Carat: collaborative energy diagnosis for mobile devices
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).
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Instrumenting smartphones and wearables to capture in situ, fine-grained, and moment-by-moment data sets with sensing apps has made it possible to passively collect data sets in naturalistic settings.
“Instrumenting smartphones and wearables to capture in situ, fine-grained, and moment-by-moment data sets with sensing apps [20-24] has made it possible to passively collect data sets in naturalistic settings.”
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On the Unicity of Smartphone ApplicationsPreprint
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- Authors
- Oliner, A. J., Iyer, A. P., Stoica, I., Lagerspetz, E., & Tarkoma, S.
- Affiliations
- University of California, Berkeley (US)
- University of Helsinki (FI)
- Venue
- Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
- Publisher
- ACM
- Published
- 2013-11-11
- Pages
- 1-14
- OpenAlex
- W2121746381
- Cited by
- 148
- References
- 50
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 2 · 2021: 8 · 2020: 8 · 2019: 12 · 2018: 8 · 2017: 15 · 2016: 28 · 2015: 33 · 2014: 16 · 2013: 4
- FWCI
- 32.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Embedded Networked Sensor Systems (SenSys) (publishedVersion)
- Funders
- Hewlett-Packard Development Company
- NetApp
- Division of Computing and Communication Foundations (CCF-1139158)
- Intel Corporation
- Microsoft
- Cisco Systems
- VMware
- Defense Advanced Research Projects Agency (FA8650-11-C-7136)
- Ericsson
- Splunk
- Blue Goji
- Huawei Technologies
- Amazon Web Services
- SAP America
- Cloudera
- General Electric
- Oracle
- Quanta
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Keywords
- coarse-grained measurements, false positive reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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