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

Research on wireless sensors represents a continuously evolving technological domain thanks to their high potentialities: 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 and exploitation. In this paper we consider a scenario in which a wireless smart camera, equipped with a low-energy radio receiver, is used to visually detect a moving radio-emitting target. To preserve the camera lifetime, we design a probabilistic energy-aware controller that regulates the camera state. The radio signal strength at the receiver side is used to predict the target detectability, via self-supervised Gaussian Process Regression combined with Recursive Bayesian Estimation. Both numerical and experimental results validate the proposed approach in terms of target detection accuracy and energy consumption.