Sensor selection on graphs via data-driven node sub-sampling in network time series
This work addresses the problem of efficient sensor management for applications such as power reduction in networks with redundant sensors, though it appears incremental as it builds on existing literature in sensor selection and graph signal processing.
The paper tackles the problem of selecting an optimal subset of sensors in a network to predict data streams across all nodes with minimal reconstruction error, motivated by applications like reducing power consumption in sensor networks with limited battery supplies. It proposes and compares various data-driven strategies for sensor selection, reporting numerical experiments on real bike sharing network data.
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.