CSR: SMALL: Automatically Detecting, Diagnosing and Resolving Abnormal Battery Drain Issues on Smartphone Systems

CSR：小：自动检测、诊断和解决智能手机系统上的异常电池消耗问题

• 批准号: 1217408
• 负责人: Yuanyuan Zhou
• 金额: $ 35万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217408&HistoricalAwards=false
• 关键词: CSR; SMALL; Automatically; Detecting; Diagnosing

This project presents a software system to have the smartphone itself deal with the abnormal battery drain (ABD) issues that are caused by 'battery bugs' in smartphone applications or system software as well as by battery-related configuration errors and environmental changes. The proposed system architecture contains four subsystems, namely information collection, data analysis, diagnosis, and resolution, to self-detect, self-diagnose, and self-recover with little user intervention if possible when ABD events happen. Various technical methods, including machine learning and statistical approaches, will be investigated to achieve the design goal.

该项目提供了一个软件系统，让智能手机本身处理由智能手机应用程序或系统软件中的“电池错误”以及与电池相关的配置错误和环境变化引起的异常电池耗尽(ABD)问题。提出的系统架构包括信息收集、数据分析、诊断和解决四个子系统，用于在ABD事件发生时尽可能地在较少用户干预的情况下进行自我检测、自我诊断和自我恢复。将研究各种技术方法，包括机器学习和统计方法，以实现设计目标。