Proceedings ArticleDOI
Carat: collaborative energy diagnosis for mobile devices
TLDR
During a deployment to a community of more than 500,000 devices, Carat diagnosed thousands of energy anomalies in the wild and increased a user's battery life by 11% after 10 days (compared with 1.9% for the control group).Abstract:
We aim to detect and diagnose energy anomalies, abnormally heavy battery use. This paper describes a collaborative black-box method, and an implementation called Carat, for diagnosing anomalies on mobile devices. A client app sends intermittent, coarse-grained measurements to a server, which correlates higher expected energy use with client properties like the running apps, device model, and operating system. The analysis quantifies the error and confidence associated with a diagnosis, suggests actions the user could take to improve battery life, and projects the amount of improvement. During a deployment to a community of more than 500,000 devices, Carat diagnosed thousands of energy anomalies in the wild. Carat detected all synthetically injected anomalies, produced no known instances of false positives, projected the battery impact of anomalies with 95% accuracy, and, on average, increased a user's battery life by 11% after 10 days (compared with 1.9% for the control group).read more
Citations
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Near-Optimal Fingerprinting with Constraints
TL;DR: In this article, the problem of identifying the attributes (e.g., smartphone applications) that can serve as a fingerprint of users given constraints on the size of the fingerprint was addressed.
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Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects
TL;DR: An original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information, and proposes a more security focused approach for the solution.
Proceedings ArticleDOI
Evaluating Energy-Efficiency using Thermal Imaging
Huber Flores,Jonatan Hamberg,Xin Li,Titti Malmivirta,Agustin Zuniga,Eemil Lagerspetz,Petteri Nurmi +6 more
TL;DR: An innovative and novel approach for assessing energy footprint of mobile and wearable systems using thermal imaging using an off-the-shelf thermal camera used to monitor thermal radiation of a device while it is operating an application.
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Adaptive context‐aware service optimization in mobile cloud computing accounting for security aspects
TL;DR: An original agent‐based adaptive task scheduling system which optimizes the performance of services in the mobile cloud computing environment using machine learning mechanisms and context information is presented.
Book ChapterDOI
Jouler: A Policy Framework Enabling Effective and Flexible Smartphone Energy Management
TL;DR: Jouler is presented, a framework enabling effective and flexible smartphone energy management by cleanly separating energy control mechanisms from management policies and results indicate that users appreciate more flexible smartphoneEnergy management and that Jouler policies can help users achieve their energy management goals.
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