Digraph Spectral Clustering with Applications in Distributed Sensor Validation

Yue-Jin Du,Li-Dong Zhai,Hui Lu

doi:10.1155/2014/536901

Abstract

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%.

Highlights

Sensor networks as an enabling technique have been deployed in many scenarios that human beings find it hard to reach, for example, in the wild area, ocean, battle fields, and so forth
As the change rate on received signal strength (RSS) increases from 0.1 to 0.5, the detection accuracy of both our digraph spectral clustering method and the traditional spectral clustering method increases, which is because larger changes on RSS leads to higher dissimilarity between “bad nodes” and “good” nodes
In this paper we propose a generalized digraph spectral clustering algorithm for validating sensor status in distributed sensor networks

Summary

Introduction

Sensor networks as an enabling technique have been deployed in many scenarios that human beings find it hard to reach, for example, in the wild area, ocean, battle fields, and so forth. These sensor networks serve an important purpose to collect information to help people understand and monitor the unreachable regions. A periodical validation of sensor status is needed. In many cases, it is not possible to reach the sensor network to find the problematic senor nodes. A self-validation method becomes more practical in reality, where sensor nodes validate their goodness by monitoring the signals received from their neighbors

Methods

Results

Conclusion