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A Knowledge-based Approach for Real-Time IoT Stream Annotation and Processing


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iThings 2014

Published in: Education
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A Knowledge-based Approach for Real-Time IoT Stream Annotation and Processing

  1. 1. A Knowledge-based Approach for Real-Time IoT Stream Annotation and Processing 1 Sefki Kolozali, Maria Bermundez, Daniel Puschmann, Frieder Ganz, Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom
  2. 2. 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
  3. 3. 3 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.
  4. 4. 4 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
  5. 5. 5 Real-Time Stream Annotation Framework
  6. 6. 6 Existing models - e.g. W3C SSN Ontology Ontology Link: M. Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
  7. 7. Information Models Describing a stream annotation work flow using the Stream Annotation Ontology (SAO)
  8. 8. Stream Annotation Ontology ... The SAO allows representation of aggregated stream data and temporal characteristics. It is based on the SSN Ontology and Timeline Ontology.
  9. 9. IoT Stream Processing WSN WSN WSN WSN WSN Network-enabled Devices Data MMWW streams Network-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 data Or Higher-level abstractions MMWW MMWW
  10. 10. Middleware Advance Message Queue Protocol (AMQP) enum MType { transform, forward, store } struct Message { 1: list<MType> messageTypes 2: map<string,string> data 3: 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.
  11. 11. 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.
  12. 12. 12 Data abstraction Using Symbolic Aggregate Approximation (SAX) and SensorSAX SAX Pattern (blue) with word length of 20 and a vocabulary of 10 symbols over 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. fggfffhfffffgjhghfff jfhiggfffhfffffgjhgi fggfffhfffffgjhghfff
  13. 13. Data Aggregation with SAX and its representation based on SAO @prefix sao: <> . @prefix ssn: <> . @prefix qoi: <> . @prefix tl: <> . :government a foaf:Organisation, prov: Agent . :sefki a foaf:Person, prov:Agent ; foaf:givenName "Sefki" ; foaf:mbox <> 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-A real time average speed data obtained traffic-619 . 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.
  14. 14. Evaluation Results
  15. 15. 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; 15
  16. 16. Q&A − Thank you. − EU FP7 CityPulse Project: @ictcitypulse