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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.

Internet of Things (IoT) is an emerging technology that expands wireless and mobile networks into heterogeneous network of connected devices. Trustable remote monitoring and management systems are required to establish a controlled environment for new services and devices in order to (i) improve the quality of existing services and (ii) enable novel services. However, monitoring and remote management can cause security and privacy concerns and thus affect the trust formation between customer and service provider. This paper introduces a trust model considering institutions as mediators to assess trustability of remote monitoring and management systems. The proposed model considers governance as an approach to audit remote monitoring and management systems and accordingly provides institutional assurance in form of certificate or labels in order to facilitate trust decision making and motivate trustworthy behaviors. The proposed model utilized the multi-metric method to measure governance criteria objectively and represent level of trustworthiness with A-F labels. Representing governance criteria with labels accompanied by color coding facilitates trust decision making based on application context or requirements for everyone regardless of level of expertise. Meanwhile, issuing trustworthiness certificate or A-F labels will encourage service providers to improve trustability of their remote monitoring and management approaches, which improve acceptability and efficiency of managed services.

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.

The growing number of Wi-Fi networks at homes, buildings and public areas can consume excessive energy and therefore increase CO2 emissions. Hence, the aim of this paper is to investigate the energy consumption of Wi-Fi networks in general. This study is based on real-world observations both usage and energy consumption of Wi-Fi access points. A variety of off-the-shelf Wi-Fi access points were examined to investigate to what degree clients' usage patterns, especially the signal to noise ratio (SNR) affects the energy consumption. The results indicate that low SNR increases the energy consumption up to 136% in various Wi-Fi access points. Therefore, confining clients with a low level of SNR can reduce the energy consumption of Wi-Fi access points while increases network throughput. Hence, findings of this paper can improve the energy efficiency of Wi-Fi networks in particular energy and cost efficiency of public hotspots. Index Terms-Energy efficiency; Wi-Fi networks; energy monitoring; power consumption; green computing.

Wi-Fi will be the preferred access network in smart infrastructure, which is a considerably cheaper alternative of mobile broadband. Emerging services such as Internet of things (IoT), virtual reality (VR) and ehealth, which require carriergrade quality have shifted data traffic. Therefore, smart infrastructures need an extensive analysis of application requirements and user expectations. This paper presents the concept of cumulative network parameter monitoring and analysis in order to improve overall Wi-Fi quality in smart infrastructure. The proposed concept incorporates security and privacy in addition to generic performance parameters. The cumulative network parameters monitoring and analysis investigates various parameters in order to assess overall quality rather than individual performance parameter monitoring for a particular service. Hence, cumulative network parameter monitoring and analysis concept can establish a baseline to estimate user acceptability objectively rather than costly subjective assessments.