Blockchain-Enabled Secure Offloading for VEC: A Multi-Agent Reinforcement Learning Approach

作     者：Lu, Xiaozhen Xiao, Liang Xiao, Yilin Xiong, Zehui Liu, Zhe Zhang, Yanyong Zhuang, Weihua 

作者机构：Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore 487372 Singapore Zhejiang Lab Hangzhou 310027 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada 

出 版 物：《IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING》 (IEEE Trans. Dependable Secure Comput.)

年 卷 期：2025年第22卷第3期

页      面：2978-2995页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [62202222, U21A20444, 62301335, 62132008, 62472218, U22B2030] National Natural Science Foundation of Jiangsu Province [BK20220880, BK20220075] Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu [BK20222001] National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research and Development Programme SUTD-ZJU IDEA [SUTD-ZJU (VP) 202102] Ministry of Education, Singapore, under its Academic Research Fund Tier 2 [MOE-T2EP20221-0017] SMU-SUTD Joint [22-SIS-SMU-048] SUTD Kickstarter Initiative [SKI 20210204] 

主　　题：Eavesdropping Energy consumption Security Blockchains Smart contracts Resists Quality of service Edge computing Resource management Measurement Secure offloading vehicular edge computing multi-agent reinforcement learning safe exploration smart contract 

摘      要：Vehicular edge computing (VEC) helps improve the task computational performance of vehicles on roads but has difficulty in defending against eavesdropping and selfish attacks simultaneously. In this paper, we design a reputation-based smart contract with blockchain and propose a multi-agent reinforcement learning (RL) based secure offloading scheme for VEC against both eavesdropping and selfish attacks. This scheme has a three-level hierarchical structure for each vehicle and uses the reputations obtained from the blockchain as the basis to optimize the edge node selection, offloading ratio, and power allocation, which aims to reduce the task computational latency, the vehicle energy consumption and eavesdropping rate. By using a punishment function based on the constraints, this scheme avoids exploring dangerous policies that can cause task failure or severe data leakage. A multi-agent deep RL-based secure offloading scheme is proposed for vehicles with sufficient resources, which evaluates the long-term risk rather than the punishment function to further improve the secure offloading performance. The regret bound is analyzedand the cumulative reward upper bound is provided. Simulation results verify the effectiveness of our schemes as compared with the benchmark.