Dynamic Sensor Scheduling Based on Node Partitioning of Graphs

TL;DR

This paper introduces a dynamic sensor scheduling approach using graph node partitioning to select multiple informative node subsets, improving robustness and adapting to changing signal qualities in sensor networks.

Contribution

It presents a novel graph partitioning method based on sampling theory and DC optimization, with an adaptive online update mechanism for dynamic sensor network management.

Findings

Lower mean squared errors compared to existing methods.

Effective adaptation to changing signal subspaces over time.

Demonstrated robustness in synthetic and real-world sensor data.

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.