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Dynamic Semantics for Semantics for Dynamic IoT Environments

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Keynote talk at 7th International Workshop on Semantic Sensor Network (SSN2014) at ISWC2014

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Dynamic Semantics for Semantics for Dynamic IoT Environments

  1. 1. Dynamic Semantics for Dynamic IoT Environments 1 Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom 7th International Workshop on Semantic Sensor Networks (SSN2014) @ ISWC2014
  2. 2. 2 Things, Devices, Data, and lots of it image courtesy: Smarter Data - I.03_C by Gwen Vanhee
  3. 3. Data in the IoT − Data is collected by sensory devices and also crowd sensing sources. − It is time and location dependent. − It can be noisy and the quality can vary. − It is often continuous - streaming data. − There are other important issues such as: − Device/network management − Actuation and feedback (command and control) − Service and entity descriptions are also important.
  4. 4. 4 Key characteristics of IoT devices −Often inexpensive sensors (and actuators) equipped with a radio transceiver for various applications, typically low data rate ~ 10-250 kbps. −Some, such as CCTV, can have high data rates −Data rates/distribution, however, can be sporadic −Deployed in large numbers −Often coordination between more than once source is required to perform a task. −Sometimes in-network processing is required. −Solutions are application-dependent. 4
  5. 5. Internet of Things: The story so far RFID based solutions Wireless Sensor and Actuator networks , solutions for communication technologies, energy efficiency, routing, … Smart Devices/ Web-enabled Apps/Services, initial products, vertical applications, early concepts and demos, … Motion sensor ECG sensor Motion sensor Physical-Cyber-Social Systems, Linked-data, semantics, M2M, More products, more heterogeneity, solutions for control and monitoring, … Future: Cloud, Big (IoT) Data Analytics, Interoperability, Enhanced Cellular/Wireless Com. for IoT, Real-world operational use-cases and Industry and B2B services/applications, more Standards…
  6. 6. Scale of the problem 6 Things Data Devices 2.5 quintillion bytes per day Billions and Billions of them… Estimated 50 Billion by 2020
  7. 7. Heterogeneity, multi-modality and volume are among the key issues. We need interoperable and machine-interpretable solutions… 7
  8. 8. 8
  9. 9. 9 But why do we still not have fully integrated semantic solutions in the IoT?
  10. 10. A bit of history − “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in co-operation.“ (Tim Berners-Lee et al, 2001) 10 Image source: Miller 2004
  11. 11. Semantics & the IoT − The Semantic Sensor (&Actuator) Web is an extension of the current Web/Internet in which information is given well-defined meaning, better enabling objects, devices and people to work in co-operation and to also enable autonomous interactions between devices and/or objects. 11
  12. 12. Semantic Descriptions in Semantic (Web) World 12
  13. 13. Semantic Web these days… 13
  14. 14. 14 The world of IoT and Semantics
  15. 15. 15 Some good existing models: 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.
  16. 16. Semantic Sensor Web 16 “The semantic sensor Web enables interoperability and advanced analytics for situation awareness and other advanced applications from heterogeneous sensors.” (Amit Sheth et al, 2008)
  17. 17. Several ontologies and description models 17
  18. 18. 18 We have good models and description frameworks; The problem is that having good models and developing ontologies is not enough.
  19. 19. 19 Semantic descriptions are intermediary solutions, not the end product. They should be transparent to the end-user and probably to the data producer as well.
  20. 20. A WoT/IoT Framework WSN WSN WSN WSN WSN Network-enabled Devices Semantically annotate data 20 Gateway CoAP HTTP CoAP CoAP HTTP 6LowPAN Semantically annotate data http://mynet1/snodeA23/readTemp? WSN MQTT MQTT Gateway And several other protocols and solutions…
  21. 21. Publishing Semantic annotations − We need a model (ontology) – this is often the easy part for a single application. − Interoperability between the models is a big issue. − Express-ability vs Complexity is a challenge − How and where to add the semantics − Where to publish and store them − Semantic descriptions for data, streams, devices (resources) and entities that are represented by the devices, and description of the services. 21
  22. 22. 22 Simplicity can be very useful…
  23. 23. Hyper/CAT - Servers provide catalogues of resources to clients. - A catalogue is an array of URIs. - Each resource in the catalogue is annotated with metadata (RDF-like triples). 23 Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html
  24. 24. Hyper/CAT model 24 Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html
  25. 25. 25 Complex models are (sometimes) good for publishing research papers…. But they are often difficult to implement and use in real world products.
  26. 26. What happens afterwards is more important − How to index and query the annotated data − How to make the publication suitable for constrained environments and/or allow them to scale − How to query them (considering the fact that here we are dealing with live data and often reducing the processing time and latency is crucial) − Linking to other sources 26
  27. 27. The IoT is a dynamic, online and rapidly changing world 27 isPartOf Annotation for the (Semantic) Web Annotation for the IoT Image sources: ABC Australia and 2dolphins.com
  28. 28. Make your model fairly simple and modular 28 SSNO model
  29. 29. Tools and APIs 29
  30. 30. 30 Creating common vocabularies and taxonomies are also equally important e.g. event taxonomies.
  31. 31. 31 We should accept the fact that sometimes we do not need (full) semantic descriptions. Think of the applications and use-cases before starting to annotate the data.
  32. 32. An example: a discovery method in the IoT time location type Query formulating [#location || ##ttyyppee || ttiimmee]] Discovery ID Discovery/ DHT Server Data repository (archived data) #location #type #location #type Data hypercube #location #type Gateway Core network Logical Connection Network Connection Data
  33. 33. An example: a discovery method in the IoT S. A. Hoseinitabatabaei, P. Barnaghi, C. Wang, R. Tafazolli, L. Dong, "A Distributed Data Discovery Mechanism for the Internet of Things", 33 2014.
  34. 34. An example: a discovery method in the IoT S. A. Hoseinitabatabaei, P. Barnaghi, C. Wang, R. Tafazolli, L. Dong, "A Distributed Data Discovery Mechanism for the Internet of Things", 34 2014.
  35. 35. 101 Smart City Use-case Scenarios http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements
  36. 36. 36 Semantic descriptions can be fairly static on the Web; In the IoT, the meaning of data and the annotations can change over time/space…
  37. 37. Static Semantics 37
  38. 38. Dynamic Semantics <iot:measurement> <iot:type> temp</iot:type> <iot:unit>Celsius</iot:unit> <time>12:30:23UTC</time> <iot:accuracy>80%</iot:accuracy> <loc:long>51.2365<loc:lat> <loc:lat>0.5703</loc:lat> </iot:measurment> But this could be a function of time and location; What would be the accuracy 5 seconds after the measurement? 38
  39. 39. Dynamic annotations for data in the process chain S. Kolozali et al, A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", iThings 2014, 2014. 39
  40. 40. Dynamic annotations for provenance data S. Kolozali et al, A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", iThings 2014, 2014. 40
  41. 41. 41 Semantic descriptions can also be learned and created automatically.
  42. 42. Extraction of events and semantics from social media 42 Tweets from a city City Infrastructure https://osf.io/b4q2t/ P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, 2014.
  43. 43. Ontology learning from real world data 43
  44. 44. Overall, we need semantic technologies in the IoT and these play a key role in providing interoperability.
  45. 45. However, we should design and use the semantics carefully and consider the constraints and dynamicity of the IoT environments.
  46. 46. The IoT 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
  47. 47. #1: Design for large-scale and provide tools and APIs. #2: Think of who will use the semantics and how when you design your models. #3: Provide means to update and change the semantic annotations. 47
  48. 48. #4: Create tools for validation and interoperability testing. #5: Create taxonomies and vocabularies. #6: Of course you can always create a better model, but try to re-use existing ones as much as you can. 48
  49. 49. #7: Link your data and descriptions to other existing resources. #8: Define rules and/or best practices for providing the values for each attribute. #9: Remember the widely used semantic descriptions on the Web are simple ones like FOAF. 49
  50. 50. #10: Semantics are only one part of the solution and often not the end-product so the focus of the design should be on creating effective methods, tools and APIs to handle and process the semantics. Query methods, machine learning, reasoning and data analysis techniques and methods should be able to effectively use these semantics. 50 In Conclusion
  51. 51. Q&A − Thank you. − EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @pbarnaghi p.barnaghi@surrey.ac.uk

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