Browsing NTNU Open by Author "Taylor, Gavin"
Now showing items 1-7 of 7
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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 ... -
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 ... -
ML-based profile analysis of CUDA programs' compiler flag impact
Bækken, August Landgraff (Master thesis, 2018)With the recent successes and interest in machine learning, this project aims to investigate whether machine learning methods can be used to improve compiler optimization selection. Compiler optimization is hard because ... -
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 ... -
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 ...