Quality of service evaluation and assessment methods in wireless networks

Salama, Abdussalam; Saatchi, Reza; Burke, Derek

doi:10.1109/ict-dm.2017.8275684

Cited by 6 publications

(9 citation statements)

References 17 publications

(20 reference statements)

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“…Low QoE (i.e., QoE 33%) corresponded to PSNR < 25, SSIM ≤ 0.88 and ID ≥ 0.66. These boundaries conform to the values for PSNR and SSIM in previous studies [12,13,17,18,34].…”

Section: Mechanism Determining Video Qualitysupporting

confidence: 88%

“…Medium QoS (i.e., 33% < QoS 67%) corresponds to 150 < delay < 400 ms, 10 < jitter < 20 ms and 1% < packet loss ratio < 2%. Low QoS (i.e., QoS < 33%) corresponds to delay >400 ms, jitter >20 ms and packet loss ratio >2% [18].…”

Section: Wireless Network Set Upmentioning

confidence: 99%

“…A FIS uses fuzzy logic concepts to map its numerical input values through a reasoning process to its output. FIS is an effective mean of analysing computer network data [18,19]. The structure of a FIS is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Salama¹,

Saatchi²

2019

Technologies

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Video transmission over wireless computer networks is increasingly popular as new applications emerge and wireless networks become more widespread and reliable. An ability to quantify the quality of a video transmitted using a wireless computer network is important for determining network performance and its improvement. The process requires analysing the images making up the video from the point of view of noise and associated distortion as well as traffic parameters represented by packet delay, jitter and loss. In this study a modular fuzzy logic based system was developed to quantify the quality of video transmission over a wireless computer network. Peak signal to noise ratio, structural similarity index and image difference were used to represent the user's quality of experience (QoE) while packet delay, jitter and percentage packet loss ratio were used to represent traffic related quality of service (QoS). An overall measure of the video quality was obtained by combining QoE and QoS values. Systematic sampling was used to reduce the number of images processed and a novel scheme was devised whereby the images were partitioned to more sensitively localize distortions. To further validate the developed system, a subjective test involving 25 participants graded the quality of the received video. The image partitioning significantly improved the video quality evaluation. The subjective test results correlated with the developed fuzzy logic approach. The video quality assessment developed in this study was compared against a method that uses spatial efficient entropic differencing and consistent results were observed. The study indicated that the developed fuzzy logic approaches could accurately determine the quality of a wirelessly transmitted video.

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“…Low QoE (i.e., QoE 33%) corresponded to PSNR < 25, SSIM ≤ 0.88 and ID ≥ 0.66. These boundaries conform to the values for PSNR and SSIM in previous studies [12,13,17,18,34].…”

Section: Mechanism Determining Video Qualitysupporting

confidence: 88%

Section: Wireless Network Set Upmentioning

confidence: 99%

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Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Salama¹,

Saatchi²

2019

Technologies

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“…Therefore, a subset of packets needs to be selected such that the number of packets in the subset is significantly smaller than the total number of transmitted packets while retaining the original traffic's attributes. This operation is called sampling and plays an important role in evaluating a multimedia network's performance [11][12][13]. Sampling can be performed adaptively or in a nonadaptive manner.…”

Section: Introductionmentioning

confidence: 99%

“…Data curve fitting is based on two functions-the polynomial evaluation function and the polynomial curve fitting function. The general formula for a polynomial is shown in Equation (12).…”

mentioning

confidence: 99%

Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

2018

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Organisations such as hospitals and the public are increasingly relying on large computer networks to access information and to communicate multimedia-type data. To assess the effectiveness of these networks, the traffic parameters need to be analysed. Due to the quantity of the data packets, examining each packet's transmission parameters is not practical, especially in real time. Sampling techniques allow a subset of packets that accurately represents the original traffic to be examined and they are thus important in evaluating the performance of multimedia networks. In this study, an adaptive sampling technique based on regression and a fuzzy inference system was developed. The technique dynamically updates the number of packets sampled by responding to the traffic's variations. Its performance was found to be superior to the conventional nonadaptive sampling methods.

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Quality of Service in IEEE 802.11ac and 802.11n Wireless Protocols with Applications in Medical Environments

Salama

Saatchi

2020

Advances in Asset Management and Condition Monitoring

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Wireless computer networks are increasingly important as reliable means of communication in medical environments. Evaluation of Quality of Service (QoS) in wireless computer networks deployed in medical environments can improve network performance and enhance utilization of resources. In this study, the QoS offered by IEEE 802.11n and IEEE 802.11ac wireless protocols was evaluated and compared using multiple point-to-point links for Voice Over Internet Protocol (VoIP) traffic. QoS was evaluated based on Predictive Statistical Diagnosis (PSD) and Probabilistic Neural Network (PNN). PSD and PNN based QoS evaluation methods categorized the VoIP packets into low, medium and high QoS types based on the packets' transmission delay, jitter, and percentage packet loss ratio. Both PSD and PNN allowed QoS for VoIP to be quantified accurately. It was shown that 802.11ac provides a higher QoS for VoIP transmission as compared with IEEE 802.11n. The devised methods can be used in medical environments for evaluation of wireless networks' QoS.

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Quality of service evaluation and assessment methods in wireless networks

Cited by 6 publications

References 17 publications

Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

Quality of Service in IEEE 802.11ac and 802.11n Wireless Protocols with Applications in Medical Environments

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