Finding Link Topology of Large Scale Networks from Anchored Hop Count Reports
Finding Link Topology of Large Scale Networks from Anchored Hop Count Reports
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DOI：
10.1109/glocom.2017.8253952
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Taha Bouchoucha;Chen-Nee Chuah;Z. Ding
中科院分区：
文献类型：
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作者：
Taha Bouchoucha;Chen-Nee Chuah;Z. Ding
从其连接性的部分知识中学习网络拓扑结构是通信网络和社交媒体网络实际场景中的一个重要目标。将此类网络表示为连通图，探索和恢复网络节点之间的连接信息有助于可视化网络拓扑结构并提高网络效用。这项工作考虑使用从一部分锚节点/源节点获得的简单跳距测量来重建未知连接拓扑的大规模网络的节点连接关系。我们提出的方法包括两个步骤。我们首先开发一种基于树的搜索策略，根据跳数测量确定未知网络边的约束条件。然后，我们基于测量矩阵的主成分分析（PCA）推导出节点之间的逻辑距离，并对每个未知边提出一个二元假设检验。所提出的算法可以有效提高连接性检测的准确性以及数据路由应用中的成功投递率。
Learning network topology from partial knowledge of its connectivity is an important objective in practical scenarios of communication networks and social-media networks. Representing such networks as connected graphs, exploring and recovering connectivity information between network nodes can help visualize the network topology and improve network utility. This work considers the use of simple hop distance measurement obtained from a fraction of anchor/source nodes to reconstruct the node connectivity relationship for large scale networks of unknown connection topology. Our proposed approach consists of two steps. We first develop a tree-based search strategy to determine constraints on unknown network edges based on the hop count measurements. We then derive the logical distance between nodes based on principal component analysis (PCA) of the measurement matrix and propose a binary hypothesis test for each unknown edge. The proposed algorithm can effectively improve both the accuracy of connectivity detection and the successful delivery rate in data routing applications.