s WHERE { ?sn ssn:hasSubSystem ?s. ?s ssn:observes <http://localhost:8080/pubby- sensors/vocab/phenomenas/air_temperature>. ?sn ssn:onPlatform ?p. ?p foaf:based_near <http://sws.geonames.org/3187818/>.}

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Sensor Search (3/3)

  • Which are the locations with risk of headache on early

morning?

SELECT DISTINCT ?sensor ?location where{

?platform ssn:attachedSystem ?sensor. ?platform geo:location ?loc. ?loc foaf:based_near ?place. ?place gn:name ?location. ?sensor ssn:madeObservation ?obs. ?obs ssn:observationResult ?res. ?obs time-entry:startsOrDuring tmInt:EarlyMorning. ?res ssn:hasValue obsVal:Headache. } http://localhost:8080/openrdf-workbench/repositories/Sensor140111/query 29

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Data Publishing (1/2)

  • Publishing methods

– standardized web services – OGC’s SOS – application specific: Pachube, Sensorpedia – Linked Sensor Data

  • Linked Data

– method of exposing, sharing, and connecting data via dereferenceable URIs on the Web.

  • URI for the real-world object itself.
  • URI for a related information resource that describes the real-world
  • bject and has an HTML representation.
  • URI for a related information resource that describes the real-world
  • bject and has an RDF/XML representation.

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Data Publishing (2/2)

  • Pubby

– open source tool that provides Linked Data interfaces to SPARQL end-points – rewrite URIs found in the RDF dataset into Pubby server’s namespace – simple HTML interface about each resource. – http://localhost:8080/pubby-sensors/

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Conclusions

  • Summary

– Definition of a framework for semantic enrichment of sensor data – Instantiation of the framework components – Discussion about possible applications

  • Lessons Learned

– enriching standardized vs. non-standardized dataset – domain ontologies and application ontologies – representation of physical and virtual sensors

  • Sensors as physical entities
  • Sensors as virtual entities
  • Sensors as either physical or virtual entitites

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Future Work

  • Development of new enrichment components

– Static and stream data

  • Enrichment at sensor node level

– serving semantic descriptions from the nodes

  • Represent time and space on higher semantic levels

– stRDF/stSPARQL, GeoSPARQL, EP-SPARQL – URI generation

  • Integration with complex events

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Bibliography

  • Moraru, A.; Vučnik, M.; Porcius M.; Fortuna, C.; Mladenić, D. Exposing Real

World Information for the Web of Things. In Proceedings of the 8th International Workshop on Information Integration on the Web, in conjunction with 20th International World Wide Web Conference (2011).

  • Moraru, A.; Fotuna C.; Mladenić, D. A System for Publishing Sensor Data on

the Semantic Web. In Proceeding of 33rd International Conference on Information Technology Interfaces (June 27-30 2011).

  • Dali, L; Moraru, A.; Mladenić, D. Using Personalized PageRank for Keyword

Based Sensor Retrieval. In Proceedings of 4th International Semantic Search Workshop, located at the 20th International World Wide Web Conference, (March 29 2011).

  • Moraru, A.; Pesko, M.; Porcius, M.; Fortuna, C.; Mladenić, D. Using Machine

Learning on Sensor Data. Journal of Computing and Information Technology, 18, 4 1-7 (2010).

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