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Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks †

WLAN mesh networks are one of the key technologies for upcoming smart city applications and are characterized by a flexible and low-cost deployment. The standard amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by manag...

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Autores principales: Rethfeldt, Michael, Brockmann, Tim, Beichler, Benjamin, Haubelt, Christian, Timmermann, Dirk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588358/
https://www.ncbi.nlm.nih.gov/pubmed/34770523
http://dx.doi.org/10.3390/s21217215
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author Rethfeldt, Michael
Brockmann, Tim
Beichler, Benjamin
Haubelt, Christian
Timmermann, Dirk
author_facet Rethfeldt, Michael
Brockmann, Tim
Beichler, Benjamin
Haubelt, Christian
Timmermann, Dirk
author_sort Rethfeldt, Michael
collection PubMed
description WLAN mesh networks are one of the key technologies for upcoming smart city applications and are characterized by a flexible and low-cost deployment. The standard amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by management traffic overhead, routing delays, medium contention, and interference are common issues in wireless mesh networks and also apply to IEEE 802.11s networks. Possible solutions proposed in the literature recommend a divide-and-conquer scheme that partitions the network into clusters and forms smaller collision and broadcast domains by assigning orthogonal channels. We present CHaChA (Clustering Heuristic and Channel Assignment), a distributed cross-layer approach for cluster formation and channel assignment that directly integrates the default IEEE 802.11s mesh protocol information and operating modes, retaining unrestricted compliance to the WLAN standard. Our concept proposes further mechanisms for dynamic cluster adaptation, including subsequent cluster joining, isolation and fault detection, and node roaming for cluster balancing. The practical performance of CHaChA is demonstrated in a real-world 802.11s testbed. We first investigate clustering reproducibility, duration, and communication overhead in static network scenarios of different sizes. We then validate our concepts for dynamic cluster adaptation, considering topology changes that are likely to occur during long-term network operation and maintenance.
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spelling pubmed-85883582021-11-13 Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks † Rethfeldt, Michael Brockmann, Tim Beichler, Benjamin Haubelt, Christian Timmermann, Dirk Sensors (Basel) Article WLAN mesh networks are one of the key technologies for upcoming smart city applications and are characterized by a flexible and low-cost deployment. The standard amendment IEEE 802.11s introduces low-level mesh interoperability at the WLAN MAC layer. However, scalability limitations imposed by management traffic overhead, routing delays, medium contention, and interference are common issues in wireless mesh networks and also apply to IEEE 802.11s networks. Possible solutions proposed in the literature recommend a divide-and-conquer scheme that partitions the network into clusters and forms smaller collision and broadcast domains by assigning orthogonal channels. We present CHaChA (Clustering Heuristic and Channel Assignment), a distributed cross-layer approach for cluster formation and channel assignment that directly integrates the default IEEE 802.11s mesh protocol information and operating modes, retaining unrestricted compliance to the WLAN standard. Our concept proposes further mechanisms for dynamic cluster adaptation, including subsequent cluster joining, isolation and fault detection, and node roaming for cluster balancing. The practical performance of CHaChA is demonstrated in a real-world 802.11s testbed. We first investigate clustering reproducibility, duration, and communication overhead in static network scenarios of different sizes. We then validate our concepts for dynamic cluster adaptation, considering topology changes that are likely to occur during long-term network operation and maintenance. MDPI 2021-10-29 /pmc/articles/PMC8588358/ /pubmed/34770523 http://dx.doi.org/10.3390/s21217215 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rethfeldt, Michael
Brockmann, Tim
Beichler, Benjamin
Haubelt, Christian
Timmermann, Dirk
Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks †
title Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks †
title_full Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks †
title_fullStr Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks †
title_full_unstemmed Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks †
title_short Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks †
title_sort adaptive multi-channel clustering in ieee 802.11s wireless mesh networks †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588358/
https://www.ncbi.nlm.nih.gov/pubmed/34770523
http://dx.doi.org/10.3390/s21217215
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