Probabilistic RF-Assisted Camera Wake-Up through Self-Supervised Gaussian Process Regression

TL;DR

This paper introduces a probabilistic, energy-efficient camera wake-up system that uses self-supervised Gaussian Process Regression to predict target detectability from radio signals, significantly reducing energy use while maintaining detection accuracy.

Contribution

It presents a novel self-supervised Gaussian Process Regression approach combined with Bayesian estimation for energy-aware camera control in wireless sensor networks.

Findings

High detection accuracy achieved with low energy consumption.

Automatic training minimizes human intervention.

Numerical and experimental results validate effectiveness.

Abstract

Research on wireless sensors represents a continuously evolving technological domain thanks to their high flexibility and scalability, fast and economical deployment, pervasiveness in industrial, civil and domestic contexts. However, the maintenance costs and the sensors reliability are strongly affected by the battery lifetime, which may limit their use. In this paper we consider a wireless smart camera, equipped with a low-energy radio receiver, and used to visually detect a moving radio-emitting target. To preserve the camera lifetime without sacrificing the detection capabilities, we design a probabilistic energy-aware controller to switch on/off the camera. The radio signal strength is used to predict the target detectability, via self-supervised Gaussian Process Regression combined with Recursive Bayesian Estimation. The automatic training process minimizes the human intervention, while the controller guarantees high detection accuracy and low energy consumption, as numerical and experimental results show.