Research Article | OPEN ACCESS
An Efficient AI Based Approach for Multimedia Traffic Management in Wireless Network
1Khalid Hussain, 1Saleem Iqbal, 2Sohail Asghar and 1Abdul Hanan Abdullah
1Universiti Teknologi Malaysia, Malaysia
2University Institute of Information Technology PMAS-ARID Agriculture University,
Rawalpindi, Pakistan
Research Journal of Applied Sciences, Engineering and Technology 2013 4:551-556
Received: December 20, 2012 | Accepted: April 12, 2013 | Published: June 20, 2013
Abstract
This study presents Artificial Intelligence based channel estimation and monitoring technique call AI-Monitoring System (AIMS), for integration of Multimedia traffic in wireless network. Through AIMS technique every node in the network has the capability to monitor the neighbour node transmission and also before sending the traffic. The sending node evaluates the SNR ratio as primary parameter and queue limit of the receiving node as secondary. With the help of AIMS every node has the pre-determined information about the selected channel as well as node. Based on conditional and distribution probability model, the proposed Bay Estimator model analyses the SNR ratio before forwarding the multimedia traffic on selected path. We determine the performance of our proposed technique by obtaining the recursive analysis matrix methodology. Amendment of pre-distinct parameters make us capable to maintain the quality of service, multipath adaptability for attack prevention, as well as minimize the packet loss ratio.
Keywords:
Artificial intelligence, multimedia, noise ratio, signal to, traffic management,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|