23 citations · 66 across the 4 of their papers we have counts for
4 papers
EStacker: Explaining Battery-Less IoT System Performance with Energy Stacks
Lukas Liedtke, Per Gunnar Kjeldsberg, Frank Alexander Kraemer +1
The number of Internet of Things (IoT) devices is increasing exponentially, and it is environmentally and economically unsustainable to power all these devices with batteries. The…
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach +1
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. Despite their success, most of the current data-driven solutions lack proper quant…
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach +1
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 ba…
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach +1
Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained set…