Diversity in smartphone usage

By Hossein Falaki, Ratul Mahajan, Srikanth Kandula, Dimitrios Lymberopoulos, Ramesh Govindan, Deborah Estrin. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2010

922 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/c10948c548ea973725abd7c7e7666c2b091ac917

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Citations in the corpus

A first look at cellular machine-to-machine traffic: large scale measurement and characterization (2012, background): “conducted a measurement study of smartphone usage [11].”
Next generation cloud computing: New trends and research directions (2017, background): “This is becoming popular for smaller mobile devices, such as smartphones [23,24], which on an average have less than a 25% per hour of usage [25,26].”
How "phubbing" becomes the norm: The antecedents and consequences of snubbing via smartphone (2016, background): “Indeed, smartphones have a wider variety of functions and applications than ordinary cell phones that have less technological capability (Falaki et al., 2010).”
Cellphone based Portable Bacteria Pre-Concentrating microfluidic Sensor and Impedance Sensing System (2013, background): “With the integration of GPS [16] , powerful CPU, touch-screen displays, internet connection capability, and intelligent operating system [17] , smartphones are able to provide extensive user-friendly services.”
Measuring user confidence in smartphone security and privacy (2012, background): “examined Android and Windows Phone application usage from the perspective of reducing energy consumption [22].”
An in-depth study of LTE: effect of network protocol and application behavior on performance (2013, background): “Prior efforts [8, 30, 23] deployed smartphone user studies and collected data from tens to hundreds of participating users.”
Mobile Data Offloading: How Much Can WiFi Deliver? (2013, background): “It indicates that at any time, about 70% of users stay in a WiFi coverage area.”
A close examination of performance and power characteristics of 4G LTE networks (2012, background): “, 3GTest [24], LiveLab [31], and [23]) collect traces from real smartphone users, focusing on characterization at only IP and higher layers.”
Implicit Authentication through Learning User Behavior (2010, background): “Furthermore, usage of the device varies from person to person [7] measured through battery and network activity.”
Profiling resource usage for mobile applications: a cross-layer approach (2011, background): “, 3gTest [19], LiveLab [28], and [14]) collect traces from real smartphone users, focusing on characterization at only IP and higher layers.”
LiveLab (2011, methodology): “Existing smartphone loggers reported in the literature [12-15], including our prior work [10], employ a static installation method and are difficult to update or maintain.”