A Data Driven Model for the Detection of Random Waypoint  

Oleh:

Ting, Wang ; Chor, Ping Low

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: International Journal of Information and Education Technology vol. 03 no. 04 (Aug. 2013), page 417-423.
Topik: Locational data; mobility management; waypoint distribution; supervised learning
Fulltext: 03_04_03_Wang_Low.pdf (1.36MB)

Isi artikel

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.

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