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HybridBaro: Mining Driving Routes Using Barometer Sensor of Smartphone

Title
HybridBaro: Mining Driving Routes Using Barometer Sensor of Smartphone
Authors
Won, MyounggyuMishra, AshutoshSon, Sang H.
DGIST Authors
Won, Myounggyu; Son, Sang H.
Issue Date
2017
Citation
IEEE Sensors Journal
Type
Article
Article Type
Article in Press
Keywords
Advanced Driver Assistance Systems (ADAS)BarometersClimate ControlDriver Information SystemsDriving Route DetectionEnergy EfficiencyFalse Positive RatesGlobal Positioning System (GPS)GyroscopesIntelligent ApplicationsMobile CommunicationMobile CommunicationsMobile ComputingMobile Telecommunication SystemsReal Time SystemsReal Time TrafficsRoute DetectionsSensor Phenomena and CharacterizationSensor SystemsSmartphonesTimely IdentificationTrajectoriesTrajectory
ISSN
1530-437X
Abstract
Recent research showed that human mobility is characterized by reproducible patterns, i.e., humans tend to travel a few known places. Timely identification of these “significant journeys has prospects for emerging intelligent applications like real-time traffic route recommendation and automated HVAC systems. Existing mobile systems, however, utilize energyhungry sensors like GPS and gyroscope to detect significant journeys, which make it hard to keep such systems running to continuously monitor driving routes. To address this issue of energy efficiency without compromising the performance, in this article, a hybrid mobile system based on the barometer sensor of a smartphone is developed. Distinctive elevation signatures of driving routes are captured using the smartphone barometer sensor that is exceptionally energy efficient and position/orientation independent. Degraded accuracy due to flat areas with minimal elevation changes is offset by developing an adaptive algorithm that opportunistically obtains GPS locations for a very short period of time when such flat areas are detected in real time. Using over 150miles of field data, it is demonstrated that the proposed mobile system achieves the mean detection accuracy of 97% with the mean false positive rates of 1.5%. IEEE
URI
http://hdl.handle.net/20.500.11750/4549
DOI
10.1109/JSEN.2017.2734919
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Son, Sang Hyuk RTCPS(Real-Time Cyber-Physical Systems Research) Lab
  • Research Interests
Files:
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Collection:
ETC1. Journal Articles


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