Not you too? Distilling local contexts of poor cellular network performance through participatory sensing

Cellular service subscribers are increasingly reliant on cellular data services for all kinds of mobile applications. Oftentimes, when subscribers experience frustratingly high network delays and timeouts, they like to know whether their experiences are shared by other users nearby. The question tha...

Full description

Saved in:
Bibliographic Details
Main Authors: LIANG, Huiguang, NEVAT, Ido, KIM, Hyong S., Hwee-Pink TAN, YEOW, Wai-Leong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/3328
https://ink.library.smu.edu.sg/context/sis_research/article/4330/viewcontent/NotYouToo.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-4330
record_format dspace
spelling sg-smu-ink.sis_research-43302016-12-27T05:54:35Z Not you too? Distilling local contexts of poor cellular network performance through participatory sensing LIANG, Huiguang NEVAT, Ido KIM, Hyong S. Hwee-Pink TAN, YEOW, Wai-Leong Cellular service subscribers are increasingly reliant on cellular data services for all kinds of mobile applications. Oftentimes, when subscribers experience frustratingly high network delays and timeouts, they like to know whether their experiences are shared by other users nearby. The question that is often asked is essentially this: “is it just me, or do others around me face the same problem?” In this paper, we describe how we use Tattle, a distributed real-time participatory sensing and monitoring framework, to glean network performance information from users nearby. Tattle relies on recent advances in peer-to-peer device networking, such as Wi-Fi Direct, Bluetooth Low Energy, and Apple's iBeacon, to exchange key snippets of diagnostic information using very low-power, very short-range local-area wireless interfaces, between participating devices. We propose and develop a robust statistical algorithm, based on quantile regression, which identifies key points in time where a device experiences high delays and outages that are not observed by its neighbors, and decides if the device is performing “normally”, or “abnormally”. This directly answers the “me, or others?” question. We demonstrate and validate the efficacy of our system through real-world measurements of network delay, consisting of over 7,300 time-series that comprises over 443,500 data samples, using commodity smart devices attached to two different providers' networks. 2016-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3328 info:doi/10.1109/NOMS.2016.7502836 https://ink.library.smu.edu.sg/context/sis_research/article/4330/viewcontent/NotYouToo.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Performance evaluation Delays Monitoring IEEE 802.11 Standard Servers Sensors Probes Computer Sciences Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Performance evaluation
Delays
Monitoring
IEEE 802.11 Standard
Servers
Sensors
Probes
Computer Sciences
Software Engineering
spellingShingle Performance evaluation
Delays
Monitoring
IEEE 802.11 Standard
Servers
Sensors
Probes
Computer Sciences
Software Engineering
LIANG, Huiguang
NEVAT, Ido
KIM, Hyong S.
Hwee-Pink TAN,
YEOW, Wai-Leong
Not you too? Distilling local contexts of poor cellular network performance through participatory sensing
description Cellular service subscribers are increasingly reliant on cellular data services for all kinds of mobile applications. Oftentimes, when subscribers experience frustratingly high network delays and timeouts, they like to know whether their experiences are shared by other users nearby. The question that is often asked is essentially this: “is it just me, or do others around me face the same problem?” In this paper, we describe how we use Tattle, a distributed real-time participatory sensing and monitoring framework, to glean network performance information from users nearby. Tattle relies on recent advances in peer-to-peer device networking, such as Wi-Fi Direct, Bluetooth Low Energy, and Apple's iBeacon, to exchange key snippets of diagnostic information using very low-power, very short-range local-area wireless interfaces, between participating devices. We propose and develop a robust statistical algorithm, based on quantile regression, which identifies key points in time where a device experiences high delays and outages that are not observed by its neighbors, and decides if the device is performing “normally”, or “abnormally”. This directly answers the “me, or others?” question. We demonstrate and validate the efficacy of our system through real-world measurements of network delay, consisting of over 7,300 time-series that comprises over 443,500 data samples, using commodity smart devices attached to two different providers' networks.
format text
author LIANG, Huiguang
NEVAT, Ido
KIM, Hyong S.
Hwee-Pink TAN,
YEOW, Wai-Leong
author_facet LIANG, Huiguang
NEVAT, Ido
KIM, Hyong S.
Hwee-Pink TAN,
YEOW, Wai-Leong
author_sort LIANG, Huiguang
title Not you too? Distilling local contexts of poor cellular network performance through participatory sensing
title_short Not you too? Distilling local contexts of poor cellular network performance through participatory sensing
title_full Not you too? Distilling local contexts of poor cellular network performance through participatory sensing
title_fullStr Not you too? Distilling local contexts of poor cellular network performance through participatory sensing
title_full_unstemmed Not you too? Distilling local contexts of poor cellular network performance through participatory sensing
title_sort not you too? distilling local contexts of poor cellular network performance through participatory sensing
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3328
https://ink.library.smu.edu.sg/context/sis_research/article/4330/viewcontent/NotYouToo.pdf
_version_ 1770573113812058112