1 code implementation • 5 Dec 2021 • Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability.
1 code implementation • 8 Oct 2020 • Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value.
1 code implementation • 10 May 2019 • Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings.