Browsing Institutt for informasjonssikkerhet og kommunikasjonsteknologi by Author "Bach, Kerstin"

Now showing items 1-7 of 7

    • Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning 

      Murad, Abdulmajid Abdullah Yahya; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2019)
      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. We show that the state-of-the-art ...

    • Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2020)
      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. This ...

    • IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning 

      Murad, Abdulmajid Abdullah Yahya; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2019)
      We describe IoT Sensor Gym, a framework to train the behavior of constrained IoT devices using deep reinforcement learning. We focus on the main architectural choices to align problems from the IoT domain with cutting-edge ...

    • Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy 

      Krämer, Frank Alexander; Asad, Hafiz Areeb; Bach, Kerstin; Renner, Christian (Peer reviewed; Journal article, 2023)
      We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, ...

    • Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Peer reviewed; Journal article, 2021)
      Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty ...

    • Towards containerized, reuse-oriented AI deployment platforms for cognitive IoT applications 

      Veiga, Tiago Santos; Asad, Hafiz Areeb; Kræmer, Frank Alexander; Bach, Kerstin (Peer reviewed; Journal article, 2022)
      IoT applications with their resource-constrained sensor devices can benefit from adjusting their operations to the phenomena they sense and the environments they operate in, leading to the paradigm of self-adaptive, ...

    • Uncertainty-Aware Autonomous Sensing with Deep Reinforcement Learnings 

      Murad, Abdulmajid (Doctoral theses at NTNU;2023:64, Doctoral thesis, 2023)
      The goal of many Internet of Things (IoT) sensing applications, such as environmental monitoring, is to support decision-making by providing valuable information about various phenomena. One approach to achieve this goal ...