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Robust and Energy-Efficient Trajectory Tracking for Mobile Devices

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Robust and Energy-Efficient Trajectory Tracking for Mobile Devices. / Bhattacharya, S.; Blunck, H.; Kjærgaard, M. B.; Nurmi, P.

In: IEEE Transactions on Mobile Computing, Vol. 14, No. 2, 01.02.2015, p. 430-443.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Bhattacharya, S, Blunck, H, Kjærgaard, MB & Nurmi, P 2015, 'Robust and Energy-Efficient Trajectory Tracking for Mobile Devices', IEEE Transactions on Mobile Computing, vol. 14, no. 2, pp. 430-443. https://doi.org/10.1109/TMC.2014.2318712

APA

Bhattacharya, S., Blunck, H., Kjærgaard, M. B., & Nurmi, P. (2015). Robust and Energy-Efficient Trajectory Tracking for Mobile Devices. IEEE Transactions on Mobile Computing, 14(2), 430-443. https://doi.org/10.1109/TMC.2014.2318712

Vancouver

Bhattacharya S, Blunck H, Kjærgaard MB, Nurmi P. Robust and Energy-Efficient Trajectory Tracking for Mobile Devices. IEEE Transactions on Mobile Computing. 2015 Feb 1;14(2):430-443. https://doi.org/10.1109/TMC.2014.2318712

Author

Bhattacharya, S. ; Blunck, H. ; Kjærgaard, M. B. ; Nurmi, P. / Robust and Energy-Efficient Trajectory Tracking for Mobile Devices. In: IEEE Transactions on Mobile Computing. 2015 ; Vol. 14, No. 2. pp. 430-443.

Bibtex

@article{6149f4db79a94a3a9506daaf7911cff7,
title = "Robust and Energy-Efficient Trajectory Tracking for Mobile Devices",
abstract = "Many mobile location-aware applications require the sampling of trajectory data accurately over an extended period of time. However, continuous trajectory tracking poses new challenges to the overall battery life of the device, and thus novel energy-efficient sensor management strategies are necessary for improving the lifetime of such applications. Additionally, such sensor management strategies are required to provide a high and application-adjustable level of robustness regardless of the user's transportation mode. In this article, we extend and further analyze the sensor management strategies of the EnTrackedT system that intelligently determines when to sample different on-device sensors (e.g., accelerometer, compass and GPS) for trajectory tracking. Specifically, we propose the concept of situational bounding to improve and parameterize the robustness of sensor management strategies for trajectory tracking. We demonstrate the effectiveness of our proposed approach by performing a series of emulation experiments on real world data sets collected from different modes of transportation (including walking, running, biking and commuting by car) on mobile devices from two different platforms. Thorough experimental analyses indicate that our system can save significant amounts of battery power compared to the state-of-the-art position tracking systems, while simultaneously maintaining robustness and accuracy bounds as required by diverse location-aware applications.",
keywords = "Global Positioning System, energy conservation, mobile handsets, mobile radio, mobility management (mobile radio), radio tracking, telecommunication power management, transportation, EnTrackedT system, GPS, battery power, diverse mobile location aware application, energy-efficient continuous trajectory tracking, energy-efficient sensor management, mobile device, on-device sensor, trajectory data sampling, user transportation mode, Accuracy, Power demand, Robustness, Target tracking, Trajectory, Energy-efficiency, positioning, sensor management, trajectory, trajectory simplification",
author = "S. Bhattacharya and H. Blunck and Kj{\ae}rgaard, {M. B.} and P. Nurmi",
year = "2015",
month = feb,
day = "1",
doi = "10.1109/TMC.2014.2318712",
language = "English",
volume = "14",
pages = "430--443",
journal = "IEEE Transactions on Mobile Computing",
issn = "1536-1233",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Robust and Energy-Efficient Trajectory Tracking for Mobile Devices

AU - Bhattacharya, S.

AU - Blunck, H.

AU - Kjærgaard, M. B.

AU - Nurmi, P.

PY - 2015/2/1

Y1 - 2015/2/1

N2 - Many mobile location-aware applications require the sampling of trajectory data accurately over an extended period of time. However, continuous trajectory tracking poses new challenges to the overall battery life of the device, and thus novel energy-efficient sensor management strategies are necessary for improving the lifetime of such applications. Additionally, such sensor management strategies are required to provide a high and application-adjustable level of robustness regardless of the user's transportation mode. In this article, we extend and further analyze the sensor management strategies of the EnTrackedT system that intelligently determines when to sample different on-device sensors (e.g., accelerometer, compass and GPS) for trajectory tracking. Specifically, we propose the concept of situational bounding to improve and parameterize the robustness of sensor management strategies for trajectory tracking. We demonstrate the effectiveness of our proposed approach by performing a series of emulation experiments on real world data sets collected from different modes of transportation (including walking, running, biking and commuting by car) on mobile devices from two different platforms. Thorough experimental analyses indicate that our system can save significant amounts of battery power compared to the state-of-the-art position tracking systems, while simultaneously maintaining robustness and accuracy bounds as required by diverse location-aware applications.

AB - Many mobile location-aware applications require the sampling of trajectory data accurately over an extended period of time. However, continuous trajectory tracking poses new challenges to the overall battery life of the device, and thus novel energy-efficient sensor management strategies are necessary for improving the lifetime of such applications. Additionally, such sensor management strategies are required to provide a high and application-adjustable level of robustness regardless of the user's transportation mode. In this article, we extend and further analyze the sensor management strategies of the EnTrackedT system that intelligently determines when to sample different on-device sensors (e.g., accelerometer, compass and GPS) for trajectory tracking. Specifically, we propose the concept of situational bounding to improve and parameterize the robustness of sensor management strategies for trajectory tracking. We demonstrate the effectiveness of our proposed approach by performing a series of emulation experiments on real world data sets collected from different modes of transportation (including walking, running, biking and commuting by car) on mobile devices from two different platforms. Thorough experimental analyses indicate that our system can save significant amounts of battery power compared to the state-of-the-art position tracking systems, while simultaneously maintaining robustness and accuracy bounds as required by diverse location-aware applications.

KW - Global Positioning System

KW - energy conservation

KW - mobile handsets

KW - mobile radio

KW - mobility management (mobile radio)

KW - radio tracking

KW - telecommunication power management

KW - transportation

KW - EnTrackedT system

KW - GPS

KW - battery power

KW - diverse mobile location aware application

KW - energy-efficient continuous trajectory tracking

KW - energy-efficient sensor management

KW - mobile device

KW - on-device sensor

KW - trajectory data sampling

KW - user transportation mode

KW - Accuracy

KW - Power demand

KW - Robustness

KW - Target tracking

KW - Trajectory

KW - Energy-efficiency

KW - positioning

KW - sensor management

KW - trajectory

KW - trajectory simplification

U2 - 10.1109/TMC.2014.2318712

DO - 10.1109/TMC.2014.2318712

M3 - Journal article

VL - 14

SP - 430

EP - 443

JO - IEEE Transactions on Mobile Computing

JF - IEEE Transactions on Mobile Computing

SN - 1536-1233

IS - 2

ER -