SmartData pp 139-148 | Cite as

A Distributed Mobile Application for Data Collection with Intelligent Agent Based Data Management Policy

  • Marek Laskowski
  • Bryan C. P. Demianyk
  • Robert D. McLeod
Conference paper


This chapter presents a potential application area for SmartData (Tomko GJ, Kwan H, Borrett D. SmartData: The need, the goal, the challenge. Report, University of Toronto, Identify Privacy & Security Institute, 2012) research of importance in the near future. Technological and sociopolitical trends augur for development and adoption of a mobile distributed system for personal data collection and storage that incorporates the ideals of Privacy by Design (Tomko GJ, Kwan H, Borrett D. SmartData: The need, the goal, the challenge. Report, University of Toronto, Identify Privacy & Security Institute, 2012). Such a system will necessarily encompass a comprehensive interface which implements a complex data privacy, security, and sharing policy. This privacy management and sharing policy for distributed sensing participants represents a potential early embodiment for SmartData agents with unprecedented importance. Furthermore, distributed systems such as these form a convenient population of individuals embedded within the environment in order to exploit parallelism for crowd-sourced distributed learning. Such populations of participating users and their devices represent an intriguing opportunity to collaboratively develop a test-bed for the training and validation of SmartData agents directly within the target environment. Such embodiment and embeddedness within the 3D environment of the real-world forming a “mobile cloud” of Pervasive Internet devices is complementary to and converges with the vision of SmartData agents operating in virtual 3D online environments. A possible simulation test-bed for gaining insight into evolutionary dynamics in such a distributed learning context is discussed.


Sensor Network Payoff Period Intelligent Agent Intelligent Transportation System Mobile Cloud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Sutter H, Welcome to the jungle. Accessed 17 July 2012
  2. 2.
    Uhrmacher A, Weyns D, Editors (2009) Multi-Agent Systems: Simulation and Applications. CRC Press, New York 296Google Scholar
  3. 3.
    Maas P, Rojagopalan M (2012) “That’s no phone. That’s my tracker.” The New York Times, Published online July 13, 2012. Accessed 17 July 2012
  4. 4.
    Davenport C (2012) “Breach of new EU online data rules to carry high fines.” Thomson 300 Reuters, Published online January 25, 2012. Accessed 17 July 2012
  5. 5.
    White House Report (2012) Consumer data privacy in a networked world: a framework for protecting privacy and promoting innovation in the global digital economy. Accessed 17 July 2012
  6. 6.
    Gruman G (2012) “The next consumerization revolution: Your personal data.” Infoworld, published online June 15, 2012. Accessed 17 July 2012
  7. 7. Accessed 17 July 2012
  8. 8.
    World Economic Forum (2011) Personal data: the emergence of a new asset class. Accessed 17 July 2012
  9. 9.
    Laskowski M, McLeod RD, Friesen MR, Podaima BW, Alfa AS (2009) Models of emergency departments for reducing patient waiting times. PLoS ONE, vol. 4, no. 7: e6127, 2009. doi: 10.1371/journal.pone.0006127 2009
  10. 10.
    Laskowski M, Demianyk BCP, Benavides J, Friesen MR, McLeod RD, Mukhi SN, Crowley M (2012) Extracting Data from Disparate Sources for Agent-Based Disease Spread Models. Epidemiology Research International, vol. 2012, Article ID 716072, 18 pages, 2012. doi: 10.1155/2012/716072
  11. 11.
    Kermak WO, McKendrick AG, A contribution to the mathematical theory of epidemics, Proc R Soc Lond B 1927; 115: 700–721CrossRefGoogle Scholar
  12. 12.
    Laskowski M., Mostaco-Guidolin LC, Greer AL, Wu J, Moghadas SM (2011) The impact of demographic variables on disease spread: Influenza in remote communities Scientific Reports, 1, art. no. 105Google Scholar
  13. 13.
    Wahle J, Bazzan ALC, Klügl F, Schreckenberg M (2002) The impact of real-time information in a two-route scenario using agent-based simulation, Transportation Research Part C: Emerging Technologies, Volume 10, Issues 5–6, October–December 2002, Pages 399–417, doi: 10.1016/S0968-090X(02)00031-1
  14. 14.
    Grasselli MR, Ismail ORH (2013) An agent-based computational model for bank formation and interbank networks, Handbook on Systemic Risk, J.-Pierre. Fouque and J. Langsam (eds), Cambridge University PressGoogle Scholar
  15. 15.
    Demianyk B, Sandison D, Libbey B, Guderian R, McLeod RD, Eskicioglu MR, Friesen MR, Ferens K, Mukhi S (2010) Technologies to generate contact graphs for personal social networks. e-Health Networking Applications and Services (Healthcom), 2010 12th IEEE International Conference on, pp. 15–22, 1-3 July 2010. doi:  10.1109/HEALTH.2010.5556576
  16. 16.
    Tomko GJ, Kwan H, Borrett D (2012) SmartData: The need, the goal, the challenge. Report, University of Toronto, Identify Privacy & Security InstituteGoogle Scholar
  17. 17.
  18. 18.
    Eiben AE et al (1994) Genetic algorithms with multi-parent recombination. PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78–87Google Scholar
  19. 19.
    Dawkins R (1976) The selfish gene. Oxford University Press, OxfordGoogle Scholar
  20. 20.
    Benavides J, Demianyk BCP, Mukhi SN, Laskowski M, Friesen MR, and McLeod RD (2012) Smartphone Technologies for Social Network Data Generation, Journal of Medical and Biological Engineering, 32, 4, 235–244, 2012Google Scholar
  21. 21.
    Statistics Canada. 2007. 2006 Community Profiles. 2006 Census. Statistics Canada Catalogue no. 92-591-XWE. Ottawa. Released March 13, 2007Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Marek Laskowski
    • 1
  • Bryan C. P. Demianyk
    • 2
  • Robert D. McLeod
    • 2
  1. 1.York UniversityTorontoCanada
  2. 2.University of ManitobaWinnipegCanada

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