Collaborative Task Processing in Vehicular Edge Computing Networks

Bai, Xiao; Chen, Shanzhi; Shi, Yan; Liang, Chengzhi; Lv, Xiaochen

doi:10.1109/hoticn53262.2021.9680819

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In [ 36 ], Zhang et al constructed a software-defined networking (SDN)-assisted VEC system with the goal of optimizing the system overhead while considering 802.11p-based V2I communication and V2V communication. In [ 37 ], Bai et al designed a VEC system containing vehicular cloudlets and considered D2D-based V2V communication. However, these works did not consider the coexistence of MIMO-NOMA-based V2I communication and D2D-based V2V communication in VEC systems.…”

Section: Related Workmentioning

confidence: 99%

A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL

Long

Fan

et al. 2023

Sensors

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In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward.

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Section: Related Workmentioning

confidence: 99%

A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL

Long

Fan

et al. 2023

Sensors

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show abstract

“…MEC is a novel computing technology that shifts data processing and analysis tasks from central servers to edge devices on a network, thereby reducing the overall communication and computing latency [ 8 ]. By deploying servers near users, MEC provides abundant network resources for resource-constrained vehicles, minimizing the computational latency of vehicle applications and enhancing the computing and storage capabilities of the IoV [ 9 , 10 ]. However, vehicles must offload data and computing tasks to edge nodes before receiving processing results.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Assisted Reputation Management Scheme for Internet of Vehicles

Liu

Gong

Liu

2023

Sensors

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With the rapid development of Internet of Vehicles (IoV), particularly the introduction of Mobile Edge Computing (MEC), vehicles can efficiently share data with one another. However, edge computing nodes are vulnerable to various network attacks, posing security risks to data storage and sharing. Moreover, the presence of abnormal vehicles during the sharing process poses significant security threats to the entire network. To address these issues, this paper proposes a novel reputation management scheme, which proposes an improved multi-source multi-weight subjective logic algorithm. This algorithm fuses the direct and indirect opinion feedback of nodes through the subjective logic trust model while considering factors such as event validity, familiarity, timeliness, and trajectory similarity. Vehicle reputation values are periodically updated, and abnormal vehicles are identified through reputation thresholds. Finally, blockchain technology is employed to ensure the security of data storage and sharing. By analyzing real vehicle trajectory datasets, the algorithm is proven to effectively improve the differentiation and detection rate of abnormal vehicles.

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