期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2014
卷号：2014
DOI：10.1155/2014/536901
出版社：Hindawi Publishing Corporation
摘要：In various sensor networks, the performances of sensors vary significantly over time, due to the changes of surrounding environment, device hardware, and so forth. Hence, monitoring the status is essential in sensor network maintenance. Spectral clustering has been employed as an enabling technique to solve this problem. However, the traditional spectral clustering is developed for undirected graph, and the naive generalization for directed graph by symmetrization of the adjacency matrix will lead to loss of network information, and thus cannot efficiently detect bad sensor nodes while applying it for sensor validation. In this paper, we develop a generalized digraph spectral clustering method. Instead of simply symmetrizing the adjacency matrix, our method takes into consideration the network circulation while clustering the sensors. The extensive simulation results demonstrate that our method outperforms the traditional spectral clustering method by increasing the bad detection ratio from 19% to 41%.
