High demands for industrial networks lead to increasingly large sensor networks. However, the complexity of networks and demands for accurate data require better stability and communication quality. Conventional clustering methods for ad-hoc networks are based on topology and connectivity, leading to unstable clustering results and low communication quality. In this paper, we focus on two situations: time-evolving networks, and multi-channel ad-hoc networks. We model ad-hoc networks as graphs and introduce community detection methods to both situations. Particularly, in time-evolving networks, our method utilizes the results of community detection to ensure stability. By using similarity or human-in-the-loop measures, we construct a new weighted graph for final clustering. In multi-channel networks, we perform allocations from the results of multiplex community detection. Experiments on real-world datasets show that our method outperforms baselines in both stability and quality.
翻译:对工业网络的高度需求导致日益庞大的传感器网络。然而,网络的复杂性和对准确数据的需求要求需要更好的稳定性和通信质量。特设网络的常规集群方法基于地形学和连通性,导致集群结果不稳定和通信质量低。在本文件中,我们侧重于两种情况:时间变化的网络和多通道特设网络。我们将特设网络建成图表,并为两种情况引入社区检测方法。特别是在时间变化的网络中,我们的方法利用社区检测结果来确保稳定性。我们通过使用类似或人到场的措施,为最终集群设计了新的加权图表。在多渠道网络中,我们从多路社区检测结果中进行分配。现实世界数据集实验显示,我们的方法在稳定性和质量两方面都超过了基线。