A Lightweight and Secure Vehicular Edge Computing Framework for V2X Services

被引：3
|
作者：
Ramneek [1 ]
Pack, Sangheon [1 ]
机构：
[1] Korea Univ, Seoul, South Korea
来源：
2022 IEEE 42ND INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2022) | 2022年
基金：
新加坡国家研究基金会;
关键词：
5G; VEC; V2X; DAG; INTERNET;
D O I：
10.1109/ICDCS54860.2022.00146
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Vehicle-to-everything (V2X) communications over cellular networks have a great potential for enabling intelligent transportation systems (ITSs), and supporting advanced services such as autonomous driving. However, such services have stringent QoS and security/privacy requirements. Even though the use of blockchain can ensure security and privacy for V2X services, blockchain-based solutions suffer from the issues of high latency, low scalability, and high computation power for mining. To overcome these challenges, we propose a lightweight and secure vehicular edge computing framework. The LS-VEC framework leverages directed acyclic graphs (DAGs) for recording transactions for edge resource allocation and micro-transactions for pricing VEC resources. In addition, an auction theory-based game-theoretic approach is proposed for allocation and pricing of edge resources used for supporting computation offloading.
引用
收藏
页码：1316 / 1317
页数：2
相关论文
共 50 条
  • [31] A Protocol for Traffic Congestion Control based on Vehicular Networks V2X
    Filho, Sebastiao Galeno P.
    de Sousa, Roniel Soares
    Soares, Andre C. B.
    [J]. PROCEEDINGS OF THE 2016 XLII LATIN AMERICAN COMPUTING CONFERENCE (CLEI), 2016,
  • [32] Latency Prediction for Delay-sensitive V2X Applications in Mobile Cloud/Edge Computing Systems
    Zhang, Wenhan
    Feng, Mingjie
    Krunz, Marwan
    Volos, Haris
    [J]. 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2020,
  • [33] A Statistical Learning Framework for QoS Prediction in V2X
    Gutierrez-Estevez, Miguel A.
    Utkovski, Zoran
    Kousaridas, Apostolos
    Zhou, Chan
    [J]. 2021 IEEE 4TH 5G WORLD FORUM (5GWF 2021), 2021, : 441 - 446
  • [34] Task-Efficiency Oriented V2X Communications: Digital Twin Meets Mobile Edge Computing
    Cai, Guoqiang
    Fan, Bo
    Dong, Yiwei
    Li, Tongfei
    Wu, Yuan
    Zhang, Yan
    [J]. IEEE WIRELESS COMMUNICATIONS, 2024, 31 (02) : 149 - 155
  • [35] Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
    Wang, Pangwei
    Liu, Xiao
    Wang, Yunfeng
    Wang, Tianren
    Zhang, Juan
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (23):
  • [36] Minimizing Latency for 5G Multimedia and V2X Applications using Mobile Edge Computing
    Srinivasa, R. K.
    Naidu, Naveen Kumar Srinivasa
    Maheshwari, Sumit
    Bharathi, C.
    Kumar, Hemanth A. R.
    [J]. 2019 2ND INTERNATIONAL CONFERENCE ON INTELLIGENT COMMUNICATION AND COMPUTATIONAL TECHNIQUES (ICCT), 2019, : 213 - 217
  • [37] A Simulation Framework for Validating Cellular V2X Scenarios
    Feljan, Aneta Vulgarakis
    Jin, Yifei
    [J]. IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2018, : 4078 - 4083
  • [38] An Intent-based Framework for Vehicular Edge Computing
    He, TianZhang
    Toosi, Adel N.
    Akbari, Negin
    Islam, Muhammed Tawfiqul
    Cheema, Muhammad Aamir
    [J]. 2023 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS, PERCOM, 2023, : 121 - 130
  • [39] LARP: A Lightweight Auto-Refreshing Pseudonym Protocol for V2X
    Yang, Zheng
    Tien Tuan Anh Dinh
    Yin, Chao
    Yao, Yingying
    Yang, Dianshi
    Chang, Xiaolin
    Zhou, Jianying
    [J]. PROCEEDINGS OF THE 27TH ACM SYMPOSIUM ON ACCESS CONTROL MODELS AND TECHNOLOGIES, SACMAT 2022, 2022, : 49 - 60
  • [40] LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing
    Zhang, Zhuangzhuang
    Wu, Libing
    Ma, Chuanguo
    Li, Jianxin
    Wang, Jing
    Wang, Qian
    Yu, Shui
    [J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2023, 18 : 365 - 379
12345