The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Energy-accuracy trade-off for continuous mobile device location
Abstract
Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, it's frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.
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Cites
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The Horus WLAN location determination system
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City-Scale Location Recognition
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BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices
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Energy-efficient rate-adaptive GPS-based positioning for smartphones
Paek, J., Kim, J., & Govindan, R. (2010) · Proceedings of the 8th international conference on Mobile systems, applications, and services · doi:10.1145/1814433.1814463 · Cited by 466
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Cited by
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052577 · Cited by 667
Carat: collaborative energy diagnosis for mobile devices
Oliner, A. J., Iyer, A. P., Stoica, I., Lagerspetz, E., & Tarkoma, S. (2013) · Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2517351.2517354 · Cited by 148
Across-Sensor Feature Learning for Energy-Efficient Activity Recognition on Mobile Devices
Gavrilin, Y., & Khan, A. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851977 · Cited by 8
Metadata
- Authors
- Lin, K., Kansal, A., Lymberopoulos, D., & Zhao, F.
- Venue
- Proceedings of the 8th international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2010-06-15
- Pages
- 285-298
- OpenAlex
- W1996790762
- Cited by
- 277
- References
- 16
- FWCI
- 43.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lin, K., Kansal, A., Lymberopoulos, D., & Zhao, F. (2010). Energy-accuracy trade-off for continuous mobile device location. Proceedings of the 8th international conference on Mobile systems, applications, and services. https://doi.org/10.1145/1814433.1814462
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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