Crowd-Sourced Battery Usage Prediction for Mobile Devices
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Solution Overview
Problem
Battery-operated client devices, such as smartphones and tablets, experience unpredictable battery duration due to inefficient third-party applications and device settings, leading to unexpected battery drain, which can be problematic for users without access to charging facilities.
Innovation Solution
A system that utilizes crowd-sourced data from multiple client devices to correlate application and setting operational states with battery usage, allowing for statistical analysis to identify battery-draining factors and provide users with insights or instructions to reduce battery consumption, such as terminating applications or adjusting settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If third-party applications are allowed on battery-operated client devices, then device functionality and user choice are improved, but battery duration deteriorates due to inefficient programming and resource consumption
Solution Approach 1:
The system implements feedback by collecting battery usage data from multiple client devices, analyzing the correlation between operational states and battery consumption, and using this information to identify applications that disproportionately impact battery duration. This feedback loop enables the system to provide users with actionable insights about which third-party applications are draining battery power, allowing them to make informed decisions about application usage while maintaining access to diverse functionality.
2Reliability
If crowd-sourced data collection is implemented across multiple client devices, then reliability of battery impact estimation is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system merges data from multiple client devices by collecting battery usage information across a plurality of devices, combining these datasets to identify common operational states and their associated battery consumption patterns. This aggregation approach enhances the reliability of battery impact estimates through statistical analysis while distributing the data collection burden across many devices, thereby managing system complexity through distributed participation rather than centralized processing of individual device data.
Data Source
AI summary
A plurality of client devices may each run on battery power and each experience respective battery usage while in a respective operational state. A server may receive, from the plurality of client devices, a plurality of reports correlating the client devices' respective operational states with the client devices' respective battery usage. Based on the reports, the server may identify at least two client devices in the plurality that reported a common operational state. The server may further determine a representative battery usage for the common operational state, and use this representative battery usage to predict battery usage for a particular client device that is in the common operational state. Then, the server may instruct the particular client device to take an action based upon the predicted battery usage.