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IoT Stream: A Lightweight Ontology for Internet of Things Data Streams (GIoTS 2019)
- 1. IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams T. Elsaleh, M. Bermudez-Edo, S. Enshaeifar, S. T. Acton, R. Rezvani, P. Barnaghi IoT-Stream
- 2. Outline • IoT Data • Analogy • Annotation • Interoperability • Previous works • Our approach • Core model • Linked models • Example (Instance) • Use cases • Application scenario • To conclude
- 3. IoT Data • IoT data is often captured by various sources and is represented in different forms • Heterogeneity of the data and interoperability issues between different sources and platforms • a common challenge in creating large-scale IoT data analytics services and applications • To solve the heterogeneity of the data • most of the IoT solutions are opting to apply semantics • Semantic annotations are regarded as “heavy”
- 4. Analogy • Like streams of water • Branches into other streams which originate from or feed to one or more streams • All which are destined in a (data) lake or sea • Other streams created can be a result of processing • Analysis can be applied to detect changes, abnormalities, patterns of interest, or events
- 5. Annotation • Important to provide some form of metadata to describe • Provide them in a consumable format • Rather than Extract, transform, load (ETL) • Do we need to provide descriptions on the atomic level? • Sometimes yes, sometimes no • YES… if metadata frequently changes throughout the lifetime of a stream • NO… If it rarely changes
- 6. Interoperability • To be able to share, need to consider interoperability • Syntactic – data structure and format • Semantic – vocabulary • How to achieve? Either… • Develop AI that can interpret ANY data structure, format or vocabulary • Still needs a learning mechanism • Take practical steps onto consideration • Linked open data practices • Good ontologies guide • Popular references
- 7. Previous works • RDF Stream Processing • Abstract model for representing RDF streams on the atomic level • Introduces SPARQL extensions • Do all data points need to be annotated and stored in a triple store? • Stream Annotation Ontology • Extends SSN to address sensor data streams • Employs a class taxonomy for stream analysis techniques • Good for high granularity
- 8. Our Approach • Provide options for granularity of metadata. Can be: • Atomic • Window • Vector string • Pointer to raw data source (via an IoT service) • Analysis techniques and methods descriptions are simplified • Vector string • Use pointer to IoT service to interpret analysis techniques used • Extend and link to popular ontologies
- 9. Core Model • IoT Stream • Defines the lifetime • StreamObservation • Observation over a data point or window • Analytics • Techniques applied to the stream • Event • Detection of a state from a stream
- 10. Linked Models • IoT streams are generated by sensors • Reflect real word events • Location, phenomena, units of measure • Provided by data producers • Services, quality assessment derivedFrom belongsTo detectedFrom analysedBy sosa:madeBySensor sosa:madeObservation qoi:hasQuality iot-lite:hasQuantityKind iot-lite:hasUnit geo:location generatedBy providedBy IotStream Analytics Event StreamObservation iot-lite:Service geo:Point qu:Unit qu:QuantityKind sosa:Sensor qoi:Quality sosa:Observation
- 11. Example Analytics aarhus-iot:0004A30-sax-hum-01-sax ------------------- methods outlier, sax] parameters windowStart 2018-01-13T02:23:45:152Z windowEnd 2018-01-13T06:23:45:152Z IotStream aarhus-iot:0004A30-raw-hum-01 ----------------------------- streamStart 2018-01-12T02:23:45:152Z Event aarhus-iot:0004A30-ambient-1ebc2 --------------------- label condensation windowStart 2018-01-13T02:23:45:152Z windowEnd 2018-01-13T02:23:45:152Z StreamObservation aarhus-iot:0004A30-raw-hum-01-s1 ------------------------------ sosa:hasSimpleResult 60 sosa:resultTime 2018-01-12T02:23:45:152Z sosa:Sensor aarhus-iot:76 qu:QuantityKind aarhus-iot:humidty qu:Unit aarhus-iot:percentage geo:Point aarhus-iot:h-kpmans-pl-2-8000 ---------------------------- lat 56.152913 long 10.214597 alt 6 iot-lite:relativeLocation "http://www.geonames.org/2624652" iot-lite:Service aarhus-iot:0004A30-raw-hum-01 ----------------------- endpoint http://iot-crawler.eu/aarhus/sensor/76 interfaceType RESTful interfaceDescription http://iot-crawler.eu/aarhus/sensor/76/wadl qoi:Frequency aarhus-iot:0004A30-hum-01-freq --------------------------- value 0.116 IotStream aarhus-iot:0004A30-sax-hum-01 ----------------------------- streamStart 2018-01-13T00:00:00:000Z belongsTo detectedFrom analysedBy iot-lite:hasQuantityKind iot-lite:hasUnit PREFIX aarhus-iot = http://iot-crawler.ee.surrey.ac.uk/aarhus/environmental/ sosa:madeBySensor sosa:madeObservation generatedBy geo:location providedBy qoi:hasQuality derivedFrom
- 12. Use Cases • City of Aarhus open data bank • real-time data • Traffic management • 400 devices deployed detects Bluetooth from moving vehicles • Analytics • Observe day-to-day developments • Events • Irregular road events e.g. low or high traffic • Stream links to geo:Point
- 13. Use Cases • Smart Healthy Living • Daily activity monitoring • Physiological observations • Ambient phenomena • Continuous data for storage and analysis • Sensor identification using SSN/SOSA and iot-lite • Capturing QoI for consistency and reliability is critical • Exposing Services using IoT-lite • Allows access to data streams for other services and applications UK Deployment Site Architecture Device Edge Backend Frontend Users Fixed Gateway Mobile GatewayWearable DoorMotionAppliance Energy Home Energy Home Router Semantic Store Mobile App Web Dashboard ACTIVAGE App Raw Data Store Stakeholders Carers, Clinicians Users AIoTES Platform
- 14. Application Scenarios • Data Analysis Tools and Services • techniques such as data pre-processing, machine learning and correlation • E.g. aggregation, filtering, re-sampling and pattern detection • SPARQL templates that format the result in a processable manner • Output is reannotated to a new “analysed” IoTStream KAT Service Data Service nData Service 2Data Service 1 IoT Stream Broker Endpoint Broker Client Core Supervised Learning Unsupervised Learning Other methods e.g. Correlation Pre-processing Analysed Stream Annotator application/ld+json text/csv application/json text/csv application/sparql-query *.csv *.jsonld application/json *.json *.json ➢ SPARQL ➢ DA methods ➢ StreamID ➢ SPARQL ➢ DA methods ➢ Analysed stream ➢ Analysed stream ➢ SPARQL ➢ Stream *.csv *.rq
- 15. Application Scenarios • Crawling and Search Engines for IoT Data Streams • Crawler extracts metadata from data sources and stores them in a metadata repository • Several processing layer components discover data stream providers from the repository • and then invoke their brokers for their data streams
- 16. To conclude • Developed a model according to the • most recognised and state-of-the-art guidelines to develop semantic models • especially for IoT environments, where scalability and short processing time are essential • Lightweight semantic model • Allows simpler concepts to be added • compatible and as an extension of the well-known SSN/SOSA ontology • Demonstrated the validity of IoT-Stream through a series of real annotation scenarios • The ontology is publicly available and available in several formats • http://purl.org/iot/ontology/iot-stream#
- 17. Thank you Questions? Debates? ☺ Acknowledgements