Blockchain-based Dependable Task Offloading and Resource Allocation for IIoT via Multi-Agent Deep Reinforcement Learning

被引:2
作者
Zhang, Peifeng [1 ,2 ,3 ,4 ]
Xu, Chi [1 ,2 ,3 ]
Xia, Changqing [1 ,2 ,3 ]
Jin, Xi [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang, Peoples R China
[2] Chinese Acad Sci, Key Lab Networked Control Syst, Shenyang, Peoples R China
[3] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang, Peoples R China
[4] Univ Chinese Acad Sci, Beijing, Peoples R China
来源
2023 IEEE 98TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-FALL | 2023年
基金
中国国家自然科学基金;
关键词
IIoT; blockchain; task offloading; resource allocation; multi-agent deep reinforcement learning;
D O I
10.1109/VTC2023-Fall60731.2023.10333859
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Task offloading and resource allocation are fundamental and crucial for the edge computing-enhanced industrial Internet of things, where the security and credibility among massive heterogeneous devices are being challenged. This paper first proposes a novel blockchain consensus scheme named replicated and Byzantine fault tolerant, which can enhance the trust among nodes with the low communication cost. Then, with the objective of minimizing the task completion time, which includes credible verification, task offloading and transaction record, a joint task offloading and resource allocation problem with respect to blockchain verification ratio, offloading decision, communication and computing resources is formulated. Due to its non-convexity and the decentralized characteristic of blockchain, a multi-agent deep reinforcement learning algorithm with deterministic policy gradient is proposed to appropriate the optimal solution. Experiment results confirm the effectiveness of the proposed scheme in guaranteeing the timeliness and security.
引用
收藏
页数:6
相关论文
共 13 条
[1]   Delay-Aware and Energy-Efficient Computation Offloading in Mobile-Edge Computing Using Deep Reinforcement Learning [J].
Ale, Laha ;
Zhang, Ning ;
Fang, Xiaojie ;
Chen, Xianfu ;
Wu, Shaohua ;
Li, Longzhuang .
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2021, 7 (03) :881-892
[2]   Multitask Multiobjective Deep Reinforcement Learning-Based Computation Offloading Method for Industrial Internet of Things [J].
Cai, Jun ;
Fu, Hongtian ;
Liu, Yan .
IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (02) :1848-1859
[3]   Cooperative and Distributed Computation Offloading for Blockchain-Empowered Industrial Internet of Things [J].
Chen, Wuhui ;
Zhang, Zhen ;
Hong, Zicong ;
Chen, Chuan ;
Wu, Jiajing ;
Maharjan, Sabita ;
Zheng, Zibin ;
Zhang, Yan .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (05) :8433-8446
[4]   A Resource Allocation Scheme for Joint Optimizing Energy Consumption and Delay in Collaborative Edge Computing-Based Industrial IoT [J].
Jin, Zilong ;
Zhang, Chengbo ;
Jin, Yuanfeng ;
Zhang, Lejun ;
Su, Jian .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (09) :6236-6243
[5]   Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks [J].
Kang, Jiawen ;
Yu, Rong ;
Huang, Xumin ;
Wu, Maoqiang ;
Maharjan, Sabita ;
Xie, Shengli ;
Zhang, Yan .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (03) :4660-4670
[6]   Deep Reinforcement Learning-Based Multichannel Access for Industrial Wireless Networks With Dynamic Multiuser Priority [J].
Liu, Xiaoyu ;
Xu, Chi ;
Yu, Haibin ;
Zeng, Peng .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (10) :7048-7058
[7]  
Nguyen Dinh, Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning, P1
[8]   Delay Minimization for NOMA-Enabled Mobile Edge Computing in Industrial Internet of Things [J].
Van Dat Tuong ;
Noh, Wonjong ;
Cho, Sungrae .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (10) :7321-7331
[9]  
Xu C., 2023, IEEE J. Sel. Areas Commun.
[10]   BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing [J].
Xu, Xiaolong ;
Zhang, Xuyun ;
Gao, Honghao ;
Xue, Yuan ;
Qi, Lianyong ;
Dou, Wanchun .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (06) :4187-4195