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Designing Cross-Domain Semantic Web of Things Applications

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PhD Thesis defense, 24 April 2015
Designing Cross-Domain Semantic Web of Things Applications

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Designing Cross-Domain Semantic Web of Things Applications

  1. 1. Designing Cross-Domain Semantic Web of Things Applications Amelie Gyrard Christian Bonnet (Eurecom, Mobile Communication) Karima Boudaoud (I3S, Security)
  2. 2. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Components  Use cases  Evaluations  Demonstrations  Conclusion & Future work 2
  3. 3. How to interpret Internet of Things (IoT) data? Thermometer Sensor data Applications to visualize data Interpretation by humans How machines can interpret data? 3 Machine learning? Reusing domain knowledge?
  4. 4. How to combine and reuse IoT data? How to get additional information? How to combine data from different domains? 4 How to combine domains? How to describe data?
  5. 5. How to describe data?  Taking inspiration from the Web Automatically built by machines 5
  6. 6. How to get additional information?  Agreeing on common vocabularies to describe data on the web:  Semantic search engines  Web sites  They built together Schema.org 6
  7. 7. How to apply semantic web technologies to Internet of Things?  Machine-understandable data  Describe data with common vocabularies  Reuse domain knowledge  Link to other data  Ease the reasoning  => How to provide a common description of sensor data to later reason on it? 7
  8. 8. How to combine IoT data from different domains? 8 Innovative applications Interoperability on protocols or data?
  9. 9. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Conclusion & Future work 9
  10. 10. “Semantic Web of Things: an analysis of the application semantics for the IoT moving towards the IoT convergence” [Jara et al. 2014] Semantic Web of Things: Main challenges (1) Machine-to-Machine (M2M): no human intervention Global interoperability  How?  Why? Common description Common App. Protocol Device Abstraction Common Nwk. Protocol 10
  11. 11. Semantic Web of Things: Main challenges (2) “Semantics for the Internet of Things: early progress and back to the future” [Barnaghi et al. 2012] 11
  12. 12. State of the Art: Semantic Sensor Networks 2008 ‘Semantic Sensor Web’ ‘Linked Sensor Data’ 2013 2014 SemSOS, ‘Semantic Perception’ ‘Infer high-level abstraction’ ‘Linked Stream Data’ 2015 ‘SPARQLStream’ • A) How to design semantic-based IoT applications? • B) Interpret data? Combine domains ? Reuse domain knowledge? • C) Security & IoT? 2011 W3C SSN ontology Real-time?Use semantic web technologies? Interpret data? 12
  13. 13. State of the art: W3C SSN ontology  Limitations of W3C SSN ontology:  Interoperability issues to reuse and combine domain ontologies  Need of a common description to describe sensor measurements  Need of an approach to share and reuse the reasoning approach  Need to integrate semantics to IoT and M2M => How to extend the W3C SSN ontology to provide a common description of sensor data to later reason on it by reusing domain knowledge? http://www.w3.org/2005/Incubator/ssn/ssnx/ssn# http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/ http://www.w3.org/2015/spatial/charter 13
  14. 14. Three main research challenges to address  Challenge A: How to design semantic-based IoT applications?  Challenge B: How to interpret IoT data?  Challenge B.1: How to reuse and combine IoT data?  Challenge B.2: How to reuse and combine domain knowledge?  Challenge C: How to secure IoT applications? 14
  15. 15. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Conclusion & Future work 15
  16. 16. Our solution: Machine-to-Machine Measurement Framework (M3) Challenge A: Design semantic based IoT applications Challenge B.1 & B.2: Combine data and domains Challenge B: Interpret IoT data Challenge C: Secure IoT applications Challenge B.2: Reuse domain knowledge 16
  17. 17. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Conclusion & Future work 17
  18. 18. SWoT generator Template used in 3 steps: 1) Designing phase 2) Development phase 3) Running phase SWoT template => Benefits: No need to learn semantic web technologies IoT Application generate build use IoT developers 18
  19. 19. Designing phase 19 * * Domain where is deployed the sensor, not the applicative domain Challenge A: Design semantic based IoT applications
  20. 20. Development phase IoT developers SWoT template 1) Load: - M3 ontologies - M3 IoT data - M3 datasets 4) Get M3 suggestions or high level abstractionsSTEPS BEFORE Get template 3) Execute M3 SPARQL query + SPARQL engine SemanticWeb Framework 2) Execute M3 rules + reasoning engine 20
  21. 21. Running phase 21
  22. 22. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Conclusion & Future work 22
  23. 23. M3 language & ontology 23 Challenge B.1 & B.2: Combine data and domains
  24. 24. M3 language & M3 ontology  Data is from heterogeneous projects and domains  Domain (e.g., health, smart building, weather, room, city, etc.)  Measurement type (e.g., t = temp = temperature)  Sensor type (e.g., rainfall sensor = precipitation sensor)  Units (e.g., Celsius, Fahrenheit, Kelvin)  M3 language implemented in the M3 ontology  Describe data in an unified way  Extension of the W3C Semantic Sensor Networks (SSN) ontology (Observation Value concept)  Provide a basis for reasoning and cross-domain interlinking 24 http://www.sensormeasurement.appspot.com/documentation/Nomenclat ureSensorData.pdf
  25. 25. M3 language: a hub for cross-domain ontologies and datasets 25
  26. 26. Sensor-based Linked Open Rules (S-LOR) 26 Challenge B: Interpret IoT data
  27. 27. S-LOR: Deducing new knowledge  How to deduce new knowledge?  S-LOR: a dataset of interoperable rules  Rules example:  If Domain == Health && MeasurementType == Temperature then NewType = BodyTemperature  If BodyTemperature > 38,7°C then “Fever”  BodyTemperature and Fever are already described in domain ontologies or datasets! 27 Demo paper: Helping IoT application developers with Sensor-based Linked Open Rules [Gyrard et al., ISWC 2014, SSN workshop]
  28. 28. Linked Open Vocabularies for Internet of Things (LOV4IoT) Challenge B.2: Reuse domain knowledge 28
  29. 29.  A dataset of more than 270 ontology-based projects relevant for IoT  Ontologies  Datasets  Rules to interpret IoT data  Technologies used  Sensors used  Security mechanisms used  Domains relevant for IoT LOV4IoT http://www.sensormeasurement.appspot.com/?p=ontologies29 A second life for ontologies!
  30. 30.  LOV4IoT is used to build the SWoT template  Used to re-design interoperable ontologies, rules, datasets  Limitations: Manually and not automatically LOV4IoT http://www.sensormeasurement.appspot.com/?p=ontologies Collect Classify Interoperability SWoT template 30 A second life for ontologies!
  31. 31. M3 interoperable domain knowledge  Need to have the set of files generated in the template compatible with sensor data  Ontologies + datasets + rules + sensor data  Domain knowledge structured in the same way Domain ontologies Domain datasets Rules Interoperable IoT Application Provide sensor data SWoT templateM3 IoT data Produce 31
  32. 32. M3 semantic engine  Enrich data & combine domains 32 Paper: Enrich Machine-to-Machine Data with Semantic Web Technologies for Cross- Domain Applications [Gyrard et al., WF-IoT 2014]
  33. 33. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Conclusion & Future work 33
  34. 34. Security Toolbox: Attacks & Countermeasures (STAC) Challenge C: Help non- security experts to secure IoT applications 34
  35. 35. The STAC ontology Paper: The STAC (Security Toolbox: Attacks & Countermeasures) ontology [Gyrard et al., Poster, WWW 2013] 35
  36. 36. STAC Hub  Reusing security knowledge from LOV4IoT 36 Paper: An ontology-based approach for helping to secure the ETSI Machine-to- Machine Architecture [Gyrard et al., iThings 2014]
  37. 37. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Use cases  Evaluations  Demonstrations  Conclusion & Future work 37
  38. 38. M3 use cases  3 Mock-ups: Naturopathy, Tourism, Transport  Proof of concept: less user-friendly  Integrating the M3 approach everywhere!  Cloud, Android-powered devices and Gateway  Combine domain-specific sophisticated applications  Not just data visualization  Suggestions or high-level abstractions 38
  39. 39. Use Case: Embedding M3 in smart fridges M3 suggestions: Home remedies Get temperature measurement Stop to be sick with M3! 39
  40. 40. Use Case: Embedding M3 in smart luggage M3 suggestions: Garments & Activities Get weather measurement Stop to forget things with M3! Smart Luggage Destination: Mountain in winter Destination: Beach in summer 40
  41. 41. Use Case: Embedding M3 in smart cars Avoid accidents with M3! 41
  42. 42. Evaluations: Research hypotheses  Templates help IoT projects build their scenarios  The semantic engine is not too resource consuming  The semantic engine is generic enough to support various kind of IoT measurement.  The interoperable knowledge bases built follows semantic web best practices.  Our knowledge bases help non-experts in semantic web or in security  LOV4IoT is exploited outside of the M3 framework. 42
  43. 43. Evaluating the SWoT generator  Do we have templates covering the most popular IoT use cases? http://www.sensormeasurement.appspot.com/?p=m3_scenario  Adding a new template?  Less than 1 day  Depends on whether we already have the interoperable domain knowledge 43
  44. 44. Evaluating M3 software performances  Goal: The semantic engine is not too resource consuming  Evaluation:  Measuring time consumed  Results:  Encouraging (16 – 31 ms)  Could be embedded on Android-powered device 44
  45. 45. Demo  Demo http://sensormeasurement.appspot.com 45
  46. 46. M3 framework at work Domain experts IoT developers End users Design applications Need new applications Standardize Design new ontology matching tools + Automatic extraction of domain knowledge Exploit & Contribute 46  Relevant for different communities
  47. 47. Agenda  Introduction & Motivation  State of The Art & Main challenges  Contributions: M3 framework  Conclusion & Future work 47
  48. 48. Summary of contributions 48
  49. 49. ChallengeA M3: An entire chain from sensor data to build IoT cross-domain IoT applications Sensor data Interpret data + Combine domains Interoperable sensor data descriptions Reuse domain knowledge Build IoT applicationsProvide template Secure applications ChallengeC ChallengeA ChallengeB.1 ChallengeB ChallengeB.2 49
  50. 50. Conclusion & Lessons learnt  M3: an innovative approach to assist users in designing interoperable cross-domain Semantic Web of Things applications:  A uniform language for sensor data descriptions  An open-source approach to interpret IoT data  Combine domains  Semantics is hidden to the users  Lessons learnt:  M3 generic enough for other domains than IoT and security 50
  51. 51.  Our proposed approach: M3 framework Future work Sensor Plug & Play Extract & combine domain knowledge Standardizing common descriptions Merge M3 to existing SWoT projects Global interoperability Common description Device Abstraction Common App. Protocol Common Nwk. Protocol 51 S-LOR with more reasoning
  52. 52. Future work: Merge M3 to existing SWoT projects Use real datasets & scenarios + real-time Suggest machine learning algorithms to employ for complicated sensors Connect new sensors 52 Rewrite ontologies
  53. 53. Future work: Extracting and combining domain knowledge  Extracting popular concepts from domain ontologies  Cloud tag inspired by the W3C SSN validator  Extracting rules from ontologies  OWL 2 RL template, DLEJena  Combining domain knowledge  Design and combine new ontology matching tools  Look at ontology alignment ontology & merging tools  Designing an interoperable domain knowledge 53
  54. 54. Thank you!  gyrard@eurecom.fr  http://sensormeasurement.appspot.com/ 54
  55. 55. Relevant Publications  International Conferences:  Enrich Machine-to-Machine Data with Semantic Web Technologies for Cross-Domain Applications (WF-IoT 2014)  An ontology-based approach for helping to secure the ETSI Machine-to-Machine Architecture (iThings 2014)  A machine-to-machine architecture to merge semantic sensor measurements (WWW 2013, DC)  International Workshops:  Standardizing Generic Cross-Domain Applications in Internet of Things (Globecom , WTS, 2014)  Demo paper: Helping IoT application developers with Sensor-based Linked Open Rules (ISWC, SSN 2014)  See Google Scholar for more publications  Participation to standardizations: 55

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