Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/1999995.2000025acmconferencesArticle/Chapter ViewAbstractPublication PagesmobisysConference Proceedingsconference-collections
research-article

Energy-efficient trajectory tracking for mobile devices

Published: 28 June 2011 Publication History

Abstract

Emergent location-aware applications often require tracking trajectories of mobile devices over a long period of time. To be useful, the tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, when trajectory information needs to be sent to a remote server, on-device simplification of the trajectories is needed to reduce the amount of data transmission. While there has recently been a lot of work on energy-efficient position tracking, the energy-efficient tracking of trajectories has not been addressed in previous work. In this paper we propose a novel on-device sensor management strategy and a set of trajectory updating protocols which intelligently determine when to sample different sensors (accelerometer, compass and GPS) and when data should be simplified and sent to a remote server. The system is configurable with regards to accuracy requirements and provides a unified framework for both position and trajectory tracking. We demonstrate the effectiveness of our approach by emulation experiments on real world data sets collected from different modes of transportation (walking, running, biking and commuting by car) as well as by validating with a real-world deployment. The results demonstrate that our approach is able to provide considerable savings in the battery consumption compared to a state-of-the-art position tracking system while at the same time maintaining the accuracy of the resulting trajectory, i.e., support of specific accuracy requirements and different types of applications can be ensured.

References

[1]
G. Ananthanarayanan, M. Haridasan, I. Mohomed, D. Terry, and C. A. Thekkath. Startrack: a framework for enabling track-based applications. In Proc. 7th Intl. Conf. Mobile Systems, Applications, and Services (MobiSys 2009), pages 207--220, 2009.
[2]
L. Becker, H. Blunck, K. H. Hinrichs, and J. Vahrenhold. A framework for moving objects. In Proc. 15th Intl. Conf. Database and Expert Systems Applications (DEXA '04), volume 3180 of Lecture Notes in Computer Science, pages 854--863. Springer, 2004.
[3]
H. Blunck, K. H. Hinrichs, J. Sondern, and J. Vahrenhold. Modeling and engineering algorithms for mobile data. In Progress in Spatial Data Handling: Proc. 12th Intl. Symp. Spatial Data Handling (SDH '06), pages 61--77, 2006.
[4]
H. Cao, O. Wolfson, and G. Trajcevski. Spatio-temporal data reduction with deterministic error bounds. The VLDB Journal, 15(3):211--228, 2006.
[5]
I. Constandache, R. R. Choudhury, and I. Rhee. Towards mobile phone localization without war-driving. In Proc. 29th IEEE Intl. Conf. Computer Communications (INFOCOM), pages 2321--2329, 2010.
[6]
S. B. Eisenman, E. Miluzzo, N. D. Lane, R. A. Peterson, G.-S. Ahn, and A. T. Campbell. The bikenet mobile sensing system for cyclist experience mapping. In Proc. 5th Intl. Conf. Embedded networked sensor systems, pages 87--101. ACM, 2007.
[7]
R. H. Guting and M. Schneider. Moving Objects Databases. Morgan Kaufmann Publishers, 2005.
[8]
J. Jensen, K. Schougaard, M. Kjaergaard, and T. Toftkjaer. PerPos: a Translucent Positioning Middleware Supporting Adaptation of Internal Positioning Processes. In Proc. 11th ACM/IFIP/USENIX Intl. Middleware Conf. (Middleware 2010), 2010.
[9]
D. H. Kim, Y. Kim, D. Estrin, and M. B. Srivastava. Sensloc: sensing everyday places and paths using less energy. In Proc. 8th ACM Conf. Embedded Networked Sensor Systems, pages 43--56, 2010.
[10]
M. B. Kjaergaard. A Taxonomy for Radio Location Fingerprinting. In Proc. 3rd Intl. Symp. Location and Context Awareness, 2007.
[11]
M. B. Kjaergaard. On Improving the Energy Efficiency and Robustness of Position Tracking for Mobile Devices. In Proc. 7th Intl. Conf. Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2010), 2010.
[12]
M. B. Kjaergaard. Minimizing the Power Consumption of Location-Based Services on Mobile Phones. IEEE Pervasive Computing, To appear.
[13]
M. B. Kjaergaard, J. Langdal, T. Godsk, and T. Toftkjaer. EnTracked: energy-efficient robust position tracking for mobile devices. In Proc. 7th Intl. Conf. Mobile systems, applications, and services (MobiSys'09), pages 221--234, 2009.
[14]
M. B. Kjaergaard and K. Weckemann. PosQ: Unsupervised Fingerprinting and Visualization of GPS Positioning Quality. In Proc. 2nd Intl. Conf. Mobile Computing, Applications, and Services (MobiCASE 2010), 2010.
[15]
R. Lange, T. Farrell, F. Durr, and K. Rothermel. Remote real-time trajectory simplification. In PERCOM '09: Proc. IEEE Intl. Conf. Pervasive Computing and Communications, pages 1--10, 2009.
[16]
K. Lin, A. Kansal, D. Lymberopoulos, and F. Zhao. Energy-accuracy trade-off for continuous mobile device location. In Proc. 8th Intl. Conf. Mobile Systems, Applications, and Services (MobiSys 2010), pages 285--298, 2010.
[17]
N. Meratnia and R. de By. Spatiotemporal Compression Techniques for Moving Point Objects. In Advances in Database Technology - EDBT 2004, volume 2992 of Lecture Notes in Computer Science, pages 561--562. Springer, Berlin, Heidelberg, 2004.
[18]
S. Minamimoto, S. Fujii, H. Yamaguchi, and T. Higashino. Local Map Generation using Position and Communication History of Mobile Nodes. In Proc. 2010 IEEE Intl. Conf. Pervasive Computing and Communications, pages 2--10, 2010.
[19]
M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West, and P. Boda. Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In Proc. 7th Intl. Conf. Mobile systems, applications, and services, pages 55--68. ACM, 2009.
[20]
J. Paek, J. Kim, and R. Govindan. Energy-efficient rate-adaptive gps-based positioning for smartphones. In Proc. 8th Intl. Conf. Mobile Systems, Applications, and Services (MobiSys 2010), pages 299--314, 2010.
[21]
D. Pfoser and C. S. Jensen. Capturing the uncertainty of moving-object representations. In Proc. 6th Intl. Symp. Advances in Spatial Databases (SSD), volume 1651 of Lecture Notes in Computer Science, pages 111--132. Springer, Berlin, 1999.
[22]
J. Ryder, B. Longstaff, S. Reddy, and D. Estrin. Ambulation: A tool for monitoring mobility patterns over time using mobile phones. In Intl. Conf. Computational Science and Engineering, pages 927--931, 2009.
[23]
G. Trajcevski, O. Wolfson, K. Hinrichs, and S. Chamberlain. Managing uncertainty in moving objects databases. ACM Transactions on Database Systems, 29(3):463--507, 2004.
[24]
T. Vincenty. Direct and Inverse Solutions of Geodesics on the Ellipsoid with Application of Nested Equations. Survey Review, 23(176):88--93, 1975.
[25]
O. Wolfson, S. Chamberlain, S. Dao, L. Jiang, and G. Mendez. Cost and imprecision in modeling the position of moving objects. In Proc. 14th Intl. Conf. Data Engineering (ICDE '98), pages 588--596. IEEE Computer Society, 1998.
[26]
Z. Zhuang, K.-H. Kim, and J. P. Singh. Improving energy efficiency of location sensing on smartphones. In Proc. 8th Intl. Conf. Mobile Systems, Applications, and Services (MobiSys 2010), pages 315--330, 2010.

Cited By

View all
  • (2024)ORION: Verification of drone trajectories via remote identification messagesFuture Generation Computer Systems10.1016/j.future.2024.06.047160(869-878)Online publication date: Nov-2024
  • (2024)TraMap: SLAM-Based Trajectory Generation and Optimization for Emergency ScenariosQuality, Reliability, Security and Robustness in Heterogeneous Systems10.1007/978-3-031-65123-6_34(453-470)Online publication date: 20-Aug-2024
  • (2023)A Machine Learning Based Energy-Efficient Indoor Multiple IoT Device Tracking Algorithm Based on Correlated Group Determination2023 IEEE 9th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT58464.2023.10539405(1-6)Online publication date: 12-Oct-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MobiSys '11: Proceedings of the 9th international conference on Mobile systems, applications, and services
June 2011
430 pages
ISBN:9781450306430
DOI:10.1145/1999995
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2011

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. GPS
  2. energy-efficiency
  3. mobile computing
  4. positioning
  5. trajectory simplification

Qualifiers

  • Research-article

Conference

MobiSys'11
Sponsor:

Acceptance Rates

Overall Acceptance Rate 274 of 1,679 submissions, 16%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)17
  • Downloads (Last 6 weeks)3
Reflects downloads up to 15 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)ORION: Verification of drone trajectories via remote identification messagesFuture Generation Computer Systems10.1016/j.future.2024.06.047160(869-878)Online publication date: Nov-2024
  • (2024)TraMap: SLAM-Based Trajectory Generation and Optimization for Emergency ScenariosQuality, Reliability, Security and Robustness in Heterogeneous Systems10.1007/978-3-031-65123-6_34(453-470)Online publication date: 20-Aug-2024
  • (2023)A Machine Learning Based Energy-Efficient Indoor Multiple IoT Device Tracking Algorithm Based on Correlated Group Determination2023 IEEE 9th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT58464.2023.10539405(1-6)Online publication date: 12-Oct-2023
  • (2023)A Context- and Trajectory-Based Destination Prediction of Public Transportation UsersIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2021.313277215:1(300-317)Online publication date: Jan-2023
  • (2021)SmartLOCProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949725:4(1-24)Online publication date: 30-Dec-2021
  • (2021)Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing PlatformsIEEE Internet of Things Journal10.1109/JIOT.2020.30114618:3(1334-1346)Online publication date: 1-Feb-2021
  • (2020)A Survey of Hierarchical Energy Optimization for Mobile Edge ComputingACM Computing Surveys10.1145/337893553:2(1-44)Online publication date: 17-Apr-2020
  • (2020)Energy- and Mobility-Aware Scheduling for Perpetual Trajectory TrackingIEEE Transactions on Mobile Computing10.1109/TMC.2019.289533619:3(566-580)Online publication date: 1-Mar-2020
  • (2019)Battery Efficient Location Strategy on Android2019 International Conference on Military Communications and Information Systems (ICMCIS)10.1109/ICMCIS.2019.8842797(1-7)Online publication date: May-2019
  • (2019)Neural correlates of individual differences in affective benefit of real-life urban green space exposureNature Neuroscience10.1038/s41593-019-0451-yOnline publication date: 29-Jul-2019
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media