research-article

Energy-aware medium access control for energy-harvesting machine-to-machine networks

Authors:

Youngbae KoAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 2399 - 2405

https://doi.org/10.1145/3297280.3297516

Published: 08 April 2019 Publication History

Abstract

Energy-harvesting is being actively researched for the Machine-to-Machine networks. Without replacement of battery, energy-harvesting enables nodes (or machines) to perform their work permanently by recharging energy store periodically from an external source. After performing given tasks, in many applications, each energy-harvesting node transmits data to the gateway node. Here, the difference in harvested/consumed energy could lead to sub-optimal communication due to depletion of energy. In this paper, we design an energy-aware medium access control scheme for energy-harvesting machine-to-machine networks. The proposed algorithm controls delivery error rate due to energy depletion through limited contention among energy-exhausting nodes, and maximize slot efficiency to minimize overall communication duration. Maximizing slot efficiency is implemented in two ways: utility-based and learning-based. Simulation studies have shown that the proposed schemes effectively minimize delivery error rate and communication period, outperforming the existing strategies in the literature.

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Cited By

Salh AAudah LAbdullah QAbdullah NShah NMukred JAl-Ameri SHamzah S(2023)Development of a fully data-driven artificial intelligence and deep learning for URLLC application in 6G wireless systems: A surveyTHE 5TH INTERNATIONAL CONFERENCE ON BIOSCIENCE AND BIOTECHNOLOGY10.1063/5.0122677(080003)Online publication date: 2023
https://doi.org/10.1063/5.0122677
Li YChin K(2021)Random Channel Access Protocols for SIC Enabled Energy Harvesting IoTs NetworksIEEE Systems Journal10.1109/JSYST.2020.297830215:2(2269-2280)Online publication date: Jun-2021
https://doi.org/10.1109/JSYST.2020.2978302
Salh AAudah LShah NAlhammadi AAbdullah QKim YAl-Gailani SHamzah SEsmail BAlmohammedi A(2021)A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless SystemsIEEE Access10.1109/ACCESS.2021.30697079(55098-55131)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3069707

Index Terms

Energy-aware medium access control for energy-harvesting machine-to-machine networks
1. Networks

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cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2019

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National Research Foundation of Korea

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SAC '19

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SIGAPP

SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Salh AAudah LAbdullah QAbdullah NShah NMukred JAl-Ameri SHamzah S(2023)Development of a fully data-driven artificial intelligence and deep learning for URLLC application in 6G wireless systems: A surveyTHE 5TH INTERNATIONAL CONFERENCE ON BIOSCIENCE AND BIOTECHNOLOGY10.1063/5.0122677(080003)Online publication date: 2023
https://doi.org/10.1063/5.0122677
Li YChin K(2021)Random Channel Access Protocols for SIC Enabled Energy Harvesting IoTs NetworksIEEE Systems Journal10.1109/JSYST.2020.297830215:2(2269-2280)Online publication date: Jun-2021
https://doi.org/10.1109/JSYST.2020.2978302
Salh AAudah LShah NAlhammadi AAbdullah QKim YAl-Gailani SHamzah SEsmail BAlmohammedi A(2021)A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless SystemsIEEE Access10.1109/ACCESS.2021.30697079(55098-55131)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3069707

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