A survey of UAV-based data collection: Challenges, solutions and future perspectives

K Messaoudi, OS Oubbati, A Rachedi, A Lakas… - Journal of Network and …, 2023 - Elsevier
Abstract Internet of Things (IoT) generates unlimited data, which should be collected and
forwarded toward a central controller (CC) for further processing and decision-making …

A Q-Learning-based distributed routing protocol for frequency-switchable magnetic induction-based wireless underground sensor networks

G Liu - Future Generation Computer Systems, 2023 - Elsevier
Abstract Magnetic Induction (MI) based Wireless underground sensor networks (WUSNs)
consist of magnetic-antenna sensors that are buried in and communicate through soil …

Deep reinforcement learning for aerial data collection in hybrid-powered noma-iot networks

Z Zhang, C Xu, Z Li, X Zhao… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
With the help of unmanned aerial vehicle (UAV), remote terminals that out of wireless
coverage can be connected to the Internet of Things (IoT) networks. Currently, the IoT relies …

Deep Reinforcement Learning for Internet of Drones Networks: Issues and Research Directions

N Aboueleneen, A Alwarafy… - IEEE Open Journal of the …, 2023 - ieeexplore.ieee.org
Internet of Drones (IoD) is one of the promising technologies to enhance the performance of
wireless networks. Deploying IoD to assist wireless networks, however, needs to address …

A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities

N Parvaresh, B Kantarci - IEEE Open Journal of the …, 2023 - ieeexplore.ieee.org
Uncrewed aerial vehicle-mounted base stations (UAV-BSs), also know as drone base
stations, are considered to have promising potential to tackle the limitations of ground base …

Optimal pilot and data power allocation for joint communication-radar air-to-ground networks

JM Park, J Cho, S Noh, H Yu - IEEE Access, 2022 - ieeexplore.ieee.org
Integration of communication and radar functions with a single waveform has been actively
investigated in various wireless communication applications, including unmanned aerial …

Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach

N Aboueleneen, A Alwarafy… - … and Mobile Computing …, 2023 - ieeexplore.ieee.org
Internet of Drones (IoD)-aided wireless networks are proving their efficiency in various
commercial and military applications, such as object recognition, surveillance, and data …

Complex Network Evolution Model Based on Turing Pattern Dynamics

D Li, W Song, J Liu - IEEE Transactions on Pattern Analysis …, 2022 - ieeexplore.ieee.org
Complex network models are helpful to explain the evolution rules of network structures, and
also are the foundations of understanding and controlling complex networks. The existing …

Reinforcement Learning for Joint Detection & Mapping using Dynamic UAV Networks

A Guerra, F Guidi, D Dardari… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Dynamic radar networks, usually composed of flying UAVs, have recently attracted great
interest for time-critical applications, such as search-and-rescue operations, involving …

Tool Path Optimization for Complex Cavity Milling Based on Reinforcement Learning Approach

Y Wan, W Xu, TY Zuo - IEEE Access, 2023 - ieeexplore.ieee.org
In the machining of parts, tool paths for complex cavity milling often have different generation
options, as opposed to simple machining features. The different tool path generation options …