Can User-Level Probing Detect and Diagnose Common Home-WLAN Pathologies?

Common WLAN pathologies include low signal-to-noise ratio, congestion, hidden terminals or interference from non-802.11 devices and phenomena. Prior work has focused on the detection and diagnosis of such problems using layer-2 information from 802.11 devices and special-purpose access points and monitors, which may not be generally available. Here, we investigate a userlevel approach: is it possible to detect and diagnose 802.11 pathologies with strictly user-level active probing, without any cooperation from, and without any visibility in, layer-2 devices? In this paper, we present preliminary but promising results indicating that such diagnostics are feasible

Home Wi-Fi Impairments

International audienceThe preferred method to access Internet from home is Wi-Fi. Unfortunately poorly placed Wi-Fi access point can experience Wi-Fi impairments such as interference or congestion , leading to degraded Internet performance. Identifying these impairments can be challenging, even for wireless experts. To approach this challenge we develop a tool to identify home Wi-Fi impairments. In our work we conduct experiments triggering wireless and non-wireless issues in a testbed. The two methods we work with are active probing and passive wireless metrics collection from wireless AP and wireless client. The wireless metrics we collect include but are not limited to, RSSI, PHY Rate, Noise, etc. With these metrics we get a sense of the status of home Wi-Fi and correlate it with our active probing results. Finally, to identify a wireless impairment we run our dataset through supervised learning algorithms. We obtain the best results with random forest algorithm. Random forest is well known for its precision to classify events based on a specific set of features. We close our paper by presenting the results of home Wi-Fi impairment detection by modeling it as a classification problem

Towards Trouble-Free Networks for End Users

Network applications and Internet services fail all too frequently. However, end users cannot effectively identify the root cause using traditional troubleshooting techniques due to the limited capability to distinguish failures caused by local network elements from failures caused by elements located outside the local area network. To overcome these limitations, we propose a new approach, one that leverages collaboration of user machines to assist end users in diagnosing various failures related to Internet connectivity and poor network performance. First, we present DYSWIS ("Do You See What I See?"), an automatic network fault detection and diagnosis system for end users. DYSWIS identifies the root cause(s) of network faults using diagnostic rules that consider diverse information from multiple nodes. In addition, the DYSWIS rule system is specially designed to support crowdsourced and distributed probes. We also describe the architecture of DYSWIS and compare its performance with other tools. Finally, we demonstrate that the system successfully detects and diagnoses network failures which are difficult to diagnose using a single-user probe. Failures in lower layers of the protocol stack also have the potential to disrupt Internet access; for example, slow Internet connectivity is often caused by poor Wi-Fi performance. Channel contention and non-Wi-Fi interference are the primary reasons for this performance degradation. We investigate the characteristics of non-Wi-Fi interference that can severely degrade Wi-Fi performance and present WiSlow ("Why is my Wi-Fi slow?"), a software tool that diagnoses the root causes of poor Wi-Fi performance. WiSlow employs user-level network probes and leverages peer collaboration to identify the physical location of these causes. The software includes two principal methods: packet loss analysis and 802.11 ACK number analysis. When the issue is located near Wi-Fi devices, the accuracy of WiSlow exceeds 90%. Finally, we expand our collaborative approach to the Internet of Things (IoT) and propose a platform for network-troubleshooting on home devices. This platform takes advantage of built-in technology common to modern devices --- multiple communication interfaces. For example, when a home device has a problem with an interface it sends a probe request to other devices using an alternative interface. The system then exploits cooperation of both internal devices and remote machines. We show that this approach is useful in home networks by demonstrating an application that contains actual diagnostic algorithms

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

Online Identification of Last-Mile Throughput Bottlenecks on Home Routers

Supervisors: Renata Teixeira (Inria) , Promethee Spathis (UPMC)Advisors: Anna-Kaisa Pietilainen (Inria) , Srikanth Sundaresan (Samsara Networks/ICSI), Nick Feamster (Princeton University)International audienceWe develop a system that runs online on commodity home routers to locate last-mile throughput bottlenecks to the home wireless network or the access ISP. Pinpointing whether the home wireless or the access ISP bottlenecks Internet through-put is valuable for home users who want to better troubleshoot their Internet experience; for access ISPs that receive numerous calls from frustrated home customers; and for informing the debate on regulating the residential broadband market. Developing such a system is challenging because commodity home routers have limited resources. The main contribution of this thesis is to develop a last-mile throughput bottleneck detection algorithm that relies solely on lightweight metrics available in commodity home routers. Our evaluation shows that our system accurately locates last-mile bottlenecks on commodity home routers with little performance degradation

DETECTION AND ALLEVIATION OF LAST-MILE WIRELESS LINK BOTTLENECKS

Ph.DDOCTOR OF PHILOSOPH

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications