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Dynamic Data Analytics for the Internet of Things: Challenges and Opportunities

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IoT Large-Scale Analytics Workshop, IoT Week Lisbon, June 2015

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Dynamic Data Analytics for the Internet of Things: Challenges and Opportunities

  1. 1. Dynamic Data Analytics for the Internet of Things: Challenges and Opportunities 1 Payam Barnaghi Institute for Communication Systems (ICS) University of Surrey/CityPulse Consortium Guildford, United Kingdom IoT Large-Scale Analytics Workshop IoT Week Lisbon, June 2015
  2. 2. Contextual Challenges 2 AnyPlace AnyTime AnyThing Data Volume Security, Reliability, Trust and Privacy Societal Impacts, Economic Values and Viability Services and Applications Networking and Communication
  3. 3. IoT Data- Challanges − Multi-modal and heterogeneous − Noisy and incomplete − Time and location dependent − Dynamic and varies in quality − Crowed sourced 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! 3
  4. 4. 4 “Relying merely on data from sources that are unevenly distributed, without considering background information or social context, can lead to imbalanced interpretations and decisions.” “It’s also about automation in addition to insight and information extraction.” ?
  5. 5. Data Lifecycle 5 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
  6. 6. IoT environments are usually dynamic and (near-) real-time 6 Off-line Data analytics Data analytics in dynamic environments Image sources: ABC Australia and 2dolphins.com
  7. 7. IoT Data 7
  8. 8. Deep IoT 8
  9. 9. 9 “The ultimate goal is transforming the raw data to insights and actionable knowledge and/or creating effective representation forms for machines and also human users and creating automation.” This usually requires data from multiple sources, (near-) real time analytics and visualisation and/or semantic representations.
  10. 10. 10 “Data will come from various source and from different platforms and various systems.” This requires an ecosystem of IoT systems with several backend support components (e.g. pub/sub, storage, discovery, and access services). Semantic interoperability is also a key requirement.
  11. 11. Search on the Internet/Web in the early days 11
  12. 12. IoT discovery engines? 12 “Working across different systems and various platforms is a key requirement. Internet search engines work very well with textual data, but IoT data comes in various forms and often as streams.” This requires an ecosystem of IoT systems with several backend support components (e.g. pub/sub, storage, discovery, and access services).
  13. 13. IoT discovery engines? 13 “ To make it more complex, IoT resources are often mobile and/or transient. Quality and trust (and obviously privacy) are among the other key challenges”. This requires efficient distributed index and update mechanisms, quality-aware an resource- aware selection and ranking, and privacy control and preservation methods (and governance models) .
  14. 14. Accessing IoT data 14 “ The internet/web norm (for now) is usually searching for the data; the search engines are usually information locators – return the link to the information; IoT data access is more opportunistic and context aware”. This requires context-aware and opportunistic push mechanism, dynamic device/resource associations and (software-defined) data routing networks.
  15. 15. Web search is already adapting this model 15 Image credits: the Economist
  16. 16. A discovery engine for the IoT 16 A. HosseiniTabatabaie, P. Barnaghi, C. Wang, L. Dong, R. Tafazolli, "Method and Apparatus for Scalable Data Discovery in IoT Systems”, US Patents, May 2014.
  17. 17. CityPulse demo 17
  18. 18. KAT- Knowledge Acquisition Toolkit http://kat.ee.surrey.ac.uk/
  19. 19. The future: borders will blend 19Source: IEEE Internet Computing, Special issue on Physical-Cyber-Social Computing
  20. 20. In conclusion − IoT data analytics is different from common big data analytics. − Data collection in the IoT comes at the cost of bandwidth, network, energy and other resources. − Data collection, delivery and processing is also depended on multiple layers of the network. − We need more resource-aware data analytics methods and cross-layer optimisations (Deep IoT). − The solutions should work across different systems and multiple platforms (Ecosystem of systems). − Data sources are more than physical (sensory) observation. − The IoT requires integration and processing of physical-cyber-social data. − The extracted insights and information should be converted to a feedback and/or actionable information. 20
  21. 21. Smart city datasets 21 http://iot.ee.surrey.ac.uk:8080
  22. 22. IET sector briefing report 22 Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
  23. 23. Q&A − Thank you. − EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @pbarnaghi p.barnaghi@surrey.ac.uk

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