Multidevice mobile sessions: A first look

Finley, Benjamin; Soikkeli, Tapio

doi:10.1016/j.pmcj.2016.11.001

Cited by 8 publications

(11 citation statements)

References 32 publications

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“…In other words, even though the coefficients are reasonable and descriptive point estimates, user diversity is also a part of the story. Similarly, user usage diversity has been observed extensively in previous mobile usage studies [2,7,10,16,33]. As mentioned, device sharing between members of a household is a potential concern in the testing of H2, H4, and H5.…”

Section: Proceedings Of the Acm Onmentioning

confidence: 73%

“…We discuss the implications of this further in Section 5.1. Concerning overlapping usage (between devices), we include such usage because our hypotheses are formulated in terms of total usage and overlapping usage is both possible and relatively common [10]. We apply maximum session length timeouts of 3 hours for iOS/Android sessions and 8 hours for Windows sessions to limit sessions where the display is forced on continuously but the user may not be using the device.…”

Section: Datasetmentioning

confidence: 99%

“…Given this diversity, understanding how certain device types may substitute for other device types (specifically a shift of usage to a new device) or even prompt additional (non-substituted) usage is important for, as an example, understanding the relative positioning of these device types in the mobile ecosystem. Yet despite this importance, few studies have explicitly analyzed device type substitution [10,11,24,26,32]. Furthermore, existing studies have either relied on survey or user completed diary methods 1 [24,26,32] or neglected matching methods to control for covariate imbalances between compared groups [10].…”

Section: Introductionmentioning

confidence: 99%

“…Through our analysis, we test several different device type substitution hypotheses partially informed by prior research [11,Section 5.2] [10,Section 5.4]. The tested hypotheses are as follows:…”

Section: Introductionmentioning

confidence: 99%

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Mobile Device Type Substitution

Finley

Soikkeli²

2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Mobile users today interact with a variety of mobile device types including smartphones, tablets, smartwatches, and others. However research on mobile device type substitution has been limited in several respects including a lack of detailed and robust analyses. Therefore, in this work we study mobile device type substitution through analysis of multidevice usage data from a large US-based user panel. Specifically, we use regression analysis over paired user groups to test five device type substitution hypotheses. We find that both tablets and PCs are partial substitutes for smartphones with tablet and PC ownership decreasing smartphone usage by about 12.5 and 13 hours/month respectively. Additionally, we find that tablets and PCs also prompt about 20 and 57 hours/month respectively of additional (non-substituted) usage. We also illustrate significant inter-user diversity in substituted and additional usage. Overall, our results can help in understanding the relative positioning of different mobile device types and in parameterizing higher level mobile ecosystem models.

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Section: Proceedings Of the Acm Onmentioning

confidence: 73%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mobile Device Type Substitution

Finley

Soikkeli²

2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…This time granularity provides an adequate level of network detail and realism while still allowing for the simulation of many agent hours in a reasonable amount of wall time. Furthermore, one second is an intuitive granularity for simulating user activities since user application sessions are often within a few orders of magnitude (a median smartphone session length is about 30 seconds [19]).…”

Section: Overall Model Parametersmentioning

confidence: 99%

Benefits of mobile end user network switching and multihoming

Finley¹,

Basaure²

2018

Computer Communications

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Mobile users have not been able to exploit spatio-temporal differences between individual mobile networks operators for a variety of reasons. End user network switching and multihoming are two promising mechanisms that could allow such exploitation. However these mechanisms have not been thoroughly explored at a general system level with QoE metrics. Therefore, in this work we analyze these mechanisms in a variety of diverse scenarios through a system level model based on an agent based modeling framework.In terms of results, we find that in all scenarios end user network switching provides significant benefits in terms of both throughput and mean opinion score as the number of available networks increases. However, contrastingly, end user multihoming in most scenarios does not provide significant benefits over network switching given the same number of available networks. The major reason is inefficient radio resource allocation resulting from individual networks not taking the multihoming nature of end users into account. Though, in low user density situations this inefficiency is not a problem and multihoming does provide increased throughput though not increased mean opinion scores. Finally, scenarios that vary the fraction of users adopting multihoming suggests that both early and late adopters will have similar gains over users not adopting multihoming. Thus the adoption dynamics of multihoming appear favorable. Overall, the results support the applicability of end user network switching for improving mobile user experience and the applicability of end user multihoming in more limited situations. (A. Basaure)1 The currently high switching costs (enabled by the entrenched MNO model in many countries) result in users switching MNOs at a timescale of years. This switching frequency is orders of magnitude too slow to significantly exploit the spatiotemporal differences between mobile networks.Given this operator resistance, mechanisms that do not require operator support are particularly interesting. In that vein, end user network switching is a mechanism that does not require operator support because the network switching is assumed to occur completely on the end user device. In addition, the related mechanism of end user multihoming (an end user transmitting over several networks simultaneously and thus aggregating capacity) does not require operator support given a higher layer multipath protocol such as MPTCP. The two mechanisms are fully defined in Section 2.1.Given the potential of these mechanisms, a key driver for spurring adoption is understanding the scenarios in which these mechanisms will benefit users and whether the actual user benefit is substantial. However, prior work [1,2,3,4] on these mechanisms has primarily focused on low level technical implementations rather than higher level system analyses. Furthermore, these technical works have not applied user-centric QoE metrics such as mean opinion score (MOS) in their analyses. Therefore, in this work we examine these two mechanisms through a syst...

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Learning from Usage Analysis of Mobile Devices

Mishra

2020

Procedia Computer Science

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Multidevice mobile sessions: A first look

Cited by 8 publications

References 32 publications

Mobile Device Type Substitution

Mobile Device Type Substitution

Benefits of mobile end user network switching and multihoming

Learning from Usage Analysis of Mobile Devices

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