Work

Distributed Change Detection in Streaming Graph Signals

Year: 2019

Type: conference-paper

Abstract: Detecting abrupt changes in streaming graph signals is relevant in a variety of applications ranging from energy and water supplies, to environmental monitoring. In this paper, we address this problem when anomalies activate localized groups of nodes in a network. We introduce an online change-point detection algorithm, which is fully distributed across nodes to monitor large-scale dynamic networks. We analyze the detection statistics for controlling the probability of a global type 1 error. Finally we illustrate the detection and localization performance with simulated data.

Source: IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

DOI: 10.1109/camsap45676.2019.9022658

Authors: A. Ferrari, C. Richard, L. Verduci

Institutions: Centre National de la Recherche Scientifique, Université Côte d'Azur, Observatoire de la Côte d’Azur

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Awards: Idex UCA JEDI, Distributed adaptation and learning over graph signals