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IoT-Lite: A Lightweight Semantic Model for the Internet of Things

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2016 IEEE Conferences on Ubiquitous Intelligence & Computing, July 2016.

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IoT-Lite: A Lightweight Semantic Model for the Internet of Things

  1. 1. IoT-Lite: A Lightweight Semantic Model for the Internet of Things 1 Maria Bermudez-Edo (University of Granada), Tarek Elsaleh, Payam Barnaghi (University of Surrey), Kerry Taylor (The Australian National University/University of Surrey)
  2. 2. 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. 3 Sensor devices are becoming widely available - Programmable devices - Off-the-shelf gadgets/tools
  4. 4. 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…
  5. 5. 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. − Data is gathered from various heterogeneous sources and in various format and representations. − Often the value is in integrating data from different sources and in creating an ecosystem of systems.
  6. 6. Device/Data interoperability 6 The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
  7. 7. Heterogeneity, multi-modality and volume are among the key issues. We need interoperable and machine-interpretable solutions… 7
  8. 8. Semantic Sensor Web 8 “The semantic sensor Web enables interoperability and advanced analytics for situation awareness and other advanced applications from heterogeneous sensors.” (Amit Sheth et al, 2008)
  9. 9. 9 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.
  10. 10. 10 There are several good models and description frameworks; The problem is that having good models and developing ontologies are not enough. 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.
  11. 11. Data Lifecycle 11 Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data driven systems for building, community and city-scale applications, http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
  12. 12. Semantics in IoT networks WSN WSN WSN WSN WSN Network-enabled Devices Semantically annotate data 12 Gateway CoAP HTTP CoAP CoAP HTTP 6LowPAN Semantically annotate data http://mynet1/snodeA23/readTemp? WSN MQTT MQTT Gateway network- enabled devices Gateway
  13. 13. An overview of IoT-Lite 13
  14. 14. An example 14
  15. 15. Design Rules (1) −Design for large-scale. −Think of who will use the semantics and design for their needs (keep the minimum required tags). −Provide means to update and change the semantic annotations (not covered). −Create tools for validation and interoperability testing (TBD). −Create taxonomies and vocabularies. 15
  16. 16. Design Rules (2) − Re-use existing models. − Link data and descriptions to other existing resources. − Define rules and/or best practices for providing the values for each property. − Keep it simple. − Create effective methods, tools and APIs to handle and process the semantics. 16
  17. 17. Evaluations- data size 17 Comparison with the IoT-A model
  18. 18. Evaluations- Query Time 18 Query performed in the experiments
  19. 19. Evaluations- Query Time 19 Round Time Trip (RTT) of the queries required to retrieve the endpoint.
  20. 20. IoT-lite ontology 20
  21. 21. IoT-Lite 21 http://www.w3.org/Submission/iot-lite/
  22. 22. In Conclusion − The IoT-Lite Ontology provides an extensible way to describe devices acting as sensors, actuators or tags in terms of their attributes and associated units of measure, as well as the device's physical location and area of coverage. 22
  23. 23. In Conclusion 23 - Semantic descriptions are intermediary solutions, not the end product. - They, usually, should be transparent to the end- user and probably to the data producer as well.
  24. 24. In Conclusion −IoT-Lite (or any other similar model) should be offered with: −Tools for annotation (similar to SAOPY) −http://iot.ee.surrey.ac.uk/citypulse/ontologies/sao/saopy.html −Tools for validation (similar to the SSN validator) −http://iot.ee.surrey.ac.uk/SSNValidation/ −Best practices −Sample code and sample datasets 24
  25. 25. 25
  26. 26. Acknowledgment The research leading to these results has received funding from the European Commission’s in the Seventh Framework Programme for the FIWARE project under grant agreement no. 632893 and in the H2020 for FIESTA-IoT project under grant agreement no. CNECT-ICT-643943. 26
  27. 27. Q&A − Thank you. http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ @pbarnaghi p.barnaghi@surrey.ac.uk

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