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

Abstract

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.

Keywords

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.

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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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