Research line leader: Boudewijn Haverkort (UT)

In order for systems to work energy-autonomously, it is important to have the right balance between potential energy use by the supported applications and the energy harvesting capabilities. On top of that, to overcome periods of heavy energy-use and/or low energy harvesting, locally stored energy will be needed. To be able to estimate the required energy storage capacity to assure continuous operation, a model-based approach will be taken to come up with good dimensioning rules. This is especially challenging as the required models will be so-called stochastic hybrid models that will have to encompass continuous state components (e.g., the amount of stored energy, or the harvesting rate given the environmental circumstances, the radio and the sensing and actuation devices), linear and non-linear environmental dynamics affecting both the continuous and the discrete state components, as well as stochastics, describing variations (disturbances) in the workload and harvesting potential. We build upon earlier work on the general theory of hybrid models; however, we have to extend it, and tailor it to be applicable in an energy-related context, as has been done in for wearable devices. R6 challenges are:

  • Development of model-driven, adaptive power management, using application scenario prediction, to guarantee continuous energy availability.
  • Develop stochastic models that can predict under what circumstances the node does not have enough energy to perform its tasks.