Digraph Spectral Clustering with Applications in Distributed Sensor Validation

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%.