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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 [20-24] has made it possible to passively collect data sets in naturalistic settings.”
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Self-citationReconstruction of Battery Level Curves Based on User Data Collected from a Smartphone
Gechter, F., Beresford, A. R., & Rice, A. C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44748-3_28 · Cited by 6
GreenWeb: language extensions for energy-efficient mobile web computing
Zhu, Y., & Reddi, V. J. (2016) · ACM SIGPLAN Notices · doi:10.1145/2980983.2908082 · Cited by 6
A Weighted GraphSAGE-Based Context-Aware Approach for Big Data Access Control
Shan, D., Du, X., Wang, W., Liu, A., & Wang, N. (2023) · Big Data · doi:10.1089/big.2021.0473 · Cited by 5
Scalable Power Impact Prediction of Mobile Sensing Applications at Pre-Installation Time
Min, C., Lee, Y., Yoo, C., Hwang, I., Ju, Y., Song, J., & Kang, S. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2909897 · Cited by 5
Towards understanding smartphone usage and sleep with a crowdsensing approach
Sharmila, P., Schroderus, V., Lagerspetz, E., & Peltonen, E. (2020) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3410530.3414442 · Cited by 5
Self-citationEvidence-Aware Mobile Cloud Architectures
Flores, H., Kostakos, V., Tarkoma, S., Hui, P., & Li, Y. (2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-67925-9_4 · Cited by 5
Self-citationSudden Drop in the Battery Level?
Hoque, M. A., & Tarkoma, S. (2016) · ACM SIGOPS Operating Systems Review · doi:10.1145/2883591.2883606 · Cited by 5
Self-citationMin, C., Lee, Y., Yoo, C., Kang, S., Hwang, I., & Song, J. (2016) · GetMobile Mobile Computing and Communications · doi:10.1145/2972413.2972424 · Cited by 5
On the Unicity of Smartphone ApplicationsPreprint
Achara, J. P., Ács, G., & Castelluccia, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.07851 · Cited by 5
Metadata
- 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
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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.
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