Reinforcement Learning-Based Transmission Policies for Energy Harvesting Powered Sensors

We consider a sampled-data control system where a wireless sensor transmits its measurements to a controller over a communication channel. We assume that the sensor has a harvesting element to extract energy from the environment and store it in a rechargeable battery for future use. The harvested energy is modelled as a first-order Markovian stochastic process conditioned on a scenario parameter describing the harvesting environment. The overall model can then be represented as a Markov decision process, and a suitable transmission policy providing both good control performance and efficient energy consumption is designed using reinforcement learning approaches. Finally, supervisory control is used to switch between trained transmission policies depending on the current scenario. Also, we provide a tool for estimating an unknown scenario parameter based on measurements of harvested energy, as well as detecting the time instants of scenario changes. The above problem is solved based on Bayesian filtering and smoothing.