Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -