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Impact of wireless losses on the predictability of end-to-end flow characteristics in Mobile IP Networks

by Bhoite, Sameer Prabhakarrao

Abstract (Summary)
Technological advancements have led to an increase in the number of wireless and

mobile devices such as PDAs, laptops and smart phones. This has resulted in an ever-

increasing demand for wireless access to the Internet. Hence, wireless mobile traffic

is expected to form a significant fraction of Internet traffic in the near future, over

the so-called Mobile Internet Protocol (MIP) networks. For real-time applications,

such as voice, video and process monitoring and control, deployed over standard IP

networks, network resources must be properly allocated so that the mobile end-user

is guaranteed a certain Quality of Service (QoS). As with the wired and fixed IP

networks, MIP networks do not offer any QoS guarantees. Such networks have been

designed for non-real-time applications. In attempts to deploy real-time applications

in such networks without requiring major network infrastructure modifications, the

end-points must provide some level of QoS guarantees. Such QoS guarantees or QoS

control, requires ability of predictive capabilities of the end-to-end flow characteristics.

In this research network flow accumulation is used as a measure of end-to-end

network congestion. Careful analysis and study of the flow accumulation signal shows

that it has long-term dependencies and it is very noisy, thus making it very difficult

to predict. Hence, this work predicts the moving average of the flow accumulation

signal. Both single-step and multi-step predictors are developed using linear system

identification techniques. A multi-step prediction error of up to 17% is achieved for

prediction horizon of up to 0.5sec.

The main thrust of this research is on the impact of wireless losses on the ability to

predict end-to-end flow accumulation. As opposed to wired, congestion related packet

losses, the losses occurring in a wireless channel are to a large extent random, making

the prediction of flow accumulation more challenging. Flow accumulation prediction

studies in this research demonstrate that, if an accurate predictor is employed, the

increase in prediction error is up to 170% when wireless loss reaches as high as 15% ,

as compared to the case of no wireless loss. As the predictor accuracy in the case of

no wireless loss deteriorates, the impact of wireless losses on the flow accumulation

prediction error decreases.

Bibliographical Information:

Advisor:Parlos, Alexander G.; Pappu, Madhav; Jayasuriya, Suhada

School:Texas A&M University

School Location:USA - Texas

Source Type:Master's Thesis

Keywords:qos in mip networks mobile ip impact of wireless losses end to dynamics

ISBN:

Date of Publication:12/01/2004

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