Sensor selection on graphs via data-driven node sub-sampling in network time series

• URL: http://arxiv.org/abs/2004.11815v1
• Date: Fri, 24 Apr 2020 15:51:57 GMT
• Title: Sensor selection on graphs via data-driven node sub-sampling in network time series
• Authors: Yiye Jiang (1 and 2), J\'er\'emie Bigot (1) and Sofian Maabout (2) ((1) Institut de Math\'ematiques de Bordeaux, Universit\'e de Bordeaux, (2) Laboratoire Bordelais de Recherche en Informatique, Universit\'e de Bordeaux)
• Abstract summary: This paper is concerned by the problem of selecting an optimal sampling set of sensors over a network of time series. We propose and compare various data-driven strategies to turn off a fixed number of sensors or equivalently to select a sampling set of nodes. To illustrate the performances of our approach, we report numerical experiments on the analysis of real data from bike sharing networks in different cities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper is concerned by the problem of selecting an optimal sampling set of sensors over a network of time series for the purpose of signal recovery at non-observed sensors with a minimal reconstruction error. The problem is motivated by applications where time-dependent graph signals are collected over redundant networks. In this setting, one may wish to only use a subset of sensors to predict data streams over the whole collection of nodes in the underlying graph. A typical application is the possibility to reduce the power consumption in a network of sensors that may have limited battery supplies. We propose and compare various data-driven strategies to turn off a fixed number of sensors or equivalently to select a sampling set of nodes. We also relate our approach to the existing literature on sensor selection from multivariate data with a (possibly) underlying graph structure. Our methodology combines tools from multivariate time series analysis, graph signal processing, statistical learning in high-dimension and deep learning. To illustrate the performances of our approach, we report numerical experiments on the analysis of real data from bike sharing networks in different cities.

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