Path planning of multi-UAVs based on deep Q-network for energy-efficient data collection in UAVs-assisted IoT
X Zhu, L Wang, Y Li, S Song, S Ma, F Yang… - Vehicular …, 2022 - Elsevier
Abstract In recent years, Unmanned Aerial Vehicles (UAVs) can effectively alleviate the
problems of unstable links and low transmission efficiency, which have been applied for …
problems of unstable links and low transmission efficiency, which have been applied for …
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 …
effectively utilized to perform the data collection tasks in the Internet of Things (IoT) networks …
[HTML][HTML] The uav trajectory optimization for data collection from time-constrained iot devices: A hierarchical deep q-network approach
Recently, using unmanned aerial vehicles (UAVs) to collect information from distributed
sensors has become one of the hotspots in the Internet of Things (IoT) research. However …
sensors has become one of the hotspots in the Internet of Things (IoT) research. However …
Learning-based UAV path planning for data collection with integrated collision avoidance
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks,
and determining collision-free trajectory in multi-UAV noncooperative scenarios while …
and determining collision-free trajectory in multi-UAV noncooperative scenarios while …
Green mobility management in UAV-assisted IoT based on dueling DQN
In most cases, the batteries of sensor nodes in the Internet of Things (IoT) are usually
constrained by size and weight, and are difficult to recharge or replace. In traditional …
constrained by size and weight, and are difficult to recharge or replace. In traditional …
Energy and service-priority aware trajectory design for UAV-BSs using double Q-learning
Next generation mobile networks have proposed the integration of Unmanned Aerial
Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite the …
Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite the …
AoI-energy-aware UAV-assisted data collection for IoT networks: A deep reinforcement learning method
Thanks to the inherent characteristics of flexible mobility and autonomous operation,
unmanned aerial vehicles (UAVs) will inevitably be integrated into 5G/B5G cellular networks …
unmanned aerial vehicles (UAVs) will inevitably be integrated into 5G/B5G cellular networks …
Efficient data collection in large-scale UAV-aided wireless sensor networks
J Chen, F Yan, S Mao, F Shen, W Xia… - 2019 11th …, 2019 - ieeexplore.ieee.org
Dispatching unmanned aerial vehicles (UAVs) to collect sensor data from distributed sensor
networks is expected to significantly improve the data collection efficiency of traditional …
networks is expected to significantly improve the data collection efficiency of traditional …
EDGO: UAV-based effective data gathering scheme for wireless sensor networks with obstacles
One of the most important requirements for effective UAV–WSN operations is to perform data
collection in timely and safe manner. Identifying an effective path in an environment with …
collection in timely and safe manner. Identifying an effective path in an environment with …
Energy‐Efficient UAV Trajectory Design with Information Freshness Constraint via Deep Reinforcement Learning
X Li, J Li, D Liu - Mobile Information Systems, 2021 - Wiley Online Library
Unmanned aerial vehicle (UAV) technique with flexible deployment has enabled the
development of Internet of Things (IoT) applications. However, it is difficult to guarantee the …
development of Internet of Things (IoT) applications. However, it is difficult to guarantee the …