Active Queue Management (AQM) is most commonly used in wired networks, principally in backbone routers where packet loss is due to network congestion. In this work we extend AQM to wireless networks. In particular, a new priority queue algorithm is introduced which helps to manage both control and routing packets in the wireless network. This priority queue algorithm offers an improvement in QoS for some services; specifically for TCP traffic and real time services that are sensitive to the loss of control and routing packets. Key AQM schemes (RED, REM, AVQ, Blue and RIO) are extended to incorporate the priority queue algorithm. The behaviour and performance of the augmented schemes is then examined through simulation.
Abstract-Congestion management is a key factor in the provision of acceptable levels of quality of service (QoS) on wired networks. However, the concept of congestion management does not translate easily into the wireless domain. Active Queue Management (AQM) solutions for congestion avoidance have proved effective in wired networks but have not gained much traction in the wireless world. Wireless AQM schemes need to be capable of being easily scaled in order to maintain the algorithm characteristics and improve their efficiency. The RED, REM and BLUE AQM schemes are considered from a wireless perspective and methods for improving their efficiency and performance in wireless networks are given.
Abstract-Quality-oriented network service provisioning can take place at the network level using estimates of Intrinsic Quality of Service (IQoS) parameters or at the user level through measurements of the end-user Quality of Experience (QoE). While IQoS parameters are quantitative and measurement based, QoE estimates are more difficult to obtain as they usually rely on subjective end-user reporting.A new metric for the instantaneous estimation of QoE is proposed, expected Quality of Service (eQoS). This Perceived Quality of Service metric is calculated using IQoS parameters. eQoS estimates the QoE of common real time services for mobile devices (e.g. smartphones, tablets): Voice of IP via Constant Bit Rate, Audio and Video streaming via Variable Bit Rate.The efficiency of the proposed eQoS metric is evaluated via a realisation of an infrastructure-based wireless network. Unlike existing QoE metrics, eQoS provides an instantaneous estimate of the perceived QoS thereby establishing eQoS as an essential parameter for inclusion in future traffic management algorithms.
Abstract-One of the more obvious ways to reduce the volume of data traffic on cellular networks is through the use of handover to fixed networks via WiFi and other radio channels. With the growing focus on emerging 5G concepts and technologies, there has been a corresponding focus on the functional mechanisms needed to achieve this handover in a timely fashion. Much less attention has been paid to the practicalities, in terms of ensuring that the end-user experiences little or no loss in the quality of their network services when the handover occurs. In this paper, a methodology for managing such handover traffic in a WiFi network is proposed. The approach integrates and leverages aspects of three quality control mechanisms to enable stable, higher quality delivery of enhanced WiFi network services. It combines i) information adduced from a theoretical model with ii) a low complexity Quality of Experience metric that is quick and easy to estimate and iii) a queue management scheme.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.