Reinforcement Learning Strategies for Energy Management in Low Power IoT
Résumé
Energy management in low power IoT is a difficult problem. Modeling the consumption of a sensor node is complicated , they operate in a stochastic environment. They harvest energy in their environment but energy sources present time-varying behavior. It becomes hazardous to predict in advance the energy behavior of our system. In this paper we propose a new approach using both neural networks to estimate the harvesting energy and reinforcement learning algorithms to find the operating parameters to maximize the node's performance while preserving its energy resources.
Origine : Fichiers produits par l'(les) auteur(s)
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