🤖 AI Summary
This study addresses the critical challenge of limited battery life on mobile devices, noting that existing power-saving recommendations often lack empirical grounding in real user behavior. To bridge this gap, the authors propose an automated monitoring framework that systematically evaluates the energy impact of user-controllable settings—such as screen brightness, refresh rate, network connectivity, interface theme, battery saver mode, and in-app configurations—by simulating authentic usage patterns across popular applications including WhatsApp, Instagram, TikTok, and YouTube. Drawing on over 12,000 experimental trials, this work presents the first large-scale quantitative analysis of how these configurable parameters affect energy consumption, elucidating the trade-offs between user experience and battery longevity, and offering scientifically grounded, actionable guidance for end users seeking to extend device runtime.
📝 Abstract
Mobile devices have become ubiquitous tools for communication, entertainment, and productivity, yet battery autonomy remains a constraint. While energy-saving tips exist, they are often generic, anecdotal, or focused on software development rather than end-user behavior, leaving users to rely on grey literature or tacit knowledge to optimize their device energy consumption, lacking the academic rigor to ensure their effectiveness. This research aims to bridge the gap between technical energy analysis and practical user application by quantifying the energy consumption of different user-controlled parameters. Employing an automated monitoring framework, a series of user interface tests that simulate realistic usage patterns across popular applications (i.e., WhatsApp, Instagram, TikTok, and YouTube) was conducted. The objective is to have a systematic evaluation of the energy impact of user-controllable factors, including device settings, such as screen brightness, refresh rate, connectivity status, interface themes, and battery-saving profiles, combined with more app-specific variables (e.g., video resolution and message size). By analyzing over 12,000 data points, this paper quantifies the real-world impact of common settings, revealing the trade-offs between user experience and device autonomy.