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https://doi.org/10.1109/percom...
Article . 2016 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2023
Data sources: DBLP
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Characterization of early smartwatch apps

Authors: Seneviratne, AP; Chauhan, J; Seneviratne, S; Kaafar, MA; Mahanti, A;

Characterization of early smartwatch apps

Abstract

Wearable smart devices are already amongst us. Currently, smartwatches are one of the key drivers of the wearable technology and are being used by a large population of consumers. This paper takes a first look at this increasingly popular technology with a systematic characterization of the smartwatch app markets. We conduct a large scale analysis of three popular smartwatch app markets: Android Wear, Samsung, and Apple, and characterize more than 14,000 smartwatch apps in multiple aspects such as prices, number of developers and categories. Our analysis shows that approximately 41% and 30% of the apps in Android Wear and Samsung app markets are Personalization apps that provide watch faces. Further, we provide a generic taxonomy for apps on all three platforms based on their packaging and modes of communication, that allow us to investigate apps with respect to privacy and security. Finally, we study the privacy risks associated with the app usage by identifying third party trackers integrated into these apps and personal information leakage through network traffic analysis. We show that a higher percentage of Apple apps (62%) are connected to third party trackers compared to Samsung (36%) and Android Wear (46%).

Country
Australia
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Keywords

anzsrc-for: 4608 Human-Centred Computing, 46 Information and Computing Sciences, 4608 Human-Centred Computing, anzsrc-for: 4604 Cybersecurity and Privacy, anzsrc-for: 46 Information and Computing Sciences, 4604 Cybersecurity and Privacy, 3 Good Health and Well Being, Generic health relevance, 004

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    20
    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
Green