{"title":"Modeling the Effects of Independent Components on Mobile Device Charging Times","authors":"Mathew Schlichting, Jason Sawin","doi":"10.1109/FiCloud.2017.59","DOIUrl":null,"url":null,"abstract":"Mobile devices have been increasing in both number and power. While devices such as smartphones gain capabilities, an increasing number of users rely on them to complete more and more tasks. One of the most significant constraints of mobile devices is the necessary reliance on battery power. This limitation can be circumvented by operating the device while it is charging; however, this approach has the potential to create a conflict of goals. Clearly, users want to continue to operate their devices at the same time that they also want to achieve a certain level of battery charge in a given charging period. Unfortunately, such use of the device might increase the time needed to achieve their charging goals. In this paper, we present a preliminary exploration of the effects of independent components on the charging times of one mobile device: the smartphone. We provide the design of a data gathering framework that heavily exercises particular smartphone components while monitoring battery charge rate. The data generated from this framework can be used to create models for estimating the impacts of the individual components on battery charge times. Our empirical study demonstrates that different components can have significant impacts on smartphone charging rates.","PeriodicalId":115925,"journal":{"name":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","volume":"51 7","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FiCloud.2017.59","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Mobile devices have been increasing in both number and power. While devices such as smartphones gain capabilities, an increasing number of users rely on them to complete more and more tasks. One of the most significant constraints of mobile devices is the necessary reliance on battery power. This limitation can be circumvented by operating the device while it is charging; however, this approach has the potential to create a conflict of goals. Clearly, users want to continue to operate their devices at the same time that they also want to achieve a certain level of battery charge in a given charging period. Unfortunately, such use of the device might increase the time needed to achieve their charging goals. In this paper, we present a preliminary exploration of the effects of independent components on the charging times of one mobile device: the smartphone. We provide the design of a data gathering framework that heavily exercises particular smartphone components while monitoring battery charge rate. The data generated from this framework can be used to create models for estimating the impacts of the individual components on battery charge times. Our empirical study demonstrates that different components can have significant impacts on smartphone charging rates.