BARGAIN-MATCH: A game theoretical approach for resource allocation and task offloading in vehicular edge computing networks
Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular
networks (VNs) by deploying the cloud computing resources at the edge of the VNs …
networks (VNs) by deploying the cloud computing resources at the edge of the VNs …
Content service oriented resource allocation for space–air–ground integrated 6G networks: A three-sided cyclic matching approach
Since the existing terrestrial fifth generation (5G) network has limited coverage, it is difficult
to meet the growing demand for seamless network connection. Meanwhile, current network …
to meet the growing demand for seamless network connection. Meanwhile, current network …
Multi-agent learning-based optimal task offloading and UAV trajectory planning for AGIN-power IoT
P Qin, Y Fu, Y Xie, K Wu, X Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
UAV-based air-ground integrated computing networks (AGIN) have gained significant
traction in remote areas for the Power Internet of Things (PIoT). This paper considers an …
traction in remote areas for the Power Internet of Things (PIoT). This paper considers an …
Joint trajectory plan and resource allocation for UAV-enabled C-NOMA in air-ground integrated 6G heterogeneous network
Leveraging unmanned aerial vehicles (UAVs) for access and high altitude platform stations
(HAPSs) for data backhaul to construct the Air-Ground Integrated Network (AGIN), is a …
(HAPSs) for data backhaul to construct the Air-Ground Integrated Network (AGIN), is a …
Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication
In today's era, Internet of Things (IoT) devices generate a vast amount of data, which is
typically stored in the cloud environment and can be accessed by edge and IoT devices. The …
typically stored in the cloud environment and can be accessed by edge and IoT devices. The …
DRL connects Lyapunov in delay and stability optimization for offloading proactive sensing tasks of RSUs
W Zhao, K Shi, Z Liu, X Wu, X Zheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The integration of Roadside Units (RSUs) is vital for the development of autonomous driving
technologies. Challenges arise from sinking computing capabilities into RSUs and vehicles …
technologies. Challenges arise from sinking computing capabilities into RSUs and vehicles …
Quantum deep reinforcement learning for dynamic resource allocation in mobile edge computing-based IoT systems
This paper exploits a quantum-empowered machine learning algorithm to enhance
computation learning speed. We leverage quantum phenomena such as superposition and …
computation learning speed. We leverage quantum phenomena such as superposition and …
Optimal resource allocation for AGIN 6G: a learning-based three-sided matching approach
As ubiquitous interconnection becomes a reality for human beings, addressing the
challenge of seamless coverage in the near future 6G network, particularly for remote area …
challenge of seamless coverage in the near future 6G network, particularly for remote area …
Competition-awareness partial task offloading and uav deployment for multitier parallel computational internet of vehicles
P Qin, Y Fu, R Ding, H He - IEEE Systems Journal, 2024 - ieeexplore.ieee.org
Vehicular edge computing is poised to meet the requirements of emerging applications in
Internet of Vehicles (IoV) by offloading computation tasks from resource-limited vehicles to …
Internet of Vehicles (IoV) by offloading computation tasks from resource-limited vehicles to …
A proactive stable scheme for vehicular collaborative edge computing
J Liu, N Liu, L Liu, S Li, H Zhu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Due to the restricted computing resources and high upgrading costs, onboard processors
alone cannot meet the quality of service (QoS) requirements of the emerging and constantly …
alone cannot meet the quality of service (QoS) requirements of the emerging and constantly …