# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 7 Discussion and Conclusions  
In this work, we present the design of CHaChA, a decentralized clustering and channel selection solution for WLAN mesh networks that specifically takes into account the mesh features introduced in the standard amendment IEEE 802.11s. It uses only standard mechanisms of the 802.11s specification and does not require any modification at the WLAN link layer. The default 802.11s information, retrievable via existing system interfaces on every node, are used to derive the clustering metrics for our approach. Our main contribution is a phase-based algorithm for cluster formation in WLAN mesh backbones, whose nodes nowadays usually have two or more physical WLAN interfaces. Of these, one remains on a dedicated base channel, which always ensures best possible connectivity and serves CHaChA coordination. A secondary interface allows cluster-internal communication on orthogonal channels, which relieves the base channel and reduces interference. Moreover, we present mechanisms for automatic cluster adaptation to topology changes. These include subsequent or repeated cluster joining, mutual isolation and failure detection of nodes, as well as node roaming for cluster balancing. As a cross-layer approach, CHaChA integrates inherently available link and path information of the 802.11s link layer into the application layer to derive topology information and use it for cluster formation and channel selection. In turn, network structure optimization is performed at the application layer by configuring secondary interfaces. The selective instrumentation of standard mechanisms, such as the 802.11s routing protocol HWMP, plays an important role. The use of different variants of the proactive HWMP mode enables, on the one hand, a low-cost node discovery compared to mechanisms at higher protocol layers and, on the other hand, the mutual failure detection between nodes. By investigating our prototype implementation in a real-world testbed using grid topologies with up to 25 nodes, the practicality of the approach was demonstrated. With an occurrence rate of over 90 % for the most frequent cluster constellation in the \(5\times 5\)-node grid, the experiments prove the reproducibility of the results. A conservatively chosen initial parameterization of our algorithm was experimentally adjusted to the test environment, resulting in a clustering duration below one minute in the \(5\times 5\)-node grid and a sent data volume of approx. 2.5 MB. The realization of the algorithm as a phase sequence facilitates its practical analysis and allows time estimates that correspond well with the measured clustering duration. In different grid sizes, we investigated the dependence of the amount of data sent on the number of nodes. As expected, an almost linear relationship was found for TCP, which is mainly used for communication between neighboring nodes in our concept. In the case of UDP, a stronger nonlinear topology dependency was shown, since it is used for network-wide broadcast messages. Beyond the initial cluster formation, however, the long-term communication overhead is dominated in particular by periodic CH announcements, whose transmission interval can be chosen depending on the use case. Finally, we practically validated our mechanisms for subsequent network joining, fault detection, node roaming, and cluster balancing, which are essential for long-term network operation. Future studies could focus on automatically deriving and optimizing 802.11s and CHaChA protocol parameters for different network sizes, device classes, and use case requirements. In this context, we intend to evaluate CHaChA in larger and more diverse network scenarios. Prospectively, employing a more powerful device platform in our testbed will also allow the use of higher 802.11 data rates and enable further fine-tuning of the timing parameters of our algorithm. As an additional goal, we plan to conduct a quantitative comparison with the related works, as discussed in Section 3.