Collaborative Caching Strategy for RL-Based Content Downloading Algorithm in Clustered Vehicular Networks

Bi, Xiaodan; Zhao, Lian

doi:10.1109/jiot.2023.3235661

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…MDP is generally defined in a tuple with four elements:

. In this tuple,

is state space,

is action space,

is the transition probabilities, and

is the reward function [ 30 ]. In the scenario of this work, the state space and the action space for offloading decisions can be characterized as follows.…”

Section: Collaborative Task Processing Algorithmmentioning

confidence: 99%

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Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks

Bi,

Zhao

2024

Sensors

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With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing (MEC) server, the proposed architecture aims to release the burden on roadside units (RSUs) servers. Specifically, we propose a two-layer edge intelligence scheme to allocate network computing resources. The first layer intelligently offloads and allocates tasks generated by wireless devices in the vehicular system, and the second layer utilizes the partially observable stochastic game (POSG), solved by duelling deep Q-learning, to allocate the computing resources of each processing node (PN) to different tasks. Meanwhile, we propose a weighted position optimization algorithm for the UAV movement in the system to facilitate task offloading and task processing. Simulation results demonstrate the improved performance by applying the proposed scheme.

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“…MDP is generally defined in a tuple with four elements:

. In this tuple,

is state space,

is action space,

is the transition probabilities, and

is the reward function [ 30 ]. In the scenario of this work, the state space and the action space for offloading decisions can be characterized as follows.…”

Section: Collaborative Task Processing Algorithmmentioning

confidence: 99%

“…Algorithm 1 shows the DQN algorithm’s procedure [ 30 ]. We take the newly generated task, and the computing capacity of each PN and the set of PNs can be selected as the neural network state space, and then we utilize the DQN to make the task offloading decision.…”

Section: Collaborative Task Processing Algorithmmentioning

confidence: 99%

Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks

Bi,

Zhao

2024

Sensors

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“…The work in 23 also utilizes the idea of clustering. The goal is to select the cluster head vehicle in each cluster and allocate the cached content to it.…”

Section: Related Workmentioning

confidence: 99%

Efficient content caching for 5G assisted vehicular networks

Ahmed,

Alsamani,

Alkhathami

et al. 2024

Sci Rep

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Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

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“…In practical edge caching networks, the popularity of contents may not be known in advance by the system [19], and may also vary over time [20]. Further, the CPs may keep generating new contents that may be of interest to users.…”

Section: Introductionmentioning

confidence: 99%

Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

Yi¹,

Lv²,

Wang³

et al. 2024

Digital Communications and Networks

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Collaborative Caching Strategy for RL-Based Content Downloading Algorithm in Clustered Vehicular Networks

Cited by 9 publications

References 32 publications

Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks

Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks

Efficient content caching for 5G assisted vehicular networks

Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

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