Subscribe Now Subscribe Today
Science Alert
 
FOLLOW US:     Facebook     Twitter
Blue
   
Curve Top
Information Technology Journal
  Year: 2014 | Volume: 13 | Issue: 16 | Page No.: 2581-2587
DOI: 10.3923/itj.2014.2581.2587
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail
Adaptive Traffic Offloading Method Based on OWN in Overlay Networks with LTE and WLAN
Yangchun Li, Zhaoming Lu and Xiangming Wen

Abstract:
Traffic overload problem in the cellular networks has become crucial. In order to approach the problem of increasing traffic, an adaptive traffic offloading method based on a combination of Open Wireless Networks (OWN) and a biologically inspired attractor selection model is proposed in this study. This method provides a pragmatic way to keep load balance and reduce selfishness. Thus, it can improve the overall performance in overlay networks with 3GPP Long Term Evolution (LTE) and Wireless Local Area Networks (WLAN). Firstly, based on discrete Markov Modulated Poisson Process (dMMPP), a queuing model is considered to obtain the real-time network status in both LTE and WLAN. Secondly, by following the OWN architecture to develop the attractor selection model for offloading at the right time in order to accommodate unpredictable traffic demands in fluctuating environment. The whole wireless network can be managed in an autonomous, load balancing and robust manner by the global control which is under OWN and the autonomous decision of attractor selection. The simulation results demonstrate that the proposed mechanism can reach a tradeoff between the system delay and the handover times by realizing efficient traffic offloading.
PDF Fulltext XML References Citation Report Citation
How to cite this article:

Yangchun Li, Zhaoming Lu and Xiangming Wen, 2014. Adaptive Traffic Offloading Method Based on OWN in Overlay Networks with LTE and WLAN. Information Technology Journal, 13: 2581-2587.

DOI: 10.3923/itj.2014.2581.2587

URL: https://scialert.net/abstract/?doi=itj.2014.2581.2587

 
COMMENT ON THIS PAPER
 
 
 

 

 
 
 
 
 
 
 
 
 

 
 
 
 
 

       

       

Curve Bottom