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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2013 Vol.3(4): 417-423 ISSN: 2010-3689
DOI: 10.7763/IJIET.2013.V3.311

A Data Driven Model for the Detection of Random Waypoint

Ting Wang and Chor Ping Low
Abstract—Locational data are extremely useful resource to study customer behavior and mobility patterns. In this paper, beyond directly measuring how their location, velocity and acceleration change over time, we extend our discussion to construct a data driven model to quantitatively evaluate the moving objects’ interests and intentions, which are represented by their waypoints distributions. Waypoints are defined with the Random Waypoint (RWP) mobility model, which is one of the most commonly used models in mobility management. To effectively deploy RWP model, the detection of accurate waypoint distribution is crucial and, however, challenging in most practical situations. Moreover, to understand the how and why an object moves in a its specific pattern, the knowledge of waypoint distribution could be valuable in many use cases. In this work, we analytically derive the relationship between waypoint distribution and the locational data that could be obtained directly from sensors, such as the number of objects’ arrivals to a particular area. An estimation scheme using supervised learning algorithm is proposed to simplify the evaluation of our model. Simulations are carried out to verify the correctness and accuracy of our proposed scheme.

Index Terms—Locational data, mobility management, waypoint distribution, supervised learning.

Wang Ting is now with SAP Asia Ptd Ltd, Singapore (e-mail: dean.wang@sap.com).
Chor Ping Low is with School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore (e-mail: icplow@ntu.edu.sg).

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Cite:Ting Wang and Chor Ping Low, "A Data Driven Model for the Detection of Random Waypoint," International Journal of Information and Education Technology vol. 3, no. 4, pp. 417-423, 2013.

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