- ConTaaS is a novel contextualization architecture and technique for scaling up contextualization of internet-of-things data to internet scales. - It employs prime factorization to efficiently contextualize large volumes of data from many IoT devices. - The approach was implemented on Amazon EC2 cloud infrastructure and evaluated using synthetic data from Melbourne city datasets. It provides a way to represent, contextualize, and query large-scale IoT data.

1. ConTaaS: An Approach to Internet-Scale Contextualisation for
Developing Efficient Internet of Things Applications
Ali Yavari
mail@aliyavari.com
www.aliyavari.com
Presented at ISSIP Service Innovation Speaker Series

2. RMIT University - July 2015 2
In the late 1960s, communication between two computers was made possible through a computer network
In the early 1980s the TCP/IP stack was introduced.
Commercial use of the Internet started in the late 1980
World Wide Web (WWW) became available in 1991
Internet of Things term by Kevin Ashton 1998 (“The Internet of Things has the potential to change the world, just as the Internet did.
Maybe even more so”)
Web of Things (WoT) in 2000
MIT Auto-ID centre presented their IoT vision in 2001
IoT was formally introduced by International Telecommunication Union (ITU) in 2005
More “things or objects” were connected to the Internet than people. 2008-2009 [Cisco]
1960 1980 1996 2000 2001

3. Sensors and other Internet-connected devices that are all connected to the
internet and they interact intelligently to make the development and delivery
of new services and products
A new paradigm which connects a variety of things- All the things that have the ability to communicate

4. Identify
Communicate
Sense
Action

5. Data Pyramid
“The price of light is less than the cost of darkness.” – Arthur C Nielsen
Wisdom
Knowledge
Information
Data
Process

6. Cisco IBSG projections,UN Economic & Social Affairs http://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf
6.307
6.721 6.894 7.347 7.83
0
10
20
30
40
50
2003 2008 2010 2015 2020
BillionsofDevices
World Population
50
Billion
SmartObjects
Rapid adoption rate of digital infrastructure
5 x fasterthan electricity & telephony
“Things” per person
Inflection Point

7. 1.1 Billion
Data points generatedby sensors daily
500 Gigabytes
Data generatedby an offshoreoil rig weekly
1000 Gigabytes
Data generatedby an oil refinery daily
10,000 Gigabytes
Data generatedby a jet engine every 30 minutes
2.5 Billion Gigabytes
Data generatedworldwide daily
90% of the world’s data
Has beencreatedin the last2 years!

8. Observation
Orientation
Decision
Action

9. IoT Platforms

10. Internet-connected Devices
Connectivity
Internet-scaled Data
Data Reduction
IoT Services and Application
Presentation
Contextualisation

11. Contextualisation
• Contextualisation excludes irrelevant data from
consideration and has the potential to reduce data
from several aspects including volume, velocity, and
variety in IoT applications
• Contextualisation of IoT data can help improve the
value of information extracted from IoT
• Contextualisation improve the data processing and
knowledge extraction in IoT applications
• Existing contextualisation techniques can only
handle small datasets from a modest number of IoT
devices
• Current contextualisation techniques are limited to
particular applications
Context Collection
Contextualisation
Dissemination of
the contextualised
data

12. Context
Context or contextual information is any information about any entity that
can be used to effectively reduce the amount of reasoning required (via
filtering, aggregation, and inference) for decision making within the scope
of specific applications.

13. ConTaaS
• A novel contextualisation architecture, which we refer to as ConTaaS
Architecture, for contextualising Internet-scale IoT data and facilitating the
developing of efficient IoT applications.
• A novel contextualisation technique that employs prime factorisation to
scale up the contextualisation of IoT data.
• A cloud-based ConTaaS implementation that utilises commercially
available cloud infrastructure services (more specifically Amazon EC2).
Yavari, A., Jayaraman, P. P., Georgakopoulos, D., and Nepal, S., 2017. ConTaaS: An Approach to Internet-Scale
Contextualisation for Developing Efficient Internet of Things Applications. In Proceedings of the 50th Hawaii International
Conference on System Sciences. IBM/ISSIP Best Paper Award

14. A. Yavari, P. P. Jayaraman, D. Georgakopoulos, and S. Nepal, ‘‘ContaaS: An approach to internet-scale contextualisation for developing
efficient internet of things applications,’’ in Hawaii International Conference on System Sciences HICSS

15. 15
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K.,
Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
W3C Semantic Sensor Network Ontology

16. Yavari, A., Jayaraman, P. P., Georgakopoulos, D., and Nepal, S., 2017. ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient
Internet of Things Applications. In Proceedings of the 50th Hawaii International Conference on System Sciences.

17. A. Yavari, P. P. Jayaraman, D. Georgakopoulos, and S. Nepal, ‘‘ContaaS: An approach to internet-scale contextualisation for developing
efficient internet of things applications,’’ in Hawaii International Conference on System Sciences HICSS

18. • An approach to represent and contextualised data originating from IoT
devices.
• A mechanism to efficiently query the contextualised IoT data
• An example of a smart parking space recommender application
• An experimental evaluation of the proposed contextualised IoT data
querying approach using synthetic data generated from Melbourne city
datasets https://data.melbourne.vic.gov.au/
Contextualised Service Delivery in the
Internet of Things

19. User contexts
• License Type (Full for experienced, P Green for 1 year experience, Red P for
beginner).
• Car Specification (e.g. BMW X5 2015)
• Preferences (e.g. Secure car park, shady car park and so forth)
• Location (Suburbs)
Parking spot contexts
• Location (Suburbs)
• Type (closed, opened)
• Space (number of available spots)
• Acceptable car body types (SUV, Hatch, Sedan and so forth)
• Weather condition (Sunny, rainy, snowy and so forth)
Yavari, A., Jayaraman, P. P., and Georgakopoulos, D.. Contextualised Service Delivery in the Internet of Things, Parking
Recommender for Smart Cities. In Proceedings of the IEEE World Forum on Internet of Things. 2016.

20. Yavari, A., Jayaraman, P. P., and Georgakopoulos, D.. Contextualised Service Delivery in the Internet of Things, Parking
Recommender for Smart Cities. In Proceedings of the IEEE World Forum on Internet of Things. 2016.

21. Thank You!