Quality of Service Evaluation and Assessment Methods in Wireless Networks

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

Probabilistic classification of quality of service in wireless computer networks

There is an increasing reliance on wireless computer networks for communicating various types of time sensitive applications such as voice over internet protocol (VoIP). Quality of service (QoS) can play an important role in wireless computer networks as it can facilitate evaluation of their performance and can provide mechanisms to improve their operation. In this study probabilistic neural network (PNN) and Bayesian classification were developed to process delay, jitter and percentage packet loss ratio for VoIP traffic. Both methods successfully categorized the transmission of VoIP packets into low, medium and high QoS categories but overall the Bayesian approach performed more accurately than PNN. By accurately determining the network's QoS, an improved understanding of its performance is obtained

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

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

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

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