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Android vs. IOS: a comparative analysis over mobile operator infrastructures based on crowdsourced mobile dataset

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Engin Zeydan at CTTC Catalan Telecommunications Technology Centre
Engin Zeydan
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Abstract and Figures

User equipment (UE)’s operating system (OS) and category types are important factors that are affecting the end-user performance in a given mobile network operator (MNO)’s infrastructure. For this reason, fair and statistically accurate observed network performance differences of UE’s OSs based on category types, MNOs or locations can be of interest for mobile telecommunication ecosystem players. This paper’s focus is on performance comparisons of UE OSs (including Android, IOS (iPhone Operating System) and Windows phones) over different UE categories, MNOs and locations based on previously collected end-to-end nationwide crowd-sourced data measurements in Turkey. The analysis results performed in this paper uses statistical comparisons of unpaired observations due to imbalance between number of observations between all OSs and yield insight on how the mobile OS types’ network performances differ using some important Key Performance Indicators (KPIs) such as downlink (DL) speed, latency, jitter and packet loss (PL). The outcome of the analysis indicate that Android devices perform better in terms of DL speed among all MNOs, whereas IOS devices are better in terms of latency values. On the other hand depending on the UE category, the performances of MNOs may vary when IOS and Android OSs are compared based on different KPIs. Additionally, IOS has shown better performance than Android in large geographical areas of Turkey. Finally, the business aspects of performing the proposed statistical OS comparisons from the perspectives of OS developers, MNOs, device manufacturers, and end-users are highlighted.
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Telecommunication Systems (2021) 78:405–419
https://doi.org/10.1007/s11235-021-00820-y
Android vs. IOS: a comparative analysis over mobile operator
infrastructures based on crowdsourced mobile dataset
Engin Zeydan1
Accepted: 24 July 2021 / Published online: 20 August 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Abstract
User equipment (UE)’s operating system (OS) and category types are important factors that are affecting the end-user
performance in a given mobile network operator (MNO)’s infrastructure. For this reason, fair and statistically accurate
observed network performance differences of UE’s OSs based on category types, MNOs or locations can be of interest for
mobile telecommunication ecosystem players. This paper’s focus is on performance comparisons of UE OSs (including
Android, IOS (iPhone Operating System) and Windows phones) over different UE categories, MNOs and locations based on
previously collected end-to-end nationwide crowd-sourced data measurements in Turkey. The analysis results performed in
this paper uses statistical comparisons of unpaired observations due to imbalance between number of observations between
all OSs and yield insight on how the mobile OS types’ network performances differ using some important Key Performance
Indicators (KPIs) such as downlink (DL) speed, latency, jitter and packet loss (PL). The outcome of the analysis indicate that
Android devices perform better in terms of DL speed among all MNOs, whereas IOS devices are better in terms of latency
values. On the other hand depending on the UE category, the performances of MNOs may vary when IOS and Android OSs
are compared based on different KPIs. Additionally, IOS has shown better performance than Android in large geographical
areas of Turkey. Finally, the business aspects of performing the proposed statistical OS comparisons from the perspectives of
OS developers, MNOs, device manufacturers, and end-users are highlighted.
Keywords Large scale measurements ·MNO ·UE category ·UE OS ·Statistics
1 Introduction
The emerging wireless cellular technologies and vast amount
of data that are generated in large-scale networks have given
an unprecedented opportunity to mobile network operators
(MNOs) to discover hidden values, explore new values and
study characteristics of wireless devices and network infras-
tructures [1]. In order to observe network performance and
obtain competitive advantages for managing services and
networks, this generated data needs to be analyzed and eval-
uated [2,3]. In the telecommunication world, MNOs are
leveraging data analytics approaches to harness the rapidly
increasing complexity of networks. In future networks, it will
be almost impossible to run a network without data driven
analytics due to large number of connected User Equipments
BEngin Zeydan
engin.zeydan@cttc.cat
1Centre Technologic de Telecomunicacions de Catalunya,
08860 Castelldefels, Barcelona, Spain
(UE) into the network infrastructure. Hence, data-driven
analysis coupled with heterogeneous dataset (e.g. either
publicly or proprietary crowd-based dataset) can empower
automating network operations and infer some insight for
additional sources of revenues (e.g. determining the set of
UE operating systems (OSs) that may work well with the
infrastructure based on context information).
Many wide range of research studies in different domains
that attempt to understand performance of UE OSs and
their comparisons are done in the literature. Some exam-
ples of those domains are privacy and security aspects [4–8],
usability scores, user behaviour analysis and comparative OS
evolution steps [9–11], application release frequency, impor-
tant features or number of bug comparisons [12–15], mobile
streaming and cellular network connectivity or control/data
layer performances [16–18], impact on social media [19]or
image processing capabilities [20]). In terms of mobile OS
comparisons, connection management and handover capabil-
ities of three major operating systems OSs namely Windows,
IOS (iPhone Operating System) and Android are studied
123
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... Additionally, there are challenges with fragmentation in Android, with multiple concurrent OS versions and countless original equipment manufacturer (OEM) hardware modifications. Indeed, many existing crowd-sourced smartphone sensing studies tend to be done on iPhones only, for those very reasons 46 . There is an advantage to that approach, given that we do not have to deal with calibration issues across two platforms running different software. ...
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