Standard
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. Cham: Springer, 2021. p. 191-200 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 345).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
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, Cham, pp. 191-200.
https://doi.org/10.1007/978-3-030-67369-7_14
APA
Vancouver
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. Cham: Springer. 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
Author
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. Cham : Springer, 2021. pp. 191-200 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).
Bibtex
@inproceedings{212dd6982cd14fbca53961e872202329,
title = "Energy Efficient Adaptive GPS Sampling Using Accelerometer Data",
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%.",
keywords = "Accelerometer, Adaptive sampling, Cognitive IoT, GPS, Internet of Things",
author = "Saad Ezzini and Ismail Berrada",
year = "2021",
month = jan,
day = "31",
doi = "10.1007/978-3-030-67369-7_14",
language = "English",
isbn = "9783030673680",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer",
pages = "191--200",
editor = "Luca Foschini and {El Kamili}, Mohamed",
booktitle = "Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings",
}
RIS
TY - GEN
T1 - Energy Efficient Adaptive GPS Sampling Using Accelerometer Data
AU - Ezzini, Saad
AU - Berrada, Ismail
PY - 2021/1/31
Y1 - 2021/1/31
N2 - 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%.
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%.
KW - Accelerometer
KW - Adaptive sampling
KW - Cognitive IoT
KW - GPS
KW - Internet of Things
U2 - 10.1007/978-3-030-67369-7_14
DO - 10.1007/978-3-030-67369-7_14
M3 - Conference contribution/Paper
SN - 9783030673680
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 191
EP - 200
BT - Ad Hoc Networks - 12th EAI International Conference, ADHOCNETS 2020, Proceedings
A2 - Foschini, Luca
A2 - El Kamili, Mohamed
PB - Springer
CY - Cham
ER -
