Shrewd Selection Speeds Surfing: Use Smart EXP3!

In this paper, we explore the use of multi-armed bandit online learning techniques to solve distributed resource selection problems. As an example, we focus on the problem of network selection. Mobile devices often have several wireless networks at their disposal. While choosing the right network is vital for good performance, a decentralized solution remains a challenge. The impressive theoretical properties of multi-armed bandit algorithms, like EXP3, suggest that it should work well for this type of problem. Yet, its real-word performance lags far behind. The main reasons are the hidden cost of switching networks and its slow rate of convergence. We propose Smart EXP3, a novel bandit-style algorithm that (a) retains the good theoretical properties of EXP3, (b) bounds the number of switches, and (c) yields significantly better performance in practice. We evaluate Smart EXP3 using simulations, controlled experiments, and real-world experiments. Results show that it stabilizes at the optimal state, achieves fairness among devices and gracefully deals with transient behaviors. In real world experiments, it can achieve 18% faster download over alternate strategies. We conclude that multi-armed bandit algorithms can play an important role in distributed resource selection problems, when practical concerns, such as switching costs and convergence time, are addressed.Comment: Full pape

A nation-wide wi-fi RSSI dataset : Statistical analysis and resulting insights.

We present a dataset collected during ten months from a network comprising approximately 9500 double-band Access Points (APs), corresponding to Uruguay’s nation-wide one-to-one computing program’s internet provider. The dataset includes the transmission power, used channel and measured RSSI (Radio Signal Strength Indicator) that each AP senses every other AP in sight, with a granularity of an hour. This results in a total of more than 750 million measurements, one of the largest Wi-Fi datasets to date. In the study of this dataset we have first focused on a linklevel analysis. Our contributions are fourfold. We verify that approximately only half of the RSSI time-series are actually stationary, and that in that case, they present strong time correlations. Moreover, the typical assumption that the channel is symmetrical is not true, even in the long-term, and we show that interference plays an important role on this asymmetry. Finally, we study attenuation in the 5 GHZ band and show that its upper section is prone to larger attenuation than what is predicted by classic models. The practical consequences of these observations are discussed throughout the article. We also present networklevel indicators of the system (such as number of neighbors per AP and interference level). These are particularly useful for simulating a planned network such as the one discussed here

The Design and Implementation of a Wireless Video Surveillance System.

Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed offline with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traffic scheduling algorithms further optimize Vigil's bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between five and 200 times greater than an approach that simply streams video over the wireless network. For a fixed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a user's query

Predicting the effect of home Wi-Fi quality on Web QoE

International audienceWi-Fi is the preferred way of accessing the Internet for many devices at home, but it is vulnerable to performance problems. The analysis of Wi-Fi quality metrics such as RSSI or PHY rate may indicate a number of problems, but users may not notice many of these problems if they don't degrade the performance of the applications they are using. In this work, we study the effects of the home Wi-Fi quality on Web browsing experience. We instrument a commodity access point (AP) to passively monitor Wi-Fi metrics and study the relationship between Wi-Fi metrics and Web QoE through controlled experiments in a Wi-Fi testbed. We use support vector regression to build a predictor of Web QoE when given Wi-Fi quality metrics available in most commercial APs. Our validation shows root-mean square errors on MOS predictions of 0.6432 in a controlled environment and of 0.9283 in our lab. We apply our predictor on Wi-Fi metrics collected in the wild from 4,880 APs to shed light on how Wi-Fi quality affects Web QoE in real homes

Predicting the effect of home Wi-Fi quality on QoE

International audiencePoor Wi-Fi quality can disrupt home users' internet experience, or the Quality of Experience (QoE). Detecting when Wi-Fi degrades QoE is extremely valuable for residential Internet Service Providers (ISPs) as home users often hold the ISP responsible whenever QoE degrades. Yet, ISPs have little visibility within the home to assist users. Our goal is to develop a system that runs on commodity access points (APs) to assist ISPs in detecting when Wi-Fi degrades QoE. Our first contribution is to develop a method to detect instances of poor QoE based on the passive observation of Wi-Fi quality metrics available in commodity APs (e.g., PHY rate). We use support vector regression to build predictors of QoE given Wi-Fi quality for popular internet applications. We then use K-means clustering to combine per-application predictors to identify regions of Wi-Fi quality where QoE is poor across applications. We call samples in these regions as poor QoE samples. Our second contribution is to apply our predictors to Wi-Fi metrics collected over one month from 3479 APs of customers of a large residential ISP. Our results show that QoE is good most of the time, still we find 11.6% of poor QoE samples. Worse, approximately 21% of stations have more than 25% poor QoE samples. In some cases, we estimate that Wi-Fi quality causes poor QoE for many hours, though in most cases poor QoE events are short

Predicting the effect of home Wi-Fi quality on QoE: Extended Technical Report

Poor Wi-Fi quality can disrupt home users' internet experience, or the Quality of Experience (QoE). Detecting when Wi-Fi degrades QoE is extremely valuable for residential Internet Service Providers (ISPs) as home users often hold the ISP responsible whenever QoE degrades. Yet, ISPs have little visibility within the home to assist users. Our goal is to develop a system that runs on commodity access points (APs) to assist ISPs in detecting when Wi-Fi degrades QoE. Our first contribution is to develop a method to detect instances of poor QoE based on the passive observation of Wi-Fi quality metrics available in commodity APs (e.g., PHY rate). We use support vector regression to build predictors of QoE given Wi-Fi quality for popular internet applications. We then use K-means clustering to combine per-application predictors to identify regions of Wi-Fi quality where QoE is poor across applications. We call samples in these regions as poor QoE samples. Our second contribution is to apply our predictors to Wi-Fi metrics collected over one month from 3479 APs of customers of a large residential ISP. Our results show that QoE is good most of the time, still we find 11.6% of poor QoE samples. Worse, approximately 21% of stations have more than 25% poor QoE samples. In some cases, we estimate that Wi-Fi quality causes poor QoE for many hours, though in most cases poor QoE events are short

Tracking the Evolution and Diversity in Network Usage of Smartphones

ABSTRACT We analyze the evolution of smartphone usage from a dataset obtained from three, 15-day-long, user-side, measurements with over 1500 recruited smartphone users in the Greater Tokyo area from 2013 to 2015. This dataset shows users across a diverse range of networks; cellular access (3G to LTE), WiFi access (2.4 to 5GHz), deployment of more public WiFi access points (APs), as they use diverse applications such as video, file synchronization, and major software updates. Our analysis shows that smartphone users select appropriate network interfaces taking into account the deployment of emerging technologies, their bandwidth demand, and their economic constraints. Thus, users show diversity in both how much traffic they send, as well as on what networks they send it. We show that users are gradually but steadily adopting WiFi at home, in offices, and public spaces over these three years. The majority of light users have been shifting their traffic to WiFi. Heavy hitters acquire more bandwidth via WiFi, especially at home. The percentage of users explicitly turning off their WiFi interface during the day decreases from 50% to 40%. Our results highlight that the offloading environment has been improved during the three years, with more than 40% of WiFi users connecting to multiple WiFi APs in one day. WiFi offload at offices is still limited in our dataset due to a few accessible APs, but WiFi APs in public spaces have been an alternative to cellular access for users who request not only simple connectivity but also bandwidth-consuming applications such as video streaming and software updates. Categories and Subject Descriptors General Terms Measurement Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]