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...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2016
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| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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