Cooperative UAV resource allocation and task offloading in hierarchical aerial computing systems: A MAPPO-based approach

H Kang, X Chang, J Mišić, VB Mišić… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
This article investigates a hierarchical aerial computing system, where both high-altitude
platforms (HAPs) and unmanned aerial vehicles (UAVs) provision computation services for …

Dynamic and intelligent edge server placement based on deep reinforcement learning in mobile edge computing

X Jiang, P Hou, H Zhu, B Li, Z Wang, H Ding - Ad Hoc Networks, 2023 - Elsevier
In the era of 5G and beyond, Mobile Edge Computing (MEC) has emerged as a technology
that seamlessly integrates wireless networks and the Internet, enabling low-latency and high …

Optimal priority rule-enhanced deep reinforcement learning for charging scheduling in an electric vehicle battery swapping station

J Jin, S Mao, Y Xu - IEEE Transactions on Smart Grid, 2023 - ieeexplore.ieee.org
For a battery swapping station (BSS) with solar generation, charging bays, and an inventory
of batteries, we study the charging scheduling problem under random EV arrivals …

Evolutionary multitask optimization in real-world applications: A survey

Y Wu, H Ding, B Xiang, J Sheng, W Ma… - Journal of Artificial …, 2023 - ojs.istp-press.com
Because of its strong ability to solve problems, evolutionary multitask optimization (EMTO)
algorithms have been widely studied recently. Evolutionary algorithms have the advantage …

Multi-Hop Task Offloading and Relay Selection for IoT Devices in Mobile Edge Computing

T Li, Y Liu, T Ouyang, H Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
To bridge the gap of conventional single-hop task offloading schemes in infrastructure-free
scenarios, multi-hop task offloading schemes for IoT devices in Mobile Edge Computing …

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

M Khani, S Jamali, MK Sohrabi… - International Journal …, 2024 - Wiley Online Library
The emergence of 5G networks has increased the demand for network resources, making
efficient resource management crucial. Slice admission control (SAC) is a process that …

Device-Specific QoE Enhancement Through Joint Communication and Computation Resource Scheduling in Edge-Assisted IoT Systems

Q Wang, Q Wang, H Zhao, H Zhang… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
With rapid adoption in vertical industries and further assistance of edge computing, Internet
of Things (IoT) applications are experiencing phenomenal growth. However, the …

Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges

M Ghasemi, AH Mousavi, D Ebrahimi - arXiv preprint arXiv:2411.18892, 2024 - arxiv.org
Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence
(AI), enabling agents to learn optimal behaviors through interactions with their environments …

An online transfer learning based multifactorial evolutionary algorithm for solving the clustered Steiner tree problem

NB Long, HB Ban, HTT Binh - Knowledge-Based Systems, 2024 - Elsevier
The rapid increase in the number and computing power of devices interacting through the
network over the past few years has led to a growing trend of decentralized clustering …

Meta reinforcement learning-based computation offloading in RIS-aided MEC-enabled cell-free ran

Y Lu, Y Jiang, L Zhang, M Bennis… - ICC 2023-IEEE …, 2023 - ieeexplore.ieee.org
In this paper, the computation offloading problem in reconfigurable intelligent surface (RIS)-
aided mobile edge computing (MEC)-enabled cell-free radio access network (CF-RAN) is …