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MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network

The development of Medium Access Control (MAC) protocols for Internet of Things should consider various aspects such as energy saving, scalability for a wide number of nodes, and grouping awareness. Although numerous protocols consider these aspects in the limited view of handling the medium access,...

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Autores principales: Sameer Sadeq, Abdulrahman, Hassan, Rosilah, Hafizah Mohd Aman, Azana, Sallehudin, Hasimi, Allehaibi, Khalid, Albogami, Nasser, Satria Prabuwono, Anton
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627235/
https://www.ncbi.nlm.nih.gov/pubmed/34901420
http://dx.doi.org/10.7717/peerj-cs.733
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author Sameer Sadeq, Abdulrahman
Hassan, Rosilah
Hafizah Mohd Aman, Azana
Sallehudin, Hasimi
Allehaibi, Khalid
Albogami, Nasser
Satria Prabuwono, Anton
author_facet Sameer Sadeq, Abdulrahman
Hassan, Rosilah
Hafizah Mohd Aman, Azana
Sallehudin, Hasimi
Allehaibi, Khalid
Albogami, Nasser
Satria Prabuwono, Anton
author_sort Sameer Sadeq, Abdulrahman
collection PubMed
description The development of Medium Access Control (MAC) protocols for Internet of Things should consider various aspects such as energy saving, scalability for a wide number of nodes, and grouping awareness. Although numerous protocols consider these aspects in the limited view of handling the medium access, the proposed Grouping MAC (GMAC) exploits prior knowledge of geographic node distribution in the environment and their priority levels. Such awareness enables GMAC to significantly reduce the number of collisions and prolong the network lifetime. GMAC is developed on the basis of five cycles that manage data transmission between sensors and cluster head and between cluster head and sink. These two stages of communication increase the efficiency of energy consumption for transmitting packets. In addition, GMAC contains slot decomposition and assignment based on node priority, and, therefore, is a grouping-aware protocol. Compared with standard benchmarks IEEE 802.15.4 and industrial automation standard 100.11a and user-defined grouping, GMAC protocols generate a Packet Delivery Ratio (PDR) higher than 90%, whereas the PDR of benchmark is as low as 75% in some scenarios and 30% in others. In addition, the GMAC accomplishes lower end-to-end (e2e) delay than the least e2e delay of IEEE with a difference of 3 s. Regarding energy consumption, the consumed energy is 28.1 W/h for GMAC-IEEE Energy Aware (EA) and GMAC-IEEE, which is less than that for IEEE 802.15.4 (578 W/h) in certain scenarios.
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spelling pubmed-86272352021-12-10 MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network Sameer Sadeq, Abdulrahman Hassan, Rosilah Hafizah Mohd Aman, Azana Sallehudin, Hasimi Allehaibi, Khalid Albogami, Nasser Satria Prabuwono, Anton PeerJ Comput Sci Computer Networks and Communications The development of Medium Access Control (MAC) protocols for Internet of Things should consider various aspects such as energy saving, scalability for a wide number of nodes, and grouping awareness. Although numerous protocols consider these aspects in the limited view of handling the medium access, the proposed Grouping MAC (GMAC) exploits prior knowledge of geographic node distribution in the environment and their priority levels. Such awareness enables GMAC to significantly reduce the number of collisions and prolong the network lifetime. GMAC is developed on the basis of five cycles that manage data transmission between sensors and cluster head and between cluster head and sink. These two stages of communication increase the efficiency of energy consumption for transmitting packets. In addition, GMAC contains slot decomposition and assignment based on node priority, and, therefore, is a grouping-aware protocol. Compared with standard benchmarks IEEE 802.15.4 and industrial automation standard 100.11a and user-defined grouping, GMAC protocols generate a Packet Delivery Ratio (PDR) higher than 90%, whereas the PDR of benchmark is as low as 75% in some scenarios and 30% in others. In addition, the GMAC accomplishes lower end-to-end (e2e) delay than the least e2e delay of IEEE with a difference of 3 s. Regarding energy consumption, the consumed energy is 28.1 W/h for GMAC-IEEE Energy Aware (EA) and GMAC-IEEE, which is less than that for IEEE 802.15.4 (578 W/h) in certain scenarios. PeerJ Inc. 2021-11-22 /pmc/articles/PMC8627235/ /pubmed/34901420 http://dx.doi.org/10.7717/peerj-cs.733 Text en © 2021 Sameer Sadeq et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Computer Networks and Communications
Sameer Sadeq, Abdulrahman
Hassan, Rosilah
Hafizah Mohd Aman, Azana
Sallehudin, Hasimi
Allehaibi, Khalid
Albogami, Nasser
Satria Prabuwono, Anton
MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network
title MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network
title_full MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network
title_fullStr MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network
title_full_unstemmed MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network
title_short MAC protocol with grouping awareness GMAC for large scale Internet-of-Things network
title_sort mac protocol with grouping awareness gmac for large scale internet-of-things network
topic Computer Networks and Communications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627235/
https://www.ncbi.nlm.nih.gov/pubmed/34901420
http://dx.doi.org/10.7717/peerj-cs.733
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