PowerForecaster: Predicting power impact of mobile sensing applications at pre-installation time

• Main Authors: MIN, Chulhong, LEE, Youngki, YOO, Chungkuk, KANG, Seungwoo, HWANG, Inseok, SONG, Junehwa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Power; Estimation; System; Sensing; Pre-installation tie; Computer Sciences; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3307; https://ink.library.smu.edu.sg/context/sis_research/article/4309/viewcontent/powerforecaster.pdf
• Institution: Singapore Management University

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly insta...