Cart (Loading....) | Create Account
Close category search window
 

Spatiotemporal Event Detection in Mobility Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Au, T.S. ; AT&T Lab. Res., Florham Park, NJ, USA ; Rong Duan ; Heeyoung Kim ; Guang-Qin Ma

Learning and identifying events in network traffic is crucial for service providers to improve their mobility network performance. In fact, large special events attract cell phone users to relative small areas, which causes sudden surge in network traffic. To handle such increased load, it is necessary to measure the increased network traffic and quantify the impact of the events, so that relevant resources can be optimized to enhance the network capability. However, this problem is challenging due to several issues: (1) Multiple periodic temporal traffic patterns (i.e., nonhomogeneous process) even for normal traffic, (2) Irregularly distributed spatial neighbor information, (3) Different temporal patterns driven by different events even for spatial neighborhoods, (4) Large scale data set. This paper proposes a systematic event detection method that deals with the above problems. With the additivity property of Poisson process, we propose an algorithm to integrate spatial information by aggregating the behavior of temporal data under various areas. Markov Modulated Nonhomogeneous Poisson Process (MMNHPP) is employed to estimate the probability with which event happens, when and where the events take place, and assess the spatial and temporal impacts of the events. Localized events are then ranked globally for prioritizing more significant events. Synthetic data are generated to illustrate our procedure and validate the performance. An industrial example from a telecommunication company is also presented to show the effectiveness of the proposed method.

Published in:

Data Mining (ICDM), 2010 IEEE 10th International Conference on

Date of Conference:

13-17 Dec. 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.