The Internet of Things (IoT) plays an important role in the development of smart cities. In this paper we focus on the development of IoT-based smart services for solving urban problems that involve IoT-enabled Observation, Orientation, Decision, and Action (OODA) loops. We also focus on how to efficiently support such OODA loops in situations where such loops involve internet-scale data. More specifically, IoT supports Observation via the discovery of sensors and the integration of their data. It supports Orientation via a contextualisation process that refines such data to include only those that are relevant to the situation and/or activities of each specific individual or group. As IoT contextualisation potentially involves internet-scale data, performing this process efficiently allows for fast decision making, and this in turn permits carrying out a timely Action.

1. Contextualised Service Delivery in
Internet of Things
Smart Parking for Smart Ci<es
Ali Yavari *, Prem Prakash Jayaraman †, Dimitrios Georgakopoulos †
* RMIT University, Melbourne, Australia
† Swinburne University of Technology, Melbourne, Australia
mail@aliyavari.com

2. RMIT University - July 2015 2
In the late 1960s, communicaEon 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 poten0al 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 InternaEonal TelecommunicaEon Union (ITU) in 2005
More “things or objects” were connected to the Internet than people. 2008-2009 [Cisco]
1960 1980 1996 2000 2001

3. Cisco IBSG projecEons, UN Economic & Social Affairs h`p://www.un.org/esa/populaEon/publicaEons/longrange2/WorldPop2300final.pdf
6.307
6.721 6.894 7.347 7.83
0
10
20
30
40
50
2003 2008 2010 2015 2020
Billions of Devices
World PopulaEon
50
Billion
SmartObjects
Rapid adopEon rate of digital infrastructure
5 x faster than electricity & telephony
“Things” per person
InflecEon Point

4. 1.1 Billion
Data points generated by sensors daily
500 Gigabytes
Data generated by an offshore oil rig weekly
1000 Gigabytes
Data generated by an oil refinery daily
10,000 Gigabytes
Data generated by a jet engine every 30 minutes
2.5 Billion Gigabytes
Data generated worldwide daily
90% of the world’s data
Has been created in the last 2 years!
• Cisco IBSG projecEons, UN Economic & Social Affairs h`p://www.un.org/esa/populaEon/publicaEons/longrange2/WorldPop2300final.pdf

5. 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

6. IdenEfy
Communicate
Sense
Control

7. “The price of light is less than the cost of darkness.” – Arthur C Nielsen
Wisdom
Knowledge
Informa<on
Data
Process

10. Parking Space in a Smart City
• Direct drivers to empty parking spaces
– R. E. Barone, T. Giuffrè, S. M. Siniscalchi, M. A. Morgano, and G. Tesoriere, “Architecture for parking management in
smart ciEes,” IET Intell. Transp. Syst., vol. 8, no. 5, pp. 445–452, 2014.
– R. Lu, X. Lin, H. Zhu, and X. Shen, “SPARK: a new VANET-based smart parking scheme for large parking lots,” in
INFOCOM 2009, IEEE, 2009, pp. 1413–1421.
– R. Grodi, D. B. Rawat, and F. Rios-GuEerrez, “Smart parking: Parking occupancy monitoring and visualizaEon system
for smart ciEes,” in SoutheastCon 2016, 2016, pp. 1–5.
– Y. Zheng, S. Rajasegarar, and C. Leckie, “Parking availability predicEon for sensor-enabled car parks in smart ciEes,” in
Intelligent Sensors, Sensor Networks and Informa0on Processing (ISSNIP), 2015 IEEE Tenth Interna0onal Conference
on, 2015, pp. 1–6.
– J. Cherian, J. Luo, H. Guo, S.-S. Ho, and R. Wisbrun, “Poster: ParkGauge: Gauging the CongesEon Level of Parking
Garages with Crowdsensed Parking CharacterisEcs,” in Proceedings of the 13th ACM Conference on Embedded
Networked Sensor Systems, 2015, pp. 395–396.
– Z. Ji, I. Ganchev, M. O’Droma, L. Zhao, and X. Zhang, “A cloud-based car parking middleware for IoT-based smart
ciEes: design and implementaEon,” Sensors, vol. 14, no. 12, pp. 22372–22393, 2014.
• Provide an esEmate of average waiEng Eme to park
– P. R. de Almeida, L. S. Oliveira, A. S. Bri`o, E. J. Silva, and A. L. Koerich, “PKLot–A robust dataset for
– A. Koster, A. Oliveira, O. Volpato, V. Delvequio, and F. Koch, “RecogniEon and recommendaEon of parking places,” in
Ibero-American Conference on Ar0ficial Intelligence, 2014, pp. 675–685.
– J. Rico, J. Sancho, B. Cendon, and M. Camus, “Parking easier by using context informaEon of a smart city: Enabling fast
search and management of parking resources,” in Advanced Informa0on Networking and Applica0ons Workshops
(WAINA), 2013 27th Interna0onal Conference on, 2013, pp. 1380–1385.
– E. Akhavan-Rezai, M. F. Shaaban, E. El-Saadany, and F. Karray, “Online intelligent demand management of plug-in
electric
– Y. Geng and C. G. Cassandras, “New ‘Smart Parking’ system based on resource allocaEon and reservaEons,” IEEE
Trans. Intell. Transp. Syst., vol. 14, no. 3, pp. 1129–1139, 2013.

11. Parking Space in a Smart City
• We take into account each driver's context that may include:
– driver’s preferences (e.g., park in a covered parking space),
– driving experience (e.g., avoid narrow parking spaces),
– car’s locaEon (e.g., collected from the driver’s smart phone),
– vehicle’s properEes (e.g., vehicle type, length, height, etc.),
– other parking informaEon provided (e.g., the properEes of parking spaces,
such as shaded, covered, etc.).

12. Observe
Orient
Decision
AcEon

13. Internet-connected Devices
Connec<vity
Internet-scaled Data
Data Reduc<on
IoT Services and Applica<on
Presenta<on
Contextualisa<on

14. Context
Context or contextual informaEon is any informaEon about any enEty that
can be used to effecEvely reduce the amount of reasoning required (via
filtering, aggregaEon, and inference) for decision making within the scope
of specific applicaEons.

15. ContextualisaEon
• ContextualisaEon excludes irrelevant data from
consideraEon and has the potenEal to reduce data
from several aspects including volume, velocity, and
variety in IoT applicaEons
• ContextualisaEon of IoT data can help improve the
value of informaEon extracted from IoT
• ContextualisaEon improve the data processing and
knowledge extracEon in IoT applicaEons
Context CollecEon
ContextualisaEon
DisseminaEon of
the contextualised
data

16. • An approach to represent and contextualised data originaEng from IoT devices.
• A mechanism to efficiently query the contextualised IoT data
• An example of a smart parking space recommender applicaEon
• An experimental evaluaEon of the proposed contextualised IoT data querying
approach using syntheEc data generated from Melbourne city datasets h`ps://
data.melbourne.vic.gov.au/
Contextualised Service Delivery in the
Internet of Things

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

20. Conclusion
• Scalable and real-Eme contextualisaEon of IoT data has the potenEal to
significantly improve data processing for large scale IoT applicaEons in
Smart CiEes
• We proposed an approach to contextualise and query Internet scale IoT
data and we exemplify the approach via a smart parking space
recommender applicaEon for Smart CiEes.
• The experimental scenario in this paper illustrates that contextualisaEon
of IoT data reduces query Emes for IoT services (such as a smart parking
space recommender) by more than 3 Emes in comparison with a situaEon
where the query is contextualisaEon agnosEc.

21. Thank You!
mail@aliyavari.com