An Empirical Analysis of Mobile Energy Consumption Across User Configurations

• URL: http://arxiv.org/abs/2604.25587v1
• Date: Tue, 28 Apr 2026 12:54:30 GMT
• Title: An Empirical Analysis of Mobile Energy Consumption Across User Configurations
• Abstract summary: 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.<n>The objective is to have a systematic evaluation of the energy impact of user-controllable factors, including device settings.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions [63.13827503828231]
  We introduce VitaBench 2.0, a benchmark for evaluating personalized and proactive agent behavior in long-term user interactions.<n>Results show that real-world personalization remains highly challenging even for state-of-the-art models.
  arXiv  Detail & Related papers  (2026-05-26T15:07:38Z)
• Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants [85.1155076383488]
  Pare is a framework for building and evaluating proactive agents in digital environments.<n>Pare-Bench is a benchmark of 143 diverse tasks spanning communication, productivity, scheduling, and lifestyle apps.
  arXiv  Detail & Related papers  (2026-04-01T12:53:01Z)
• Beyond Permissions: Investigating Mobile Personalization with Simulated Personas [30.156063920321774]
  This paper presents a sandbox system that uses sensor spoofing and persona simulation to audit and visualize how mobile apps respond to inferred behaviors.<n>Our system injects multi-sensor profiles into Android devices in real time, enabling users to observe app responses to contexts such as high activity, location shifts, or time-of-day changes.<n>Preliminary findings show measurable app adaptations across fitness, e-commerce, and everyday service apps such as weather and navigation.
  arXiv  Detail & Related papers  (2025-11-03T08:39:38Z)
• Screencast-Based Analysis of User-Perceived GUI Responsiveness [53.53923672866705]
  tool is a technique that measures GUI responsiveness directly from mobile screencasts.<n>It uses computer vision to detect user interactions and analyzes frame-level visual changes to compute two key metrics.<n>tool has been deployed in an industrial testing pipeline and analyzes thousands of screencasts daily.
  arXiv  Detail & Related papers  (2025-08-02T12:13:50Z)
• Creating General User Models from Computer Use [53.59999173952482]
  This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer.<n>The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture user knowledge and preferences.
  arXiv  Detail & Related papers  (2025-05-16T04:00:31Z)
• EnergyAnalyzer: Using Static WCET Analysis Techniques to Estimate the Energy Consumption of Embedded Applications [0.6144680854063939]
  EnergyAnalyzer is a code-level static analysis tool for estimating the energy consumption of embedded software. It was developed as part of a larger project called TeamPlay, which aimed to provide a toolchain for developing embedded applications where energy properties are first-class citizens.
  arXiv  Detail & Related papers  (2023-05-24T10:01:32Z)
• SimplyMime: A Control at Our Fingertips [0.17499351967216337]
  This paper presents a novel system, named SimplyMime, which aims to eliminate the need for multiple remote controls for consumer electronics. SimplyMime leverages a dynamic hand gesture recognition architecture, incorporating Artificial Intelligence and Human-Computer Interaction. The performance of the proposed method for detecting and recognizing gestures in a stream of motion was thoroughly tested and validated.
  arXiv  Detail & Related papers  (2023-04-22T11:25:19Z)
• Building Energy Consumption Models Based On Smartphone User's Usage Patterns [0.0]
  This study aims to build an energy consumption model based on user usage patterns. We establish a method to identify the components with the most influence in the smartphone's energy consumption. After training and testing each strategy to model the energy consumption based on the user's usage, we demonstrated that it is possible to get a Mean Absolute Error of 158.57mW.
  arXiv  Detail & Related papers  (2020-12-15T00:53:40Z)
• SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
  We present SensiX, a personal edge platform that stays between sensor data and sensing models. We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
  arXiv  Detail & Related papers  (2020-12-04T23:06:56Z)
• Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study [2.741120981602367]
  This work builds on the detection of repeated usage patterns from consumption logs. It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators. The system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits.
  arXiv  Detail & Related papers  (2020-10-09T17:29:56Z)
• General-Purpose User Embeddings based on Mobile App Usage [46.343844014289246]
  behaviors on mobile app usage, including retention, installation, and uninstallation, can be a good indicator for both long-term and short-term interests of users. Traditionally, user modeling from mobile app usage heavily relies on handcrafted feature engineering. We present a tailored AutoEncoder-coupled Transformer Network (AETN), by which we overcome these challenges and achieve the goals of reducing manual efforts and boosting performance.
  arXiv  Detail & Related papers  (2020-05-27T12:01:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.