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Semantic Technologies for the Internet of Things: Challenges and Opportunities


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ASEAN Innovation Forum, Kuala Lumpur, Malaysia, August 2015.

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Semantic Technologies for the Internet of Things: Challenges and Opportunities

  1. 1. Semantic Technologies for the Internet of Things: Challenges and Opportunities 1 Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey Guildford, United Kingdom MyIoT Week Malaysia 2015, MIMOS Berhad Kuala Lumpur, Malaysia, August 2015
  2. 2. The Internet of Things (IoT) 2P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology (IET), I. Borthwick (editor), March 2015.
  3. 3. Real world data 3
  4. 4. 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 several important issues such as: − Device/network management − Actuation and feedback (command and control) − Resource, service and entity descriptions.
  5. 5. IoT data- challenges − Multi-modal, distributed and heterogeneous − Noisy and incomplete − Time and location dependent − Dynamic and varies in quality − Crowdsourced data can be unreliable − Requires (near-) real-time analysis − Privacy and security are important issues − Data can be biased- we need to know our data! 5 P. Barnaghi, A. Sheth, C. Henson, "From data to actionable knowledge: Big Data Challenges in the Web of Things," IEEE Intelligent Systems, vol.28 , issue.6, Dec 2013.
  6. 6. 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 Motion sensor ECG 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…
  7. 7. Scale of the problem 7 Things Data Devices 2.5 quintillion bytes per day Billions and Billions of them… Estimated 50 Billion by 2020
  8. 8. Device/Data interoperability 8 The slide is adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
  9. 9. Heterogeneity, multi-modality and volume are among the key issues. We need interoperable and machine-interpretable solutions… 9
  10. 10. 10
  11. 11. 11 But why do we still not have fully integrated semantic solutions in the IoT?
  12. 12. 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) 12 Image source: Miller 2004
  13. 13. 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. 13
  14. 14. Semantic Descriptions in Semantic (Web) World 14
  15. 15. Semantic Web these days… 15
  16. 16. 16 The world of IoT and Semantics
  17. 17. 17 Some good existing models: SSN Ontology Ontology Link: M. Compton, P. Barnaghi, L. Bermudez, et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
  18. 18. Semantic Sensor Web 18 “The semantic sensor Web enables interoperability and advanced analytics for situation awareness and other advanced applications from heterogeneous sensors.” (Amit Sheth et al., 2008)
  19. 19. Several ontologies and description models 19
  20. 20. 20 We have good models and description frameworks; The problem is that having good models and developing ontologies is not enough.
  21. 21. 21 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.
  22. 22. A WoT/IoT Framework WSN WSN WSN WSN WSN Network-enabled Devices Semantically annotate data 22 Gateway CoAP HTTP CoAP CoAP HTTP 6LowPAN Semantically annotate data http://mynet1/snodeA23/readTemp? WSN MQTT MQTT Gateway And several other protocols and solutions…
  23. 23. 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. 23
  24. 24. 24 Simplicity can be very useful…
  25. 25. HyperCAT 25 Source: Toby Jaffey, HyperCat Consortium, - 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).
  26. 26. Hyper/CAT model 26 Source: Toby Jaffey, HyperCat Consortium,
  27. 27. 27 Complex models are (sometimes) good for publishing research papers…. But they are often difficult to implement and use in large-scale and in real world products.
  28. 28. 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 28
  29. 29. The IoT is a dynamic, online and rapidly changing world 29 isPartOf Annotation for the (Semantic) Web Annotation for the IoT Image sources: ABC Australia and
  30. 30. Make your model fairly simple and modular 30 SSNO model
  31. 31. Tools and APIs 31P. Barnaghi, M. Presser, K. Moessner, "Publishing Linked Sensor Data", in Proc. of the 3rd Int. Workshop on Semantic Sensor Networks (SSN), ISWC2010, 2010.
  32. 32. 32 Creating common vocabularies and taxonomies are also equally important e.g. Event and unit vocabluaries.
  33. 33. 33 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.
  34. 34. An example: a discovery method in the IoT time location type Query formulating [#location | #type | time][#location | #type | time] Discovery ID Discovery/ DHT Server Data repository (archived data) #location #type #location #type #location #type Data hypercube Gateway Core network Network Connection Logical Connection Data
  35. 35. An example: a discovery method in the IoT 35 S. A. Hoseinitabatabaei, P. Barnaghi, C. Wang, R. Tafazolli, L. Dong, "A Distributed Data Discovery Mechanism for the Internet of Things", US Patents, 2015.
  36. 36. An example: a discovery method in the IoT 36 S. A. Hoseinitabatabaei, P. Barnaghi, C. Wang, R. Tafazolli, L. Dong, "A Distributed Data Discovery Mechanism for the Internet of Things", US Patents, 2015.
  37. 37. 101 Smart city use-case scenarios
  38. 38. 38 Semantic descriptions can be fairly static on the Web; In the IoT, the meaning of data and the annotations can change over time/space…
  39. 39. Static Semantics 39
  40. 40. 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> 40 But this could be a function of time and location; What would be the accuracy 5 seconds after the measurement?
  41. 41. Dynamic annotations for data in the process chain 41 S. Kolozali et al, A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", iThings 2014, 2014.
  42. 42. Dynamic annotations for provenance data 42 S. Kolozali et al, A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", iThings 2014, 2014.
  43. 43. 43 Semantic descriptions can also be learned and created automatically.
  44. 44. Extraction of events and semantics from social media 44 City Infrastructure Tweets from a city Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P Sheth, "Extracting City Traffic Events from Social Streams", ACM Transactions on Intelligent Systems and Technology, 2015
  45. 45. Ontology learning from real world data 45 Frieder Ganz, Payam Barnaghi, Francois Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal,
  46. 46. Overall, we need semantic technologies in the IoT and these play a key role in providing interoperability.
  47. 47. However, we should design and use the semantics carefully and consider the constraints and dynamicity of the IoT environments.
  48. 48. An IoT framework WSN WSN WSN WSN WSN Network-enabled Devices 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 GWGW GWGW GWGWData streams
  49. 49. IoTLite Ontology 49
  50. 50. Reference Datasets 50
  51. 51. Importance of complementary data 51
  52. 52. #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. 52
  53. 53. #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. 53
  54. 54. #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. 54
  55. 55. #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. 55 In Conclusion
  56. 56. W3C working group on spatial data on the Web 56
  57. 57. Q&A − Thank you. @pbarnaghi