# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 3 State of the Art and Related Works  
In the area of WMN management, various research works exist that propose a decentralized organization of the network by partitioning it into logical subnetworks (clusters), but do not address the aspect of channel selection for the resulting groups of nodes. The motivation is primarily to distribute management tasks to increase their scalability. With ANMP (Ad Hoc Network Management Protocol) [21] and Guerrilla [22], graph-theoretical methods for hierarchical clustering were described with the goal of reducing the traffic generated by status monitoring of the nodes based on the Simple Network Management Protocol (SNMP). Another solution called ABCP (Access-Based Clustering Protocol) [23] requires strong adaptations at the WLAN link layer. Mesh-Mon [24] represents a broadcast-based clustering protocol that is also used to distribute an SNMP monitoring solution. MeshMan [25] defines another protocol based on ICMP that performs hierarchical addressing and clustering of nodes. None of the aforementioned work takes the WLAN mesh specification 802.11s into account. ANMP, Guerrilla, and ABCP have also only been studied simulatively. In the research field of channel selection for WLAN mesh environments, greater methodological commonalities exist in the form of group-based approaches. The surveys [12; 13; 14] provide an overview of the large number of contributions in this area. While no approaches considering 802.11s are discussed, the survey [12] presents a helpful classification of channel selection strategies for WMN with multiple network interfaces per device. This is shown in Figure 2, and the classification of our own work CHaChA is highlighted. According to [12], the following criteria are distinguished:  
**Decision point:** A basic feature is the decision point for channel selection. A distinction is made between centralized and distributed approaches, depending on whether the responsibility lies with a dedicated entity (usually with global network knowledge) or with many or all nodes. Distributed approaches usually have higher scalability and resilience. In addition, they are independent of global knowledge and can be implemented using only local information, such as connections to neighboring nodes or forwarding rules to selected endpoints [13]. **Dynamicity:** A second criterion is dynamicity, i.e., the ability to respond to changes in the network. This ranges from purely static and quasi-static methods, which perform a configuration only once when the network is put into operation or regularly at large intervals, to semi-dynamic methods, which can make new decisions within shorter periods or in response to specific events, to fully dynamic strategies, which also allow dealing with mobile nodes. **Granularity:** The granularity of the method defines whether different channels are chosen for individual connections (links), end-to-end transmissions (flows) or groups of nodes. The latter category also includes clustering approaches, which represent a trade-off between the performance gain achievable through fine-granular channel separation and the message overhead caused by frequent reconfiguration. **Methodology:** Existing methods particularly differ in the way they solve the channel selection problem. Centralized approaches are often based on graph theory (e.g., graph coloring), mathematical optimization techniques (e.g., linear programming), or artificial intelligence (e.g., evolutionary algorithms). They usually require global knowledge about the network and often involve high computational overhead. On the other hand, many decentralized approaches work peer-oriented and mainly use local connection information of the nodes. **Layering principle:** Another feature for distinguishing channel selection strategies is whether they are located only at a single layer of the network protocol stack (single-layer approaches) or operate across multiple layers (cross-layer approaches). The latter often offer higher optimization potential by using information from different layers.