Dynamic Sensor Scheduling Based on Node Partitioning of Graphs

TL;DR

This paper proposes a dynamic sensor scheduling method for sensor networks that adaptively estimate the signal subspace from historical data and sequentially update the prior for the graph node partitioning method, achieving lower average mean squared errors compared to alternative methods.

Abstract

This paper proposes a dynamic sensor scheduling method for sensor networks. In sensor network applications, we often need multiple equally-informative node subsets that are activated sequentially to make a sensor network robust against concentrated battery consumption and sensor failures. In addition, quality of these subsets changes dynamically and thus we must adapt those changes. To find those node subsets, we propose a graph node partitioning method based on sampling theory for graph signals. We aim to minimize the average reconstruction error for signals obtained at all node subsets, in contrast to conventional single subset selection. The graph node partitioning problem is formulated as a difference-of-convex (DC) optimization based on a subspace prior of graph signals, and is solved by the proximal DC algorithm. It guarantees convergence to a critical point. To accommodate the online scenario where the signal subspace and optimal partitioning may change over time, we adaptively estimate the signal subspace from historical data and sequentially update the prior for our partitioning method. Numerical experiments on synthetic and real-world sensor network data demonstrate that the proposed method achieves lower average mean squared errors compared to alternative methods.

Figures (6)

• Figure 1: Overview of the proposed method. For simplicity, the selection with two subsets is illustrated.
• Figure 2: Visualization of the absolute errors between original and reconstructed PWS graph signals. We show the noisy case with reconstruction based on the subspace prior. From top to bottom: The proposed method, SRel, and SFrob. The leftmost column is the original signals (same for all methods). The other columns show the reconstructed signals from sampled subsets. The selected nodes are highlighted by red circles.
• Figure 3: MSE of reconstructed signals [dB]. The average MSE of each method is plotted as a horizontal dashed line. SS means signal subspace. Vertical dashed lines indicate the time instances when the proposed method updates the partitioning.
• Figure 4: Visualization of a graph signal constructed from global sea surface temperature.
• Figure 5: MSE of reconstructed signals [dB]. The average MSE of each method is plotted as a horizontal dashed line.

Theorems & Definitions (1)

• Definition 1: Node domain sampling SAMPsurvey