scholarly journals Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

2022 ◽  
Vol 42 (1) ◽  
pp. 271-287
Author(s):  
Xin Liu ◽  
Siya Xu ◽  
Chao Yang ◽  
Zhili Wang ◽  
Hao Zhang ◽  
...  
2021 ◽  
Vol 18 (7) ◽  
pp. 58-68
Author(s):  
Xin Liu ◽  
Can Sun ◽  
Mu Zhou ◽  
Bin Lin ◽  
Yuto Lim

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6058
Author(s):  
Shuo Xiao ◽  
Shengzhi Wang ◽  
Jiayu Zhuang ◽  
Tianyu Wang ◽  
Jiajia Liu

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.


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