.png){kind=icon}
返回
Blockchain-Enabled Intelligent Vehicular Edge Computing
.png){kind=icon}
OA
AI
DOI：10.1109/MNET.011.2000591.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Smart vehicles are expected to be equipped with high-dimensional, resource-intensive applications, including platoon control, augmented reality supported gaming, AI-based pedestrian detection, fuel scheduling, and so on, catering to diverse user preferences and enhancing safety and efficiency. These applications pose unique challenges for resource-constrained vehicles due to their intense computation requirements, whereas vehicular edge computing (VEC) networks, consisting of roadside units (RSUs) and MEC servers, contain the capability of providing cloud-like computing experience at vehicular edges while meeting performance requirements in terms of latency and throughput. Moreover, the development of intelligent VEC (IVEC) infrastructure is accelerated due to rapid advancement of AI algorithms in recent years. However, IVEC is prone to attacks, including fake computation feedback, unfair or biased resource allocation in a VEC server, and so on, due to its centralized governance and black box computation (edge computation works like a black box for end users). To combat such security vulnerabilities, we propose a blockchain-based decentralized architecture to enhance transparency in IVEC resource management and leverage edge consumers (e.g., vehicles) with a computation verification option. Additionally, we address the unbalanced load distribution issue and propose a secure IVEC federation model for balancing loads. We also outline the main challenges and provide a brief description of promising research directions to draw the attention of concerned stakeholders and parties in both the blockchain and edge computing domains.
Keyword:
Computational modeling
Blockchain
Computer architecture
Computational efficiency
Resource management
Servers
Security
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
2.7K
被引数:
1.1W
机构
引用论文
Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks基于深度学习的无线资源分配及其在车载网络中的应用.png)
PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏DNA-dependent protein kinase is not required for the p53-dependent response to DNA damageNature
NatureIF0
PREAI
分享收藏Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning基于多智能体强化学习的车载网络频谱共享OAAI
OAAI分享收藏Crystal structure of l-tris(1,10-phenathroline)iron(II) bis(antimony(III) d-tartrate) octahydrate
PREAI
分享收藏Energy-Efficient Joint User-RB Association and Power Allocation for Uplink Hybrid NOMA-OMA上行链路混合NOMA-OMA的高能效联合用户-RB关联和功率分配OAAI
OAAI分享收藏
查看更多内容