Abstract
This paper presents a survey on the adoption of Reinforcement Learning (RL) approaches for power management in Wireless Sensor Networks (WSNs). The survey has been carried out after a review expressly focused on the most relevant and the most recent contributions for the topic. Moreover, the analysis encompassed proposals at every methodological level, from dynamic power management to adaptive autonomous middleware, from self learning scheduling to energy efficient routing protocols.
Carlo Brandolese
Assistant Professor
Carlo Brandolese is a researcher at the Department of Electronics and Information of the Politecnico di Milano and a consultant researcher at Cefriel Research Centre. His research interests are focused on design and low-power methodologies for embedded systems.
William Fornaciari
Associate Professor
William Fornaciari has published six books and over 200 papers, earning five best paper awards, an IEEE certification, and three international patents on low power design. Since 1997, he has participated in 18 EU-funded projects. His research focuses on multi/many-core architectures, NoC, low power design, and more.