A distributed self-adaptive nonparametric change-detection test for sensor/actuator networks
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
| Hi-index | 0.00 |
This paper studies decentralized quickest detection schemes that can be deployed in a sensing environment where data streams are simultaneously collected from multiple channels located distributively to jointly support the detection. Existing decentralized detection approaches are largely parametric that require the knowledge of pre-change and post-change distributions. In this paper, we first present an effective nonparametric detection procedure based on Q-Q distance measure. We then describe two implementations schemes, binary quickest detection and local decision fusion by majority voting, that realize decentralized nonparametric detection. Experimental results show that the proposed method has a comparable performance to the parametric CUSUM test in binary detection. Its decision fusion-based implementation also outperforms the other three popular fusion rules under the parametric framework.