Rendering Secure and Trustworthy Edge Intelligence in 5G-Enabled IIoT Using Proof of Learning Consensus Protocol

被引:16
作者
Qiu, Chao [1 ,2 ]
Aujla, Gagangeet Singh [3 ]
Jiang, Jing [4 ]
Wen, Wu [5 ]
Zhang, Peiying [6 ]
机构
[1] Tianjin Univ, Coll Intelligence & Comp, Tianjin 300354, Peoples R China
[2] Guangdong Lab Artificial Intelligence & Digital E, Shenzhen 518132, Peoples R China
[3] Univ Durham, Sch Comp Sci, Durham DH1 3LE, England
[4] Xian Univ Posts & Telecommun, Shaanxi Key Lab Informat Commun Network & Secur, Xian 710061, Peoples R China
[5] Guangzhou Univ, Sch Comp Sci & Cyber Engn, Guangzhou 510006, Peoples R China
[6] China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
基金
美国国家科学基金会; 中国博士后科学基金;
关键词
Industrial Internet of Things; Training; Security; Consensus protocol; Protocols; Artificial intelligence; Task analysis; Blockchain; edge intelligence; industrial Internet of Things (IIoT); proof of learning (PoL); reputation opinion; RESOURCE-ALLOCATION; INDUSTRIAL INTERNET; THINGS;
D O I
10.1109/TII.2022.3179272
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision-making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this article, we propose a proof-of-learning consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge's reputation opinion by executing a smart contract, and partly adopts the winner's learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts.
引用
收藏
页码:900 / 909
页数:10
相关论文
共 24 条
[1]   Enhanced Online Q-Learning Scheme for Resource Allocation with Maximum Utility and Fairness in Edge-IoT Networks [J].
AlQerm, Ismail ;
Pan, Jianli .
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2020, 7 (04) :3074-3086
[2]   A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks [J].
Ayaz, Ferheen ;
Sheng, Zhengguo ;
Tian, Daxin ;
Guan, Yong Liang .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (02) :1927-1940
[3]   Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning [J].
Baldominos, Alejandro ;
Saez, Yago .
ENTROPY, 2019, 21 (08)
[4]   Proof-of-Learning: a Blockchain Consensus Mechanism based on Machine Learning Competitions [J].
Bravo-Marquez, Felipe ;
Reeves, Steve ;
Ugarte, Martin .
2019 IEEE INTERNATIONAL CONFERENCE ON DECENTRALIZED APPLICATIONS AND INFRASTRUCTURES (DAPPCON), 2019, :119-124
[5]  
Chenli C, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN AND CRYPTOCURRENCY (ICBC), P19, DOI [10.1109/bloc.2019.8751419, 10.1109/BLOC.2019.8751419]
[6]   Computation Offloading and Resource Allocation in Vehicular Networks Based on Dual-Side Cost Minimization [J].
Du, Jianbo ;
Yu, F. Richard ;
Chu, Xiaoli ;
Feng, Jie ;
Lu, Guangyue .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (02) :1079-1092
[7]   Sensor Cloud Frameworks: State-of-the-Art, Taxonomy, and Research Issues [J].
Haseeb-Ur-Rehman, Rana M. Abdul ;
Liaqat, Misbah ;
Aman, Azana Hafizah Mohd ;
Ab Hamid, Siti Hafizah ;
Ali, Rana Liaqat ;
Shuja, Junaid ;
Khan, Muhammad Khurram .
IEEE SENSORS JOURNAL, 2021, 21 (20) :22347-22370
[8]   Software Defined Networking for Energy Harvesting Internet of Things [J].
Huang, Xumin ;
Yu, Rong ;
Kang, Jiawen ;
Xia, Zhuoquan ;
Zhang, Yan .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (03) :1389-1399
[9]   Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory [J].
Kang, Jiawen ;
Xiong, Zehui ;
Niyato, Dusit ;
Xie, Shengli ;
Zhang, Junshan .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (06) :10700-10714
[10]   Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach [J].
Kang, Jiawen ;
Xiong, Zehui ;
Niyato, Dusit ;
Yu, Han ;
Liang, Ying-Chang ;
Kim, Dong In .
2019 IEEE VTS ASIA PACIFIC WIRELESS COMMUNICATIONS SYMPOSIUM (APWCS 2019), 2019,