Optimized deep learning object recognition for drones using embedded gpu

PA Rad, D Hofmann, SAP Mendez… - 2021 26th IEEE …, 2021 - ieeexplore.ieee.org
Nowadays, drones can be seen in various applications in industry like surveillance and
transportation. Industrial drones leverage fully-fledged computer vision techniques, such as …

Task offloading and trajectory control for UAV-assisted mobile edge computing using deep reinforcement learning

L Zhang, ZY Zhang, L Min, C Tang, HY Zhang… - IEEE …, 2021 - ieeexplore.ieee.org
Mobile Edge Computing (MEC) has been widely employed to support various Internet of
Things (IoT) and mobile applications. By leveraging the advantages of easily deployed and …

Edge computing for visual navigation and mapping in a UAV network

MA Messous, H Hellwagner… - ICC 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
This research work presents conceptual considerations and quantitative evaluations into
how integrating computation offloading to edge computing servers would offer a paradigm …

DRL-based energy-efficient trajectory planning, computation offloading, and charging scheduling in UAV-MEC network

Q Wei, Z Zhou, X Chen - 2022 IEEE/CIC International …, 2022 - ieeexplore.ieee.org
Recently, unmanned aerial vehicle (UAV) is intro-duced into mobile edge computing (MEC)
system to help process large-scale task data generated by distributed user devices (UDs). In …

Task offloading of deep learning services for autonomous driving in mobile edge computing

J Jang, K Tulkinbekov, DH Kim - Electronics, 2023 - mdpi.com
As the utilization of complex and heavy applications increases in autonomous driving,
research on using mobile edge computing and task offloading for autonomous driving is …

[HTML][HTML] Decomposition-based learning in drone-assisted wireless-powered mobile edge computing networks

X Zhou, L Huang, T Ye, W Sun - Digital Communications and Networks, 2023 - Elsevier
This paper investigates the multi-Unmanned Aerial Vehicle (UAV)-assisted wireless-
powered Mobile Edge Computing (MEC) system, where UAVs provide computation and …

Deep learning-based energy optimization for edge device in UAV-aided communications

C Chen, J Xiang, Z Ye, W Yan, S Wang, Z Wang… - Drones, 2022 - mdpi.com
Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G)
communication will consume more energy. The unmanned aerial vehicle (UAV)-aided …

Task offloading in UAV-aided edge computing: Bit allocation and trajectory optimization

J Xiong, H Guo, J Liu - IEEE Communications Letters, 2019 - ieeexplore.ieee.org
The deployment of unmanned aerial vehicles (UAVs) in wireless communication systems
promises to provide services for devices with limited or without infrastructure coverage. With …

Embedded deep learning

B Moons, D Bankman, M Verhelst - Embedded Deep Learning, 2019 - Springer
Although state of the art in many typical machine learning tasks, deep learning algorithms
are very costly in terms of energy consumption, due to their large amount of required …

Deep reinforcement learning based energy efficient multi-UAV data collection for IoT networks

SS Khodaparast, X Lu, P Wang… - IEEE Open Journal of …, 2021 - ieeexplore.ieee.org
Unmanned aerial vehicles (UAVs) are regarded as an emerging technology, which can be
effectively utilized to perform the data collection tasks in the Internet of Things (IoT) networks …