Blar i Institutt for informasjonssikkerhet og kommunikasjonsteknologi på forfatter "Bach, Kerstin"
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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 ... -
Container-Based IoT Architectures: Use Case for Visual Person Counting
Santos Veiga, Tiago; Asad, Hafiz Areeb; Kraemer, Frank Alexander; Bach, Kerstin (Chapter, 2023)This paper studies the deployment process for a use case of visual person counting from cameras located in outdoor areas and shows how a containerized solution fulfills the particular requirements for the use case, ... -
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 learning
Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Journal article; Peer reviewed, 2024)Constructing an accurate representation model of phenomena with fewer measurements is a fundamental challenge in the Internet of Things. Leveraging sparse sensing policies to select the most informative measurements is a ... -
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 ...