Dynamic Sensor Placement Based on Sampling Theory for Graph Signals

TL;DR

This paper introduces a dynamic sensor placement method on networks using sampling theory, enabling mobile sensors to adapt positions for improved signal recovery over static placements.

Contribution

It proposes a novel approach for moving sensors within networks based on graph signal sampling theory, enhancing signal reconstruction accuracy.

Findings

Outperforms static sensor placement in experiments

Effective on both synthetic and real data

Improves signal reconstruction accuracy

Abstract

In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select K sensor positions from N candidates where K < N. Most existing methods assume that sensor positions are static, i.e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time. Moreover, underlying measurement conditions could also be changed, which are difficult to cover with statically placed sensors. We tackle the problem by allowing the sensors to change their positions in their neighbors on the network. We dynamically determine the sensor positions based on graph signal sampling theory such that the non-observed signals on the network can be best recovered from the observations. For signal recovery, the dictionary is learned from a pool of observed signals. It is also used for the sensor position selection. In experiments, we validate the effectiveness of the proposed method via the mean squared error of the reconstructed signals. The proposed dynamic sensor placement outperforms the existing static ones for both synthetic and real data.