Multimedia computer networks quality of service techniques evaluation and development.

DOGMAN, Aboagela A. (2014). Multimedia computer networks quality of service techniques evaluation and development. Doctoral, Sheffield Hallam University (United Kingdom)..

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The growth in the transmission of time-sensitive applications over computer networks means that Quality of Service (QoS) needs to be managed in an efficient manner. Network QoS management in this thesis refers to evaluation and improvement of QoS provided by integrated wired and wireless computer networks. Evaluation of QoS aims to analyse and quantify network performance with respect of meeting multimedia applications' transmission requirements. QoS improvement involves the ability to take actions to change network performance toward improved operation. Therefore, the main aims of this thesis are: (i) to develop techniques for evaluation QoS in multimedia computer networks, (ii) to develop techniques that uses the information from (i) to manage and improve network performance. Multimedia traffic generates a large amount of data. Collecting this information poses a challenge as it needs to be sufficiently fast and accurate. A contribution of this thesis is that adaptive statistical sampling techniques to sample multimedia traffic were developed and their effectiveness was evaluated. Three different adjustment mechanisms were incorporated into statistical sampling techniques to adjust the traffic sampling rate: simple linear adjustment, quarter adjustment, and Fuzzy Inference System (FIS). The findings indicated that the developed methods outperformed the conventional non-adaptive sampling methods of systematic, stratified and random. The data collected included important QoS parameters, i.e. delay, jitter, throughput, and packet loss that indicated network performance in delivering real-time applications. An issue is that QoS needs evaluation in an informative manner. Therefore, the second contribution of this thesis is that statistical and Artificial Intelligent (AI) techniques were developed to evaluate QoS for multimedia applications. The application's QoS parameters were initially analysed either by Fuzzy C-Means (FCM) clustering algorithm or by Kohonen neural network. The analysed QoS parameters were then used as inputs to a regression model or Multi-Layer Perceptron (MLP) neural network in order to quantify the overall QoS. The proposed QoS evaluation system differentiated the network's QoS into a number of levels (Poor to Good QoS) and based on this information, the overall network's QoS was successfully quantified. In order to facilitate QoS assessment, a portable hand-held device for assessing the QoS in multimedia networks was designed, regression model was implemented on the microcontroller board and its performance was successfully demonstrated.Multimedia applications transmitted over computer networks require a large bandwidth that is a critical issue especially in wireless networks. The challenge is to enable end-to-end QoS by providing different treatments for different classes of traffic and efficient use of network resources. In this thesis, a new QoS enhancement scheme for wireless-wired networks is developed. This scheme consisted of an adaptive traffic allocation algorithm that is incorporated into the network's wireless side to improve the performance of IEEE 802.11e Enhanced Distributed Channel Access (EDCA) protocol, and a Weighted Round Robin (WRR) queuing scheduling mechanism that was incorporated into the wired side. The proposed scheme improved the QoS for Multimedia applications. The average QoS for voice, and video applications were increased from their original values by 72.5%, and 70.3% respectively.

Item Type: Thesis (Doctoral)
Thesis advisor - Saatchi, Reza [0000-0002-2266-0187]
Thesis advisor - Al-Khayatt, Samir
Additional Information: Thesis (Ph.D.)--Sheffield Hallam University (United Kingdom), 2014.
Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
Depositing User: EPrints Services
Date Deposited: 10 Apr 2018 17:19
Last Modified: 03 May 2023 02:06

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