Preparing Wi-Fi Networks for Novel Services in Smart Infrastructure

Morshedi, Maghsoud

doi:10.1109/fmec49853.2020.9144819

Cited by 2 publications

(1 citation statement)

References 15 publications

(13 reference statements)

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“…Likewise, Bhattacharyya et al attempted to involve clients by measuring QoE and reporting to the software defined network (SDN) access point (AP) to apply queue prioritization for serving high-priority streaming requests in a network and consequently to improve the QoE by using reinforcement learning [ 5 ]. On the contrary, Morshedi presented a conceptual model to assess the service acceptability in the form of PQoS, which only uses network performance parameters [ 6 ]. Hence, it is desirable to distinguish between methods that use only technical factors and those leveraging user surveys to consider general opinion by using the term PQoS and QoE, respectively.…”

Section: Introductionmentioning

confidence: 99%

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Morshedi

Noll

2021

Sensors

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Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

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Section: Introductionmentioning

confidence: 99%

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Morshedi

Noll

2021

Sensors

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Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

Morshedi

Noll

2021

Future Internet

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Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

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Preparing Wi-Fi Networks for Novel Services in Smart Infrastructure

Cited by 2 publications

References 15 publications

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

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