LAPSE:2023.19261
Published Article
LAPSE:2023.19261
Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices
March 9, 2023
Abstract
A Long-Range Wide Area Network (LoRaWAN) is one of the most efficient technologies and is widely adopted for the Internet of Things (IoT) applications. The IoT consists of massive End Devices (EDs) deployed over large geographical areas, forming a large environment. LoRaWAN uses an Adaptive Data Rate (ADR), targeting static EDs. However, the ADR is affected when the channel conditions between ED and Gateway (GW) are unstable due to shadowing, fading, and mobility. Such a condition causes massive packet loss, which increases the convergence time of the ADR. Therefore, we address the convergence time issue and propose a novel ADR at the network side to lower packet losses. The proposed ADR is evaluated through extensive simulation. The results show an enhanced convergence time compared to the state-of-the-art ADR method by reducing the packet losses and retransmission under dynamic mobile LoRaWAN network.
A Long-Range Wide Area Network (LoRaWAN) is one of the most efficient technologies and is widely adopted for the Internet of Things (IoT) applications. The IoT consists of massive End Devices (EDs) deployed over large geographical areas, forming a large environment. LoRaWAN uses an Adaptive Data Rate (ADR), targeting static EDs. However, the ADR is affected when the channel conditions between ED and Gateway (GW) are unstable due to shadowing, fading, and mobility. Such a condition causes massive packet loss, which increases the convergence time of the ADR. Therefore, we address the convergence time issue and propose a novel ADR at the network side to lower packet losses. The proposed ADR is evaluated through extensive simulation. The results show an enhanced convergence time compared to the state-of-the-art ADR method by reducing the packet losses and retransmission under dynamic mobile LoRaWAN network.
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Keywords
adaptive data rate, convergence time, energy consumption, interference, Internet of Things, LoRaWAN, mobility, resource allocation, retransmissions
Subject
Suggested Citation
Anwar K, Rahman T, Zeb A, Saeed Y, Khan MA, Khan I, Ahmad S, Abdelgawad AE, Abdollahian M. Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices. (2023). LAPSE:2023.19261
Author Affiliations
Anwar K: Department of Physical & Numerical Science, Qurtuba University of Science & Information Technology, Peshawar 25000, Pakistan
Rahman T: Department of Physical & Numerical Science, Qurtuba University of Science & Information Technology, Peshawar 25000, Pakistan
Zeb A: Department of Computer Science, Abbottabad University of Science and Technology, Abbottabad 22500, Pakistan
Saeed Y: Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan
Khan MA: Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Korea; Faculty of Computing, Lahore Campus, Riphah School of Computing and Innovation, Riphah International University, Lahore 54000, Pakistan [ORCID]
Khan I: Department of Computer Science, University of Buner, Buner 19290, Pakistan
Ahmad S: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia [ORCID]
Abdelgawad AE: Industrial Engineering Department, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia [ORCID]
Abdollahian M: School of Science, College of Science, Technology, Engineering, Mathematics, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia [ORCID]
Rahman T: Department of Physical & Numerical Science, Qurtuba University of Science & Information Technology, Peshawar 25000, Pakistan
Zeb A: Department of Computer Science, Abbottabad University of Science and Technology, Abbottabad 22500, Pakistan
Saeed Y: Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan
Khan MA: Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Korea; Faculty of Computing, Lahore Campus, Riphah School of Computing and Innovation, Riphah International University, Lahore 54000, Pakistan [ORCID]
Khan I: Department of Computer Science, University of Buner, Buner 19290, Pakistan
Ahmad S: Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia [ORCID]
Abdelgawad AE: Industrial Engineering Department, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia [ORCID]
Abdollahian M: School of Science, College of Science, Technology, Engineering, Mathematics, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia [ORCID]
Journal Name
Energies
Volume
14
Issue
18
First Page
5614
Year
2021
Publication Date
2021-09-07
ISSN
1996-1073
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Original Submission
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PII: en14185614, Publication Type: Journal Article
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LAPSE:2023.19261
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https://doi.org/10.3390/en14185614
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