# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
Footnote †: This paper is an extended version of a conference paper [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON] CHACh: Clustering Heuristic and Channel Assignment for IEEE 802.11s Mesh Networks. In Proceedings of the IEEE 9 th Annual Information Technology, Electronics & Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 1-3 November 2018; pp. 1-7. doi:10.1109/IEMCON.2018.8615043. [PERSON]  
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Footnote 10: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))  
Footnote 11: [[https://www.mdpi.com/journals/pdf/10.10](https://www.mdpi.com/journals/pdf/10.10)]([https://www.mdpi.com/journals/pdf/10.10](https://www.mdpi.com/journals/pdf/10.10)). transmitted. However, unicast message forwarding across multiple hops also requires the corresponding number of individual transmissions, so that a mesh path traverses multiple collision domains. This is accompanied by the overhead traffic of the WLAN link layer, which is generated, e.g., by periodic announcements (beacons) of the mesh nodes or in the context of the routing protocol. Consequently, the communication latency and transmission error probability inevitably increase with the number of nodes and average path length in the network. These constraints significantly determine the performance of services and applications in the mesh network. They must therefore be considered and dealt with as part of the network orchestration and management. An established methodology for scalable network structuring and the approach of many research works is to partition the mesh network into independent regions, called clusters. This is done, for example, by choosing appropriate cluster heads, each of which is responsible only for managing its individual cluster [9; 10; 11]. In combination with clustering approaches, channel selection strategies can result in more efficient use of the available spectrum [12; 13; 14]. The WLAN technology mainly uses channels in unlicensed ISM bands in the 2.4 and 5 GHz frequency range. Neighboring clusters configured on non-overlapping (orthogonal) channels always operate in separate collision domains, regardless of their physical proximity. For this purpose, the clusters must have sufficient frequency spacing, which depends on the selected channel bandwidth. As a result, media access can take place without mutual interference and thus completely in parallel. A combined approach to cluster formation and channel selection promises more efficient use of communication resources by distributed applications, especially if they can be appropriately mapped to the resulting clusters. In a previous work by the authors, a centralized monitoring solution for 802.11s networks was developed, where a dedicated node collects status information of all other nodes [15]. However, as network size and mesh path length increase, the application becomes increasingly inefficient. Moreover, the central node represents a bottleneck and Single Point of Failure. Practical research has shown that a decentralized approach is necessary to ensure the scalability and resilience of the monitoring solution [16]. Thus, the task of status monitoring could be implemented in a decentralized manner by multiple cluster heads. The example in Figure 1 contrasts a centralized data retrieval in Figure 1a with a decentralized approach in Figure 1b that uses clusters on orthogonal channels. Here, the centrally positioned node acts as a second cluster head, fetching information for its part of the network (green transmissions) in parallel with the data query of the administrative access node (red transmissions).