基于社区检测的Ad-hoc网络数据驱动分簇
Data-driven Clustering in Ad-hoc Networks based on Community Detection
摘要：
对工业网络的高需求导致越来越大的传感器网络。然而，网络的复杂性和对准确数据的需求要求更好的稳定性和通信质量。传统的ad-hoc网络分簇方法是基于拓扑和连通性的，导致不稳定的分簇结果和低通信质量。在本文中，我们专注于两种情况：时间演化网络，和多信道ad-hoc网络。我们的模型ad-hoc网络的图形和社区检测方法引入这两种情况。特别是，在时间演化网络中，我们的方法利用社区检测的结果来确保稳定性。通过使用相似性或人在回路中的措施，我们构建了一个新的加权图的最终聚类。在多信道网络中，我们从多路复用社区检测的结果进行分配。在真实数据集上的实验表明，该方法在稳定性和质量上都优于基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
