Design and Implementation of a Communication Protocol to Improve Multimedia QoS and QoE in Wireless Ad Hoc Networks

Santos, Diaz Zamora De; Ramón, Juan Antonio Corrales

doi:10.4995/thesis/10251/62162

Cited by 3 publications

(4 citation statements)

References 78 publications

(114 reference statements)

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“…The network used in this study is shown in Figure 1. It follows the hierarchical network design that divides the architecture into core, distribution and access tiers and conforms to the Open Source Interconnection (OSI) network model [13]. The wired part of the network contained the core tier and had a capacity of 10 Mbps.…”

Section: Methodsmentioning

confidence: 99%

Quality of service evaluation and assessment methods in wireless networks

Salama

Saatchi

Burke

2017

2017 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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Wireless networks are capable of facilitating a reliable multimedia communication. The ease they can be deployed is ideal for disaster management. The Quality of Service (QoS) for these networks is critical to their effectiveness. Evaluation of QoS in wireless networks provides information that supports their management. QoS evaluation can be performed in multiple ways and indicates how well applications are delivered. In this work, fuzzy c-means clustering (FCM) and Kohonen unsupervised neural networks were compared for their abilities to differentiate between Good, Average and Poor QoS for voice over IP (VoIP) traffic. Fuzzy inference system (FIS), linear regression and multilayer perceptron (MLP) were evaluated to quantify QoS for VoIP. FCM and Kohonen successfully classified VoIP traffic into three types representing Low, Medium, and High QoS. FIS, regression model and MLP combined the QoS parameters (i.e. delay, jitter, and percentage packet loss ratio) with information from the generated clusters and indicated the overall QoS.

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Section: Methodsmentioning

confidence: 99%

Quality of service evaluation and assessment methods in wireless networks

Salama

Saatchi

Burke

2017

2017 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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“…This design approach improves network management by ensuring its modularity [12]. The design was compliant with the Open Source Interconnection (OSI) network model [13]. The wired part of the network contained the core layer and had a capacity of 10 Mbps.…”

Section: Methodsmentioning

confidence: 99%

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Salama

Saatchi

Burke

2017

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)

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In order to evaluate the effectiveness of wired and wireless networks for multimedia communication, suitable mechanisms to analyse their traffic are needed. Sampling is one such mechanism that allows a subset of packets that accurately represents the overall traffic to be formed thus reducing the processing resources and time. In adaptive sampling, unlike fixed rate sampling, the sample rate changes in accordance with transmission rate or traffic behavior and thus can be more optimal. In this study an adaptive sampling technique that combines regression modelling and a fuzzy inference system has been developed. It adjusts the sampling according to the variations in the traffic characteristics. The method's operation was assessed using a computer network simulated in the NS-2 package. The adaptive sampling evaluated against a number of non-adaptive sampling gave an improved performance.

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“…Furthermore, quality can be reflected synonymous with predictability and reliability of the application. Santos (2016) has identified essential QoS parameters like bandwidth, application duration, maximum delay, maximum jitter and maximum loss rate.…”

Section: Network Traffic Parametersmentioning

confidence: 99%

“…There are many parameters allied to transmission of multimedia data Santos (2016). They are described in this section.…”

Section: Multimedia Trafficmentioning

confidence: 99%

Hybrid computer network quality of service and experience: development and evaluation

Salama¹

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Multimedia transmission over wired and wireless (hybrid) networks is increasingly needed as new services emerge and hybrid networks become more diverse and reliable. Quantifying quality of multimedia applications transmitted over hybrid networks is valuable for measuring network performance and its optimisation. For video, the process involves examining the images that make up the video, by quantifying distortion, noise, and complementing them with traffic parameters characterised by packet delay, delay variation (jitter) and percentage of packet loss ratio (%PLR). Processing all received packets to evaluate the quality of received application is computationally intensive. The study developed a new multi-input adaptive sampling method that allowed a subset of transmitted packets to be chosen according to variations in three synchronised traffic parameters inputs. The method integrated fuzzy logic and regression modelling of traffic parameters and adaptively adjusted the number of packets selected for processing. Statistical and neural networks methods were developed to evaluate quality of service (QoS) for video streaming and Voice over Internet Protocol (VoIP) transmitted over hybrid networks. The traffic parameters for QoS evaluations were delay, jitter and %PLR. The work involved, Bayesian classification and probabilistic neural network (PNN) based methods to process traffic parameters. QoS. This allocation conformed to the International Telecommunication Union (ITU) recommendations. Overall, the performance of Bayesian method was better than PNN when determining QoS for VoIP. In addition, the developed methods were successfully used in practical tests to analyse QoS in the wireless standards IEEE 802.11ac and IEEE 802.11n. QoS reflects provides information that indicates the extent the traffic parameters for an application are within the expected bounds. However, the user's perception of the received application is also relevant. This evaluation can be performed through quality of experience (QoE) analysis. For video, QoE considers issues such as image distortion and noise that in this study were quantified by structural similarity index measure (SSIM), peak signal to noise ratio (PSNR) and image difference (ID). A modular fuzzy logic-based system that individually determined QoS and QoE, then combined them to determine the overall quality of a wirelessly transmitted video was developed. The performance of the devised video quality evaluation system was compared against the subjective evaluation performed by 25 participants (i.e. mean opinion scores) and consistent results were observed. A further evaluation of the video quality evaluation system was carried by comparing its results against a recently reported video quality assessment method known as the spatial efficient entropic variation quality assessment. Again, comparable results were obtained between the two methods. The QoE evaluations were carried out both in a network laboratory and over an institutional network. The study resulted in development a multi-input adaptive sampling method and artificial intelligence and statistical based QoS and QoE evaluation methods. The proposed schemes improved the QoS and QoE assessments for multimedia applications. The devised adaptive sampling model in comparison with random, stratified and systematic non-adaptive sampling methods was more effective as it represented the traffic more precisely. The developed two probabilistic QoS methods showed consistency in their classifications. Both models successfully classified the received VoIP packets into their corresponding low, medium, and high QoS types. Furthermore, QoE with image partitioning approach has improved QoE evaluation as partitioned image approach provided more accurate results than full image approach. The proposed integration approach of three multimedia parameters SSIM, PSNR and ID improved accuracy of overall QoE assessments compared to single parameter approaches.

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Design and Implementation of a Communication Protocol to Improve Multimedia QoS and QoE in Wireless Ad Hoc Networks

Cited by 3 publications

References 78 publications

Quality of service evaluation and assessment methods in wireless networks

Quality of service evaluation and assessment methods in wireless networks

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Hybrid computer network quality of service and experience: development and evaluation

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