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

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. 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. 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
“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. 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. 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. IoT Data
7

8. Deep IoT
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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
“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. Search on the Internet/Web in the early days
11

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. 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. 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. Web search is already adapting this model
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Image credits: the Economist

16. A discovery engine for the IoT
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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. CityPulse demo
17

18. KAT- Knowledge Acquisition Toolkit
http://kat.ee.surrey.ac.uk/

19. The future: borders will blend
19Source: IEEE Internet Computing, Special issue on Physical-Cyber-Social Computing

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.
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21. Smart city datasets
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http://iot.ee.surrey.ac.uk:8080

22. IET sector briefing report
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Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm

23. Q&A
− Thank you.
− EU FP7 CityPulse Project:
http://www.ict-citypulse.eu/
@pbarnaghi
p.barnaghi@surrey.ac.uk