Title
Constella: Crowdsourced system setting recommendations for mobile devices.
Abstract
The question “Where has my battery gone?” remains a common source of frustration for many smartphone users. With the increased complexity of smartphone applications, and the increasing number of system settings affecting them, understanding and optimizing battery use has become a difficult chore. The present paper develops a novel approach for constructing energy models from crowdsourced measurements. In contrast to previous approaches, which have focused on the effect of a specific sensor, system setting or application, our approach can simultaneously capture relationships between multiple factors, and provide a unified view of the energy state of the mobile device. We demonstrate the validity of using crowdsourced measurements for constructing battery models through a combination of large-scale analysis of a dataset containing battery discharge and system state measurements, and hardware power measurements. The results indicate that the models captured by our approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device. The analysis also provides several new insights about battery consumption. For example, our analysis reveals the combined effect of high CPU activity and automatic screen brightness to be higher (resulting in 9 min shorter battery lifetime on average) than the effect of medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can shorten battery life by over 13%; and a smartphone sitting in direct sunlight can witness over 50% shorter battery life than one indoors in cool conditions. Based on the crowdsourced energy models, we develop Constella, a novel recommender system for system settings. Constella provides actionable and human-readable recommendations on how to adjust system settings in order to reduce overall battery drain. We validate the effectiveness of Constella through a hardware power measurement experiment carried out using three application case studies. The results of the evaluation demonstrate that Constella is capable of generating recommendations that can provide up to 61% improvements in battery life.
Year
DOI
Venue
2016
10.1016/j.pmcj.2015.10.011
Pervasive and Mobile Computing
Keywords
Field
DocType
Mobile sensing,Energy-awareness,Energy modeling,System settings
Energy modeling,Recommender system,Mobile sensing,Computer science,Simulation,Mobile device,Signal strength,Cpu load,Empirical measure,Battery (electricity)
Journal
Volume
ISSN
Citations 
26
1574-1192
5
PageRank 
References 
Authors
0.52
31
4
Name
Order
Citations
PageRank
Ella Peltonen1215.52
Eemil Lagerspetz242729.56
Petteri Nurmi362157.08
Sasu Tarkoma41312125.76