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EnAcq: energy-efficient GPS trajectory data acquisition based on improved map matching

Published: 01 November 2011 Publication History

Abstract

Todays versatile mobile devices such as smartphones are increasingly popular platforms for trajectory-based applications such as vehicle tracking, route navigation and geotagged video acquisition. On these battery-powered devices employing a trajectory data acquisition approach that reduces the amount of energy spent but still provides accurate location information is essential for these applications' usability. This paper presents EnAcq, a novel energy-efficient GPS trajectory data acquisition scheme based on improved map matching that addresses two key challenges: providing highly accurate trajectory data and reducing energy consumption. To improve the precision of trajectory data, EnAcq utilizes an improved Hidden Markov Model (HMM)-based map matching algorithm which can find candidate matches for each GPS location sample point without using the traditionally necessary range query and determine the most likely route the mobile device (e.g., in a vehicle) has travelled. To avoid unnecessary energy consumption, EnAcq adopts an adaptive GPS sampling method which adjusts the sampling period based on the device's current motion state. On a public real-world dataset, we demonstrate via experimental results that EnAcq is able to yield accurate trajectory data while avoiding unnecessary energy consumption.

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Cited By

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  • (2024)Research on Vehicle GPS Dynamic Sampling Based on Deep Reinforcement Learning2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT)10.1109/ISCIPT61983.2024.10672728(492-498)Online publication date: 24-May-2024
  • (2023)Problems with quantitative categorization: An argument for qualitative approachesEnvironment and Planning F10.1177/263498252311631402:3(331-349)Online publication date: 24-Apr-2023
  • (2022)A Comprehensive Review of Map-Matching TechniquesInternational Journal of Web Services Research10.4018/IJWSR.30624319:1(1-32)Online publication date: 1-Jan-2022
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    cover image ACM Conferences
    GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2011
    559 pages
    ISBN:9781450310314
    DOI:10.1145/2093973
    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]

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    Published: 01 November 2011

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    Author Tags

    1. GPS
    2. energy-efficiency
    3. experiments
    4. map matching
    5. trajectory data

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    Overall Acceptance Rate 257 of 1,238 submissions, 21%

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    View all
    • (2024)Research on Vehicle GPS Dynamic Sampling Based on Deep Reinforcement Learning2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT)10.1109/ISCIPT61983.2024.10672728(492-498)Online publication date: 24-May-2024
    • (2023)Problems with quantitative categorization: An argument for qualitative approachesEnvironment and Planning F10.1177/263498252311631402:3(331-349)Online publication date: 24-Apr-2023
    • (2022)A Comprehensive Review of Map-Matching TechniquesInternational Journal of Web Services Research10.4018/IJWSR.30624319:1(1-32)Online publication date: 1-Jan-2022
    • (2022)ASRL: An Adaptive GPS Sampling Method Using Deep Reinforcement Learning2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00042(153-158)Online publication date: Jun-2022
    • (2022)From driving trajectories to driving paths: a survey on map-matching AlgorithmsCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00101-w4:3(252-267)Online publication date: 23-May-2022
    • (2020)An Energy-Efficient Method with Dynamic GPS Sampling Rate for Transport Mode Detection and Trip ReconstructionAdvances in Artificial Intelligence10.1007/978-3-030-47358-7_42(408-419)Online publication date: 13-May-2020
    • (2018)Feature-based Map Matching for Low-Sampling-Rate GPS TrajectoriesACM Transactions on Spatial Algorithms and Systems10.1145/32230494:2(1-24)Online publication date: 10-Aug-2018
    • (2018)A Green Self-Adaptive Approach for Online Map MatchingIEEE Access10.1109/ACCESS.2018.28698526(51456-51469)Online publication date: 2018
    • (2018)Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environmentMeasurement10.1016/j.measurement.2018.01.011118(83-95)Online publication date: Mar-2018
    • (2018)Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receiversTelecommunications Systems10.1007/s11235-017-0401-568:3(425-443)Online publication date: 1-Jul-2018
    • Show More Cited By

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