Energy Efficient Adaptive GPS Sampling Using Accelerometer Data

Saad Ezzini; Ismail Berrada

doi:10.1007/978-3-030-67369-7_14

Energy Efficient Adaptive GPS Sampling Using Accelerometer Data

Saad Ezzini^*
, Ismail Berrada

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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%.

Original language	English
Title of host publication	Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings
Editors	Luca Foschini, Mohamed El Kamili
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	191-200
Number of pages	10
ISBN (Print)	9783030673680
DOIs	https://doi.org/10.1007/978-3-030-67369-7_14
State	Published - 2021
Externally published	Yes
Event	12th EAI International Conference on Ad Hoc Networks, AdHocNets 2020 - Paris, France Duration: 17 Nov 2020 → 17 Nov 2020

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	345
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	12th EAI International Conference on Ad Hoc Networks, AdHocNets 2020
Country/Territory	France
City	Paris
Period	17/11/20 → 17/11/20

Bibliographical note

Publisher Copyright:
© 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Accelerometer
Adaptive sampling
Cognitive IoT
GPS
Internet of Things

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1007/978-3-030-67369-7_14

Cite this

Ezzini, S., & Berrada, I. (2021). Energy Efficient Adaptive GPS Sampling Using Accelerometer Data. In L. Foschini, & M. El Kamili (Eds.), Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings (pp. 191-200). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 345). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-67369-7_14

Ezzini, Saad ; Berrada, Ismail. / Energy Efficient Adaptive GPS Sampling Using Accelerometer Data. Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings. editor / Luca Foschini ; Mohamed El Kamili. Springer Science and Business Media Deutschland GmbH, 2021. pp. 191-200 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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Ezzini, S & Berrada, I 2021, Energy Efficient Adaptive GPS Sampling Using Accelerometer Data. in L Foschini & M El Kamili (eds), Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 345, Springer Science and Business Media Deutschland GmbH, pp. 191-200, 12th EAI International Conference on Ad Hoc Networks, AdHocNets 2020, Paris, France, 17/11/20. https://doi.org/10.1007/978-3-030-67369-7_14

Energy Efficient Adaptive GPS Sampling Using Accelerometer Data. / Ezzini, Saad; Berrada, Ismail.
Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings. ed. / Luca Foschini; Mohamed El Kamili. Springer Science and Business Media Deutschland GmbH, 2021. p. 191-200 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 345).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ezzini, Saad

AU - Berrada, Ismail

PY - 2021

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AB - 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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KW - Adaptive sampling

KW - Cognitive IoT

KW - GPS

KW - Internet of Things

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BT - Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings

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ER -

Ezzini S, Berrada I. Energy Efficient Adaptive GPS Sampling Using Accelerometer Data. In Foschini L, El Kamili M, editors, Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 191-200. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-67369-7_14

Energy Efficient Adaptive GPS Sampling Using Accelerometer Data

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this