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iThings 2014
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A Knowledge-based Approach for Real-Time IoT Stream Annotation and Processing
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Sefki Kolozali, Maria Bermundez, Daniel Puschmann, Frieder Ganz, Payam Barnaghi
Institute for Communication Systems (ICS)
University of Surrey
Guildford, United Kingdom
Smart Cities and Real-Time IoT Streams
− Data in smart cities is collected by sensor devices and also crowed sensing sources.
− The data is time and location dependent.− It can be noisy and the quality can vary. − It is continuous - streaming data
− Semantic annotation of data will help to describe:− provenance − spatial− temporal− thematic
Attributes of the data
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The main objective
• to develop a framework in the scope of the CityPulse project for real-time IoT stream annotation that employs a knowledge-based approach to represent data streams and to support mashups.
• to develop an information model to represent abstract concepts and quality related attributes of IoT stream data.
• to enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data.
http://www.ict-citypulse.eu
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The Key issues
• Virtualisation: Semantic annotation of heterogeneous data for automated discovery and knowledge-based processing• Heterogeneity
• Interoperability
• Aggregation and Abstraction: Large-scale data analytics• Data size
• Communication in distributed systems: exchange messages among different components• Time
• Space
• Synchronisation
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Real-Time Stream Annotation Framework
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Existing models - e.g. W3C SSN Ontology
Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
M. Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
Information Models
Describing a stream annotation work flow using the Stream Annotation Ontology (SAO)
Stream Annotation Ontology
.
.
.
The SAO allows representation of aggregated stream data and temporal characteristics. It is based on the SSN Ontology and Timeline Ontology.
IoT Stream Processing
WSNWSN
WSNWSN
WSNWSN
WSNWSN
WSNWSN
Network-enabled DevicesNetwork-enabled Devices
Network-enabled DevicesNetwork-enabled Devices
Network services/storage and processing
units
Data/service access at application level
Data collections and processing within the
networks
Query/access
to raw dataOr
Higher-level abstractions
MWMW
MWMW
MWMWData streamsData
streams
Middleware
Advance Message Queue Protocol (AMQP)
enum MType {transform,forward,store
}struct Message {
1: list<MType> messageTypes2: map<string,string> data3: map<string,string> metadata
}
• A publish/subscribe mechanism which decouples time, space and synchronisation.
• The message delivery logic lies with the message broker, decoupling it from the application layer.
Use Case Scenario- Traffic Scenario, Aarhus, DK
A visual representation of geographical coordinates on Google Map for a pair of road traffic sensors provided by city of Aarhus, Denmark.
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Data abstraction
Using Symbolic Aggregate Approximation (SAX) and SensorSAX
SAX Pattern (blue) with word length of 20 and a vocabulary of 10 symbolsover the original sensor time-series data (green)
Source: P. Barnaghi, F. Ganz, C. Henson, A. Sheth, "Computing Perception from Sensor Data", in Proc. of the IEEE Sensors 2012, Oct. 2012.
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Data Aggregation with SAX and its representation based on SAO
@prefix sao: <http://example.com#> .@prefix ssn: <http://purl.oclc.org/NET/ssnx/ssn#> .@prefix qoi: <http://example.com/QoSQoI.owl#> .@prefix tl: <http://purl.org/NET/c4dm/timeline.owl#> .
:government a foaf:Organisation, prov: Agent .:sefki a foaf:Person, prov:Agent ;
foaf:givenName "Sefki" ;foaf:mbox <mailto:[email protected]>prov:actedonBehalfOf :ccsrSurrey ; .
:sensorRec1 a sao:StreamData, ssn:SensorObservation ;prov: wasAttributedTo :government .
:sensorRec2 a sao:StreamData, ssn:SensorObservation ;prov: wasAttributedTo :government .
:traffic-sensor-recording-619 a sao:StreamEvent ; prov:used [ a sensorRec1; sensorRec2] ; sao:time [a tl:Interval; tl:at "2014-02-13T08:25:00"^^xsd:dateTime; tl:duration "PT15H30M"^^xsd:duration; ] ; prov:wasAsscoatedWith :sefki ; .:freshness-traffic-619 a qoi:Freshness ;
qoi:value "2014-02-13T08:25:00"^^xsd:dateTime .:sax_AverageSpeedSample a SymbolicAggregateApproximation;
rdfs:label "The sax representation of the traffic sensor recording obtained from Aarhus City.";sao:value "bbbbacdd";sao:alphabetsize "4"^^xsd:int ;sao:segmentsize "8"^^xsd:int ;prov:wasGeneratedBy traffic-sensor-recording-619; qoi:hasQoI freshness-traffic-619 .A real time average speed data obtained
from a pair of sensor points is mapped into SAX word, ”bbbbacdd”, with the segment size of “8” and alphabet size of “4” for 176 samples.
A excerpt from an RDF data annotated for a set of sensor recordings based on Stream Annotation Ontology.
Evaluation Results
In Conclusion
− We have developed a semantic model for the data streams in a smart city framework.
− The main advantages are providing an interoperable and machine-interpretable format for exchanging the data.
− The model can describe thematic, spatial, and temporal attributes of the streams and also the provenance data.
− It uses concepts from SSNO and ProvO. − We have also developed a message broker, wrapper (for restful services)
and a middleware to represent the data. − We also integrated it with a data abstraction method that we had developed
in our previous work.
− Future work: − We need to integrate this work with higher-level query mechanisms;− To integrate with our IoT data discovery and selection method;− Evaluate large-scale annotated data stream and query/access efficiency;
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Q&A
− Thank you.
− EU FP7 CityPulse Project:
http://www.ict-citypulse.eu/
@ictcitypulse