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Experiences with eNav: a low-power vehicular navigation system

Published: 07 September 2015 Publication History
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  • Abstract

    This paper presents experiences with eNav, a smartphone-based vehicular GPS navigation system that has an energy-saving location sensing mode capable of drastically reducing navigation energy needs. Traditional navigation systems sample the phone's GPS at a fixed rate (usually around 1Hz), regardless of factors such as current vehicle speed and distance from the next navigation waypoint. This practice results in a large energy consumption and unnecessarily reduces the attainable length of a navigation session, if the phone is left unplugged. The paper investigates two questions. First, would drivers be willing to sacrifice some of the affordances of modern navigation systems in order to prolong battery life? Second, how much energy could be saved using straightforward alternative localization mechanisms, applied to complement GPS for vehicular navigation? According to a survey we conducted of 500 drivers, as much as 91% of drivers said they would like to have a vehicular navigation application with an energy saving mode. To meet this need, eNav exploits on-board accelerometers for approximate location sensing when the vehicle is sufficiently far from the next navigation waypoint (or is stopped). A user test-study of eNav shows that it results in roughly the same user experience as standard GPS navigation systems, while reducing navigation energy consumption by almost 80%. We conclude that drivers find an energy-saving mode on phone-based vehicular navigation applications desirable, even at the expense of some loss of functionality, and that significant savings can be achieved using straightforward location sensing mechanisms that avoid frequent GPS sampling.

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    • (2023)Do Social Aspects Affect Built-in Car Navigation Habits? A Stereotype StudySustainability10.3390/su1506520315:6(5203)Online publication date: 15-Mar-2023
    • (2023)Neural Network Models for Time Series DataArtificial Intelligence for Edge Computing10.1007/978-3-031-40787-1_1(3-25)Online publication date: 4-Aug-2023
    • (2022)What Does the Ideal Built-In Car Navigation System Look Like?—An Investigation in the Central European RegionApplied Sciences10.3390/app1208371612:8(3716)Online publication date: 7-Apr-2022
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    1. Experiences with eNav: a low-power vehicular navigation system

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      cover image ACM Conferences
      UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
      September 2015
      1302 pages
      ISBN:9781450335744
      DOI:10.1145/2750858
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      Published: 07 September 2015

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      Author Tags

      1. GPS
      2. dead-reckoning
      3. low-power
      4. navigation
      5. smartphone

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      • Yahoo! Japan
      • SIGMOBILE
      • FX Palo Alto Laboratory, Inc.
      • ACM
      • Rakuten Institute of Technology
      • Microsoft
      • Bell Labs
      • SIGCHI
      • Panasonic
      • Telefónica
      • ISTC-PC

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      UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;
      Overall Acceptance Rate 764 of 2,912 submissions, 26%

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      Cited By

      View all
      • (2023)Do Social Aspects Affect Built-in Car Navigation Habits? A Stereotype StudySustainability10.3390/su1506520315:6(5203)Online publication date: 15-Mar-2023
      • (2023)Neural Network Models for Time Series DataArtificial Intelligence for Edge Computing10.1007/978-3-031-40787-1_1(3-25)Online publication date: 4-Aug-2023
      • (2022)What Does the Ideal Built-In Car Navigation System Look Like?—An Investigation in the Central European RegionApplied Sciences10.3390/app1208371612:8(3716)Online publication date: 7-Apr-2022
      • (2021)Urban Map Inference by Pervasive Vehicular Sensing Systems with Complementary MobilityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34480765:1(1-24)Online publication date: 30-Mar-2021
      • (2021)GazMon: Eye Gazing Enabled Driving Behavior Monitoring and PredictionIEEE Transactions on Mobile Computing10.1109/TMC.2019.296276420:4(1420-1433)Online publication date: 1-Apr-2021
      • (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)Privacy-Preserving Truth Discovery in Crowd Sensing SystemsACM Transactions on Sensor Networks10.1145/327750515:1(1-32)Online publication date: 9-Jan-2019
      • (2019)Driver and Passenger Identification From Smartphone DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.284511320:4(1278-1288)Online publication date: Apr-2019
      • (2019)GreenRoute: A Generalizable Fuel-Saving Vehicular Navigation Service2019 IEEE International Conference on Autonomic Computing (ICAC)10.1109/ICAC.2019.00011(1-10)Online publication date: Jun-2019
      • (2018)Energy Efficient Mobile Positioning System Using Adaptive Particle FilterIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.E101.A.997E101.A:6(997-999)Online publication date: 1-Jun-2018
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