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Data Traffic Offload from Mobile to Wi-Fi Networks: Behavioural Patterns of Smartphone Users

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Data Traffic Offload from Mobile to Wi-Fi Networks: Behavioural Patterns of Smartphone Users Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 2608419, 13 pages https://doi.org/10.1155/2018/2608419 Research Article Data Traffic Offload from Mobile to Wi-Fi Networks: Behavioural Patterns of Smartphone Users Siniša Husnjak , Dragan PerakoviT , and Ivan Forenbacher Faculty of Transport and Trafc Sciences, University of Zagreb, Vukeli´ceva 4, 10000 Zagreb, Croatia Correspondence should be addressed to Siniˇsa Husnjak; [email protected] Received 11 August 2017; Revised 14 January 2018; Accepted 12 February 2018; Published 25 March 2018 Academic Editor: Michael McGuire Copyright © 2018 Siniˇsa Husnjak et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper presents a model for defning the behavioural patterns of smartphone users when ofoading data from mobile to Wi-Fi networks. Te model was generated through analysis of individual characteristics of 298 smartphone users, based on data collected via online survey as well as the amount of data ofoaded from mobile to Wi-Fi networks as measured by an application integrated into the smartphone. Users were segmented into categories based on data volume ofoaded from mobile to Wi-Fi networks, and numerous user characteristics were explored to develop a model capable of predicting the probability that a user with given characteristics will fall into a given category of data ofoading. Tis model may prove useful for analysing smartphone user behaviour when ofoading data. 1. Introduction [5] concludes that the amount of smartphone-based data trafc ofoading to Wi-Fi networks will be 56% of total data Te number of smartphone subscription connections at trafc by the year 2020, and studies have claimed that 65% the global level has reached 3 billion, and in the last fve [14] or more than 70% [15] of smartphone data trafc is being years, data trafc has increased more than 40-fold [1–4]. ofoaded to Wi-Fi networks. One study of smartphone use According to a Cisco report [5], smartphone-based data found that the ratio of data trafc generated using Wi-Fi and trafc is expected to exceed 80% of total data trafc gen- mobile networks was greater than 4 : 1 [16]. At the global level, erated on mobile networks by 2020. Te increased use of data trafc on mobile devices occurs via Wi-Fi networks and smartphones, adaptation of price plans, and user demands for mobile networks in the ratio 80%/20% [17]. Smartphones and entertainment services and video content are accelerating the phablet devices will continue to dominate the generation of increase in data trafc ofoading, and this growth is expected mobile data trafc [5]. to continue [6, 7]. Data trafc ofoading refers to trafc Mobile network operators are aware that the use of via complementary access network technology (e.g., Wi-Fi, Wi-Fi networks is a key element in smartphone users’ WiMAX) instead of trafc via mobile networks [2, 8, 9]. behaviour. Measuring such use is challenging in part because Tis paper considers only ofoading involving Wi-Fi of the fragmentation of Wi-Fi networks. As a result, mobile networks [10], which account for a substantial proportion of network operators cannot gain a detailed view of smart- data trafc ofoading. More than 180 million Wi-Fi access phone connection with Wi-Fi access points or of data trafc points are spread around the world [11]. Wi-Fi technology ofoading. is popular because it does not require licenced frequency Using applications integrated into the smartphone itself spectrum and provides users with mobility to move within ofers the possibility of measuring data trafc ofoaded onto the local area coverage [12]. Te quantity of data trafc that Wi-Fi networks, which would provide, for the frst time, is currently being ofoaded to Wi-Fi networks, primarily the opportunity for mobile network operators to measure because of Wi-Fi access points in households, already exceeds this aspect of user behaviour. Tis could lead to insights the total amount of data trafc in mobile networks, and this that could guide the design of strategic actions, optimisation proportion is expected to keep growing [6, 13]. A Cisco report of operations (e.g., personalisation of services and price 2 Wireless Communications and Mobile Computing plans), market segmentation, user targeting, and planning of future, and what makes them prefer to connect their device to telecommunication networks based on potential implemen- Wi-Fi rather than to the mobile network [1]. One study [34] tation of complementary Wi-Fi access networks. used an application for iOS and Android operating systems to analyse how the users of smartphones access networks (3G, 2. Overview of Previous Studies 4G, and Wi-Fi access). Teir results suggest that users select thenetworkbasedontheiraccesstonewercommunication Manyauthorshaveanalysedtheuseofmobilephoneswithin technologies, their possibilities for data transfer speed, and various contexts, but few have examined the reasons for economic possibilities. Smartphone user behaviour changes the substantial diferences among users in the amount of depending on the access network selected [16], and mobile smartphone data trafc ofoaded [20–22]. network subscribers ofen choose to access Wi-Fi rather than Several studies have considered the generation of data a3Gor4Gmobilenetwork[15].Tishasgivenriseto trafc exclusively involving mobile networks, and this lit- the ofoading “blackhole”: when mobile subscribers switch erature has established connections among user behaviour, from their mobile network to Wi-Fi, most operators can smartphones, and particular information-communication no longer track their activities, their data trafc amount, services. Alwahaishi and Sna´ˇsel [23] analysed user behaviour, the quality of their user experience, or the security of their characteristics, and intentions regarding the use of mobile Internet connection [35]. Tis blackhole represents a large Internet. Falaki et al. [24] analysed in detail patterns and vol- proportion of mobile subscriber behaviour: one study [34] umes of mobile data trafc generated by smartphones during indicates that users increase their data trafc when they the use of various information-communication services and move from a mobile to Wi-Fi network, and more than applications, such as Internet searching, instant messaging, 40% of users connect to more than one Wi-Fi access point and use of navigational maps. Baghel et al. [25] analysed in a single day. Tis data trafc ofoading creates several the volume of data trafc generated by various smartphone challenges for telecommunication operators, for which some apps. Ghose and Han [26] surveyed a large sample of users authors have already proposed some solutions [8]. Bakhit to derive a detailed view of their behaviour during the use et al. [36] concluded that ofoading to Wi-Fi networks is of information-communication services and applications. an optimal ofoad option since it takes advantage of Wi- Similarly, He et al. [27] analysed patterns of data trafc Fi availability and throughput capacity. Indeed, the data generated with various smartphones, in this case with the goal trafc ofoad from mobile to Wi-Fi networks has been of achieving desired performance levels in mobile networks. stimulated by “free” availability of Wi-Fi access at homes, Yangetal.[28]establishedalinkamongthevolumeofdata simplicity of automatic selection of the Wi-Fi network trafc generated, smartphone-based apps, and user mobility. by smartphones, quality of experience (QoE) on mobile Binde and Fuksa [29] examined the development of mobile networks, and price structure of data services of mobile communication networks and information-communication networks [16]. Hetting [37] states that the majority of users services as factors afecting mobile Internet use in Latvia. consider Internet access via Wi-Fi networks as less expensive, Other studies demonstrate possibilities for measuring simpler, faster, and more reliable than access via mobile data trafc generation on mobile phones or smartphones. networks. One study [20] illustrated various approaches to such mea- One approach to researching the blackhole of data trafc surement, each with its advantages and drawbacks. Another ofoading is to use applications installed on the mobile study [30] laid out in detail research methods for such mea- device itself to measure the trafc (e.g., [38]). Taylor et al. surement and described their applicability to given research [1] analysed user behaviour, smartphone use, and interest in objectives. Tat work explored the concept of measurement accessing Internet via Wi-Fi networks. Kaisar [39] compared points inside telecommunication networks as well as the data trafc generated via mobile or Wi-Fi networks with an possibility of predicting smartphone user behaviour and use application that collected data on user characteristics such as of information-communication services. network, location, and time. A potentially more informative Soikkeli [31] analysed periods of mobile phone use based approach is to supplement measurements taken on users’ on measurements collected on the mobile devices themselves. mobiledevicesthemselveswithdatafromsurveysofthesame Te data were collected by trafc-monitoring sofware previ- users [32, 40]. ously installed on the devices of test users, and they captured Previous studies have documented the diversity of Inter- the context in which the mobile
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