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Energy Efficient Adaptive GPS Sampling Using Accelerometer Data

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Ad Hoc Networks (ADHOCNETS 2020)

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Abstract

Internet of Things (IoT) is a major component of the connected world. With billions of battery-powered devices connected to the internet, energy and bandwidth consumption become significant issues. Embedding intelligence/cognition in the apparatus is recognized as one of the solutions to mitigate these issues. Global Positioning System (GPS) is recognized as one of the most energy-consuming mobile sensors in smart vehicles/systems. This paper proposes a smart adaptive sampling method for GPS sensors using the accelerometer data. Our approach adapts the sampling frequency of the GPS sensor according to the data stream of the accelerometer, without causing significant distortions to the data. In our experiment, we could reduce the GPS sensing by 78% while preserving an accuracy of 91.4%.

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Author information

Authors and Affiliations

  1. University of Luxembourg, SnT, Luxembourg, Luxembourg

    Saad Ezzini

  2. Mohammed VI Polytechnic University, SCCS, Benguérir, Morocco

    Ismail Berrada

Authors
  1. Saad Ezzini
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  2. Ismail Berrada
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Corresponding author

Correspondence to Saad Ezzini .

Editor information

Editors and Affiliations

  1. DISI, University of Bologna, Bologna, Bologna, Italy

    Luca Foschini

  2. Higher School of Technology, Hassan II University of Casablanca, Casablanca, Morocco

    Mohamed El Kamili

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ezzini, S., Berrada, I. (2021). Energy Efficient Adaptive GPS Sampling Using Accelerometer Data. In: Foschini, L., El Kamili, M. (eds) Ad Hoc Networks. ADHOCNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-67369-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-67369-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67368-0

  • Online ISBN: 978-3-030-67369-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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