Usage of 802.11n in practice: A measurement study

Mishra, Naman; Chaurasia, Avinash; Kallavi, Arun; Raman, Bhaskaran; Kulkarni, Purushottam

doi:10.1109/comsnets.2015.7098700

Cited by 7 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Passive measurement approaches. Deploying a network of sniers can enable collection of packet traces which could facilitate a passive inference of the inter-node connectivity in the network and activity of interfering nodes for a given STA [23][24][25]. Such insights can enable estimation and decomposition of WLAN uplink latency for all STAs that the sniers can sense.…”

Section: Related Workmentioning

confidence: 99%

uScope

Nayak

Knightly

2021

Proceedings of the Twenty-Second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Net

View full text Add to dashboard Cite

This paper presents uScope, an AP-side framework for validation of delay-based SLAs. Specically, uScope enables in the estimation of WLAN uplink latency for any of the associated STAs and decomposition into its constituent components. uScope does not require any form of active probing, no special purpose software installations on the STAs, nor any additional infrastructure to collect more information, and makes estimations solely based on passive AP-side observations. We implement uScope on a commodity hardware platform and conduct extensive eld trials on a university campus and in a residential apartment complex. In over 1 million tests, uScope demonstrates a high estimation accuracy with mean estimation errors under 10% for all the estimated parameters.

show abstract

Section: Related Workmentioning

confidence: 99%

uScope

Nayak

Knightly

2021

Proceedings of the Twenty-Second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Net

View full text Add to dashboard Cite

show abstract

“…The 802.11n can reach up to 600 Mbps speed rate that considerably 10 times better than the previous 802.11a/b/g standards(Mishra et al, 2015). The newest WLAN is 802.11ac standard that is upgrade of 802.11n.…”

mentioning

confidence: 98%

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

Salama¹

View full text Add to dashboard Cite

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.

show abstract