Research Article
Channel Allocation Based K-Medoids in a Wireless Mesh Network
@INPROCEEDINGS{10.1007/978-3-031-81570-6_13, author={Thomas Djotio Ndie and Paulin Melatagia Yonta and Isma\`{\i}l Samaye and Karl Jonas}, title={Channel Allocation Based K-Medoids in a Wireless Mesh Network}, proceedings={Towards new e-Infrastructure and e-Services for Developing Countries. 15th International Conference, AFRICOMM 2023, Bobo-Dioulasso, Burkina Faso, November 23--25, 2023, Proceedings, Part I}, proceedings_a={AFRICOMM}, year={2025}, month={2}, keywords={Wireless mesh network K-medoids channel allocation network cluster}, doi={10.1007/978-3-031-81570-6_13} }- Thomas Djotio Ndie
Paulin Melatagia Yonta
Ismaël Samaye
Karl Jonas
Year: 2025
Channel Allocation Based K-Medoids in a Wireless Mesh Network
AFRICOMM
Springer
DOI: 10.1007/978-3-031-81570-6_13
Abstract
Wireless mesh networks (WMNs) are considered one of the most promising approaches to power networks using non-physical connection media that require high bandwidth and coverage. Because of its qualities in terms of bandwidth and coverage, they face quality of service problems such as throughput due in some cases to poor channel allocation. The channel allocation issue in a WMN is similar to a graph edge coloring problem which is an NP-complete problem. In order to solve this problem, various approaches have been proposed, some based on graph theory, some on conflict graph theory, and others using message exchange for synchronization on communication channel between nodes. In this paper, we present K-MEDAL, an approach to channel allocation in WMNs based on K-medoids algorithm. For our simulation, we used a testbed composed of 33 static nodes randomly arranged over an area of(1000Km^2 )in the NS-2 simulator. The K-medoids algorithm allowed us to build small network clusters to reduce the complexity of the channel allocation problem. Compared to other solutions found in the literature, the K-MEDAL approach shows out a 2 to 3 times increase in both the throughput distributed over the active links of a cluster and the aggregate throughput per cluster.
