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2022 | OriginalPaper | Buchkapitel

4. Blockchain Driven Edge Intelligence

verfasst von : Xiaofei Wang, Chao Qiu, Xiaoxu Ren, Zehui Xiong, Victor C. M. Leung, Dusit Niyato

Erschienen in: Integrating Edge Intelligence and Blockchain

Verlag: Springer International Publishing

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Abstract

BC-driven EI focuses on addressing the challenges of EI as described in Chap. 4. In this chapter, we present the EI benefits that can be realized with the assistance of BC, including computing-power management, data administration and model optimization.

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Literatur
1.
Zurück zum Zitat X. Chen, L. Pu, L. Gao, W. Wu, D. Wu, Exploiting massive D2D collaboration for energy-efficient mobile edge computing. IEEE Wirel. Commun. 24(4), 64–71 (2017)CrossRef X. Chen, L. Pu, L. Gao, W. Wu, D. Wu, Exploiting massive D2D collaboration for energy-efficient mobile edge computing. IEEE Wirel. Commun. 24(4), 64–71 (2017)CrossRef
2.
Zurück zum Zitat Y. Zhang, R. Yu, M. Nekovee, Y. Liu, S. Xie, S. Gjessing, Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw. 26(3), 6–13 (2012)CrossRef Y. Zhang, R. Yu, M. Nekovee, Y. Liu, S. Xie, S. Gjessing, Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw. 26(3), 6–13 (2012)CrossRef
3.
Zurück zum Zitat J. Kang, R. Yu, X. Huang, S. Maharjan, Y. Zhang, E. Hossain, Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans. Ind. Inf. 13(6), 3154–3164 (2017)CrossRef J. Kang, R. Yu, X. Huang, S. Maharjan, Y. Zhang, E. Hossain, Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans. Ind. Inf. 13(6), 3154–3164 (2017)CrossRef
4.
Zurück zum Zitat H. Yao, T. Mai, J. Wang, Z. Ji, C. Jiang, Y. Qian, Resource trading in blockchain-based industrial internet of things. IEEE Trans. Ind. Inf. 15(6), 3602–3609 (2019)CrossRef H. Yao, T. Mai, J. Wang, Z. Ji, C. Jiang, Y. Qian, Resource trading in blockchain-based industrial internet of things. IEEE Trans. Ind. Inf. 15(6), 3602–3609 (2019)CrossRef
5.
Zurück zum Zitat Z. Xiong, Y. Zhang, D. Niyato, P. Wang, Z. Han, When mobile blockchain meets edge computing. IEEE Commun. Mag. 56(8), 33–39 (2018)CrossRef Z. Xiong, Y. Zhang, D. Niyato, P. Wang, Z. Han, When mobile blockchain meets edge computing. IEEE Commun. Mag. 56(8), 33–39 (2018)CrossRef
6.
Zurück zum Zitat Z. Xiong, S. Feng, W. Wang, D. Niyato, P. Wang, Z. Han, Cloud/fog computing resource management and pricing for blockchain networks. IEEE Internet Things J. 63, 4585–4600 (2019)CrossRef Z. Xiong, S. Feng, W. Wang, D. Niyato, P. Wang, Z. Han, Cloud/fog computing resource management and pricing for blockchain networks. IEEE Internet Things J. 63, 4585–4600 (2019)CrossRef
7.
Zurück zum Zitat R. Yang, F. R. Yu, P. Si, Z. Yang, Y. Zhang, Integrated blockchain and edge computing systems: a survey, some research issues and challenges. IEEE Commun. Surv. Tutorials 21(2), 1508–1532 (2019)CrossRef R. Yang, F. R. Yu, P. Si, Z. Yang, Y. Zhang, Integrated blockchain and edge computing systems: a survey, some research issues and challenges. IEEE Commun. Surv. Tutorials 21(2), 1508–1532 (2019)CrossRef
8.
Zurück zum Zitat A. Asheralieva, D. Niyato, Learning-based mobile edge computing resource management to support public blockchain networks. IEEE Trans. Mob. Comput. 20(3), 1092–1109 (2021)CrossRef A. Asheralieva, D. Niyato, Learning-based mobile edge computing resource management to support public blockchain networks. IEEE Trans. Mob. Comput. 20(3), 1092–1109 (2021)CrossRef
9.
Zurück zum Zitat A. Asheralieva, D. Niyato, Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-Enabled mobile edge computing. IEEE Internet Things J. 7(3), 1974–1993 (2020)CrossRef A. Asheralieva, D. Niyato, Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-Enabled mobile edge computing. IEEE Internet Things J. 7(3), 1974–1993 (2020)CrossRef
10.
Zurück zum Zitat Y. Cao, X. Ren, C. Qiu, X. Wang, Hierarchical reinforcement learning for blockchain-assisted software defined industrial energy. IEEE Trans. Ind. Inf. 18(9), 6100–6108 (2022)CrossRef Y. Cao, X. Ren, C. Qiu, X. Wang, Hierarchical reinforcement learning for blockchain-assisted software defined industrial energy. IEEE Trans. Ind. Inf. 18(9), 6100–6108 (2022)CrossRef
13.
Zurück zum Zitat Z. Xie, R. Wu, M. Hu, H. Tian, Blockchain-enabled computing resource trading: A deep reinforcement learning approach, in 2020 IEEE Wireless Communications and Networking Conference (WCNC) (2020), pp. 1–8 Z. Xie, R. Wu, M. Hu, H. Tian, Blockchain-enabled computing resource trading: A deep reinforcement learning approach, in 2020 IEEE Wireless Communications and Networking Conference (WCNC) (2020), pp. 1–8
14.
Zurück zum Zitat S. Fan, H. Zhang, Y. Zeng, W. Cai, Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet Things J. 8(4), 2252–2264 (2021)CrossRef S. Fan, H. Zhang, Y. Zeng, W. Cai, Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet Things J. 8(4), 2252–2264 (2021)CrossRef
15.
Zurück zum Zitat N.C. Luong, Z. Xiong, P. Wang, D. Niyato, Optimal auction for edge computing resource management in mobile blockchain networks: A deep learning approach, in 2018 IEEE International Conference on Communications (ICC) (2018), pp. 1–6 N.C. Luong, Z. Xiong, P. Wang, D. Niyato, Optimal auction for edge computing resource management in mobile blockchain networks: A deep learning approach, in 2018 IEEE International Conference on Communications (ICC) (2018), pp. 1–6
16.
Zurück zum Zitat N.C. Luong, Y. Jiao, P. Wang, D. Niyato, D.I. Kim, Z. Han, A machine-learning-based auction for resource trading in fog computing. IEEE Commun. Mag. 58(3), 82–88 (2020)CrossRef N.C. Luong, Y. Jiao, P. Wang, D. Niyato, D.I. Kim, Z. Han, A machine-learning-based auction for resource trading in fog computing. IEEE Commun. Mag. 58(3), 82–88 (2020)CrossRef
17.
Zurück zum Zitat S. Yu, X. Chen, Z. Zhou, X. Gong, D. Wu, When deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network. IEEE Internet Things J. 8(4), 2238–2251 (2021)CrossRef S. Yu, X. Chen, Z. Zhou, X. Gong, D. Wu, When deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network. IEEE Internet Things J. 8(4), 2238–2251 (2021)CrossRef
18.
Zurück zum Zitat X. Fu, F.R. Yu, J. Wang, Q. Qi, J. Liao, Resource allocation for blockchain-enabled distributed network function virtualization (NFV) with mobile edge cloud (MEC), in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2019), pp. 1–6 X. Fu, F.R. Yu, J. Wang, Q. Qi, J. Liao, Resource allocation for blockchain-enabled distributed network function virtualization (NFV) with mobile edge cloud (MEC), in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2019), pp. 1–6
19.
Zurück zum Zitat F. Guo, F.R. Yu, H. Zhang, H. Ji, M. Liu, V.C.M. Leung, Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing. IEEE Trans. Wirel. Commun. 19(3), 1689–1703 (2020)CrossRef F. Guo, F.R. Yu, H. Zhang, H. Ji, M. Liu, V.C.M. Leung, Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing. IEEE Trans. Wirel. Commun. 19(3), 1689–1703 (2020)CrossRef
20.
Zurück zum Zitat P.K. Singh, R. Singh, S.K. Nandi, K.Z. Ghafoor, D.B. Rawat, S. Nandi, Blockchain-based adaptive trust management in internet of vehicles using smart contract. IEEE Trans. Intell. Transp. Syst. 22(6), 3616–3630 (2021)CrossRef P.K. Singh, R. Singh, S.K. Nandi, K.Z. Ghafoor, D.B. Rawat, S. Nandi, Blockchain-based adaptive trust management in internet of vehicles using smart contract. IEEE Trans. Intell. Transp. Syst. 22(6), 3616–3630 (2021)CrossRef
21.
Zurück zum Zitat R. Casado-Vara, P. Chamoso, F. De la Prieta, et al., Non-linear adaptive closed-loop control system for improved efficiency in IoT-blockchain management. Inf. Fusion 49, 227–239 (2019)CrossRef R. Casado-Vara, P. Chamoso, F. De la Prieta, et al., Non-linear adaptive closed-loop control system for improved efficiency in IoT-blockchain management. Inf. Fusion 49, 227–239 (2019)CrossRef
22.
Zurück zum Zitat M. Liu, F.R. Yu, Y. Teng, V.C.M. Leung, M. Song, Distributed resource allocation in blockchain-based video streaming systems with mobile edge computing. IEEE Trans. Wireless Commun. 18(1), 695–708 (2019)CrossRef M. Liu, F.R. Yu, Y. Teng, V.C.M. Leung, M. Song, Distributed resource allocation in blockchain-based video streaming systems with mobile edge computing. IEEE Trans. Wireless Commun. 18(1), 695–708 (2019)CrossRef
23.
Zurück zum Zitat L. Xiao, Y. Ding, D. Jiang, J. Huang, D. Wang, J. Li, H.V. Poor, A reinforcement learning and blockchain-based trust mechanism for edge networks. IEEE Trans. Commun. 68(9), 5460–5470 (2020)CrossRef L. Xiao, Y. Ding, D. Jiang, J. Huang, D. Wang, J. Li, H.V. Poor, A reinforcement learning and blockchain-based trust mechanism for edge networks. IEEE Trans. Commun. 68(9), 5460–5470 (2020)CrossRef
24.
Zurück zum Zitat M. Li, F.R. Yu, P. Si, W. Wu, Y. Zhang, Resource optimization for delay-tolerant data in blockchain-enabled IoT with edge computing: a deep reinforcement learning approach. IEEE Internet Things J. 7(10), 9399–9412 (2020)CrossRef M. Li, F.R. Yu, P. Si, W. Wu, Y. Zhang, Resource optimization for delay-tolerant data in blockchain-enabled IoT with edge computing: a deep reinforcement learning approach. IEEE Internet Things J. 7(10), 9399–9412 (2020)CrossRef
25.
Zurück zum Zitat S. Guo, Y. Dai, S. Xu, X. Qiu, F. Qi, Trusted cloud-edge network resource management: DRL-Driven service function chain orchestration for IoT. IEEE Internet Things J. 7(7), 6010–6022 (2020)CrossRef S. Guo, Y. Dai, S. Xu, X. Qiu, F. Qi, Trusted cloud-edge network resource management: DRL-Driven service function chain orchestration for IoT. IEEE Internet Things J. 7(7), 6010–6022 (2020)CrossRef
26.
Zurück zum Zitat Y. He, Y. Wang, C. Qiu, Q. Lin, J. Li, Z. Ming, Blockchain-based edge computing resource allocation in IoT: a deep reinforcement learning approach. IEEE Internet Things J. 8(4), 2226–2237 (2021)CrossRef Y. He, Y. Wang, C. Qiu, Q. Lin, J. Li, Z. Ming, Blockchain-based edge computing resource allocation in IoT: a deep reinforcement learning approach. IEEE Internet Things J. 8(4), 2226–2237 (2021)CrossRef
27.
Zurück zum Zitat G. Zyskind, O. Nathan, A. Pentland, Decentralizing privacy: Using blockchain to protect personal data, in 2015 IEEE Security and Privacy Workshops (2015), pp. 180–184 G. Zyskind, O. Nathan, A. Pentland, Decentralizing privacy: Using blockchain to protect personal data, in 2015 IEEE Security and Privacy Workshops (2015), pp. 180–184
28.
Zurück zum Zitat N.Z. Aitzhan, D. Svetinovic, Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streams. IEEE Trans. Dependable Secure Comput. 15(5), 840–852 (2018)CrossRef N.Z. Aitzhan, D. Svetinovic, Security and privacy in decentralized energy trading through multi-signatures, blockchain and anonymous messaging streams. IEEE Trans. Dependable Secure Comput. 15(5), 840–852 (2018)CrossRef
29.
Zurück zum Zitat J. Feng, F.R. Yu, Q. Pei, X. Chu, J. Du, L. Zhu, Cooperative computation offloading and resource allocation for blockchain-enabled mobile-edge computing: A deep reinforcement learning approach. IEEE Internet Things J. 7(7), 6214–6228 (2020)CrossRef J. Feng, F.R. Yu, Q. Pei, X. Chu, J. Du, L. Zhu, Cooperative computation offloading and resource allocation for blockchain-enabled mobile-edge computing: A deep reinforcement learning approach. IEEE Internet Things J. 7(7), 6214–6228 (2020)CrossRef
30.
Zurück zum Zitat X. Zheng, M. Li, Y. Chen, J. Guo, M. Alam, W. Hu, Blockchain-based secure computation offloading in vehicular networks. IEEE Trans. Intell. Transp. Syst. 22(7), 4073–4087 (2021)CrossRef X. Zheng, M. Li, Y. Chen, J. Guo, M. Alam, W. Hu, Blockchain-based secure computation offloading in vehicular networks. IEEE Trans. Intell. Transp. Syst. 22(7), 4073–4087 (2021)CrossRef
31.
Zurück zum Zitat H. Liao, Y. Mu, Z. Zhou, M. Sun, Z. Wang, C. Pan, Blockchain and learning-based secure and intelligent task offloading for vehicular fog computing. IEEE Trans. Intell. Transp. Syst. 22(7), 4051–4063 (2021)CrossRef H. Liao, Y. Mu, Z. Zhou, M. Sun, Z. Wang, C. Pan, Blockchain and learning-based secure and intelligent task offloading for vehicular fog computing. IEEE Trans. Intell. Transp. Syst. 22(7), 4051–4063 (2021)CrossRef
32.
Zurück zum Zitat M. Liu, F.R. Yu, Y. Teng, V.C.M. Leung, M. Song, Joint computation offloading and content caching for wireless blockchain networks, in IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2018), pp. 517–522 M. Liu, F.R. Yu, Y. Teng, V.C.M. Leung, M. Song, Joint computation offloading and content caching for wireless blockchain networks, in IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2018), pp. 517–522
33.
Zurück zum Zitat Z. Li, M. Xu, J. Nie, J. Kang, W. Chen, S. Xie, NOMA-enabled cooperative computation offloading for blockchain-empowered internet of things: a learning approach. IEEE Internet Things J. 8(4), 2364–2378, (2021)CrossRef Z. Li, M. Xu, J. Nie, J. Kang, W. Chen, S. Xie, NOMA-enabled cooperative computation offloading for blockchain-empowered internet of things: a learning approach. IEEE Internet Things J. 8(4), 2364–2378, (2021)CrossRef
34.
Zurück zum Zitat L. Yang, M. Li, P. Si, R. Yang, E. Sun, Y. Zhang, Energy-efficient resource allocation for blockchain-enabled industrial internet of things with deep reinforcement learning. IEEE Internet Things J. 8(4), 2318–2329 (2021)CrossRef L. Yang, M. Li, P. Si, R. Yang, E. Sun, Y. Zhang, Energy-efficient resource allocation for blockchain-enabled industrial internet of things with deep reinforcement learning. IEEE Internet Things J. 8(4), 2318–2329 (2021)CrossRef
35.
Zurück zum Zitat X. Qiu, L. Liu, W. Chen, Z. Hong, Z. Zheng, Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing. IEEE Trans. Veh. Technol. 68(8), 8050–8062 (2019)CrossRef X. Qiu, L. Liu, W. Chen, Z. Hong, Z. Zheng, Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing. IEEE Trans. Veh. Technol. 68(8), 8050–8062 (2019)CrossRef
36.
Zurück zum Zitat H. Wu, K. Wolter, P. Jiao, Y. Deng, Y. Zhao, M. Xu, EEDTO: an energy-efficient dynamic task offloading algorithm for blockchain-enabled IoT-edge-cloud orchestrated computing. IEEE Internet Things J. 8(4), 2163–2176 (2021)CrossRef H. Wu, K. Wolter, P. Jiao, Y. Deng, Y. Zhao, M. Xu, EEDTO: an energy-efficient dynamic task offloading algorithm for blockchain-enabled IoT-edge-cloud orchestrated computing. IEEE Internet Things J. 8(4), 2163–2176 (2021)CrossRef
37.
Zurück zum Zitat S.-C. Oh, M.-S. Kim, Y. Park, G.-T. Roh, C.-W. Lee, Implementation of blockchain-based energy trading system. Asia Pac. J. Innovat. Entrep. 11(3), 322–334 (2017) S.-C. Oh, M.-S. Kim, Y. Park, G.-T. Roh, C.-W. Lee, Implementation of blockchain-based energy trading system. Asia Pac. J. Innovat. Entrep. 11(3), 322–334 (2017)
38.
Zurück zum Zitat M.G. Xevgenis, D.G. Kogias, P. Karkazis, H.C. Leligou, C.Z. Patrikakis, Application of blockchain technology in dynamic resource management of next generation networks. Information 11(12), 570 (2020) M.G. Xevgenis, D.G. Kogias, P. Karkazis, H.C. Leligou, C.Z. Patrikakis, Application of blockchain technology in dynamic resource management of next generation networks. Information 11(12), 570 (2020)
39.
Zurück zum Zitat K.M. Venkateswarlu, S. Avizheh, R. Safavi-Naini, A blockchain based approach to resource sharing in smart neighbourhoods, in Financial Cryptography and Data Security - FC 2020 International Workshops, AsiaUSEC, CoDeFi, VOTING, and WTSC, Kota Kinabalu, Malaysia, February 14, 2020, Revised Selected Papers, ser. Lecture Notes in Computer Science, vol. 12063 (Springer, Berlin, 2020), pp. 550–567 K.M. Venkateswarlu, S. Avizheh, R. Safavi-Naini, A blockchain based approach to resource sharing in smart neighbourhoods, in Financial Cryptography and Data Security - FC 2020 International Workshops, AsiaUSEC, CoDeFi, VOTING, and WTSC, Kota Kinabalu, Malaysia, February 14, 2020, Revised Selected Papers, ser. Lecture Notes in Computer Science, vol. 12063 (Springer, Berlin, 2020), pp. 550–567
40.
Zurück zum Zitat X. Wang, C. Wang, X. Li, V.C.M. Leung, T. Taleb, Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching. IEEE Internet Things J. 7(10), 9441–9455 (2020)CrossRef X. Wang, C. Wang, X. Li, V.C.M. Leung, T. Taleb, Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching. IEEE Internet Things J. 7(10), 9441–9455 (2020)CrossRef
41.
Zurück zum Zitat Z. Ning, Y. Feng, M. Collotta, X. Kong, X. Wang, L. Guo, X. Hu, B. Hu, Deep learning in edge of vehicles: Exploring trirelationship for data transmission. IEEE Trans. Ind. Inf. 15(10), 5737–5746 (2019)CrossRef Z. Ning, Y. Feng, M. Collotta, X. Kong, X. Wang, L. Guo, X. Hu, B. Hu, Deep learning in edge of vehicles: Exploring trirelationship for data transmission. IEEE Trans. Ind. Inf. 15(10), 5737–5746 (2019)CrossRef
42.
Zurück zum Zitat X. Lin, J. Li, J. Wu, H. Liang, W. Yang, Making knowledge tradable in edge-ai enabled iot: a consortium blockchain-based efficient and incentive approach. IEEE Trans. Ind. Inf. 15(12), 6367–6378 (2019)CrossRef X. Lin, J. Li, J. Wu, H. Liang, W. Yang, Making knowledge tradable in edge-ai enabled iot: a consortium blockchain-based efficient and incentive approach. IEEE Trans. Ind. Inf. 15(12), 6367–6378 (2019)CrossRef
43.
Zurück zum Zitat H. Chai, S. Leng, Y. Chen, K. Zhang, A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Trans. Intell. Transp. Syst. 22(7), 3975–3986 (2021)CrossRef H. Chai, S. Leng, Y. Chen, K. Zhang, A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Trans. Intell. Transp. Syst. 22(7), 3975–3986 (2021)CrossRef
44.
Zurück zum Zitat L. Zhu, H. Dong, M. Shen, K. Gai, An incentive mechanism using shapley value for blockchain-based medical data sharing, in 5th IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) (2019), pp. 113–118 L. Zhu, H. Dong, M. Shen, K. Gai, An incentive mechanism using shapley value for blockchain-based medical data sharing, in 5th IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) (2019), pp. 113–118
45.
Zurück zum Zitat W. Xiong, L. Xiong, Smart contract based data trading mode using blockchain and machine learning. IEEE Access 7, 102331–102344 (2019)CrossRef W. Xiong, L. Xiong, Smart contract based data trading mode using blockchain and machine learning. IEEE Access 7, 102331–102344 (2019)CrossRef
46.
Zurück zum Zitat M. Hanley, H. Tewari, Managing lifetime healthcare data on the blockchain, in 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (2018), pp. 246–251 M. Hanley, H. Tewari, Managing lifetime healthcare data on the blockchain, in 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (2018), pp. 246–251
47.
Zurück zum Zitat V. Sharma, I. You, D.N.K. Jayakody, D. Gutiérrez-Reina, K.R. Choo, Neural-blockchain-based ultrareliable caching for edge-enabled UAV networks. IEEE Trans. Ind. Inf. 15(10), 5723–5736 (2019)CrossRef V. Sharma, I. You, D.N.K. Jayakody, D. Gutiérrez-Reina, K.R. Choo, Neural-blockchain-based ultrareliable caching for edge-enabled UAV networks. IEEE Trans. Ind. Inf. 15(10), 5723–5736 (2019)CrossRef
48.
Zurück zum Zitat Y. Dai, D. Xu, K. Zhang, S. Maharjan, Y. Zhang, Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks. IEEE Trans. Veh. Technol. 69(4), 4312–4324 (2020)CrossRef Y. Dai, D. Xu, K. Zhang, S. Maharjan, Y. Zhang, Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks. IEEE Trans. Veh. Technol. 69(4), 4312–4324 (2020)CrossRef
49.
Zurück zum Zitat Y. Qian, Y. Jiang, L. Hu, M.S. Hossain, M. Alrashoud, M.H. Al-Hammadi, Blockchain-based privacy-aware content caching in cognitive internet of vehicles. IEEE Netw. 34(2), 46–51 (2020)CrossRef Y. Qian, Y. Jiang, L. Hu, M.S. Hossain, M. Alrashoud, M.H. Al-Hammadi, Blockchain-based privacy-aware content caching in cognitive internet of vehicles. IEEE Netw. 34(2), 46–51 (2020)CrossRef
50.
Zurück zum Zitat R. Zhang, F.R. Yu, J. Liu, R. Xie, T. Huang, Blockchain-incentivized D2D and mobile edge caching: a deep reinforcement learning approach. IEEE Netw. 34(4), 150–157 (2020)CrossRef R. Zhang, F.R. Yu, J. Liu, R. Xie, T. Huang, Blockchain-incentivized D2D and mobile edge caching: a deep reinforcement learning approach. IEEE Netw. 34(4), 150–157 (2020)CrossRef
51.
Zurück zum Zitat J. Zhang, F. Zhang, X. Huang, X. Liu, Leakage-resilient authenticated key exchange for edge artificial intelligence. IEEE Trans. Dependable Secur. Comput. 18(6), 2835–2847 (2021)CrossRef J. Zhang, F. Zhang, X. Huang, X. Liu, Leakage-resilient authenticated key exchange for edge artificial intelligence. IEEE Trans. Dependable Secur. Comput. 18(6), 2835–2847 (2021)CrossRef
52.
Zurück zum Zitat B. Yin, H. Yin, Y. Wu, Z. Jiang, FDC: a secure federated deep learning mechanism for data collaborations in the internet of things. IEEE Internet Things J. 7(7), 6348–6359 (2020)CrossRef B. Yin, H. Yin, Y. Wu, Z. Jiang, FDC: a secure federated deep learning mechanism for data collaborations in the internet of things. IEEE Internet Things J. 7(7), 6348–6359 (2020)CrossRef
53.
Zurück zum Zitat C.H. Liu, Q. Lin, S. Wen, Blockchain-enabled data collection and sharing for industrial iot with deep reinforcement learning. IEEE Trans. Ind. Inf. 15(6), 3516–3526 (2019)CrossRef C.H. Liu, Q. Lin, S. Wen, Blockchain-enabled data collection and sharing for industrial iot with deep reinforcement learning. IEEE Trans. Ind. Inf. 15(6), 3516–3526 (2019)CrossRef
54.
Zurück zum Zitat M.A. Khan, S. Abbas, A. Rehman, Y. Saeed, A. Zeb, M.I. Uddin, N. Nasser, A. Ali, A machine learning approach for blockchain-based smart home networks security. IEEE Netw. 35(3), 223–229 (2021)CrossRef M.A. Khan, S. Abbas, A. Rehman, Y. Saeed, A. Zeb, M.I. Uddin, N. Nasser, A. Ali, A machine learning approach for blockchain-based smart home networks security. IEEE Netw. 35(3), 223–229 (2021)CrossRef
55.
Zurück zum Zitat M. Keshk, B.P. Turnbull, N. Moustafa, D. Vatsalan, K.R. Choo, A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks. IEEE Trans. Ind. Inf. 16(8), 5110–5118 (2020)CrossRef M. Keshk, B.P. Turnbull, N. Moustafa, D. Vatsalan, K.R. Choo, A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks. IEEE Trans. Ind. Inf. 16(8), 5110–5118 (2020)CrossRef
56.
Zurück zum Zitat Y. Lu, X. Huang, Y. Dai, S. Maharjan, Y. Zhang, Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans. Ind. Inf. 16(6), 4177–4186 (2020)CrossRef Y. Lu, X. Huang, Y. Dai, S. Maharjan, Y. Zhang, Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans. Ind. Inf. 16(6), 4177–4186 (2020)CrossRef
57.
Zurück zum Zitat X. Zheng, R.R. Mukkamala, R. Vatrapu, J.B.O. Meré, Blockchain-based personal health data sharing system using cloud storage, in 20th IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) (2018), pp. 1–6 X. Zheng, R.R. Mukkamala, R. Vatrapu, J.B.O. Meré, Blockchain-based personal health data sharing system using cloud storage, in 20th IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) (2018), pp. 1–6
58.
Zurück zum Zitat P.V. Kakarlapudi, Q.H. Mahmoud, Design and development of a blockchain-based system for private data management. Electronics 10(24), 3131 (2021) P.V. Kakarlapudi, Q.H. Mahmoud, Design and development of a blockchain-based system for private data management. Electronics 10(24), 3131 (2021)
60.
Zurück zum Zitat J. Kim, S. Moon, Blockchain-based edge computing for deep neural network applications, in Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications (INTESA@ESWEEK ) (2018), pp. 53–55 J. Kim, S. Moon, Blockchain-based edge computing for deep neural network applications, in Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications (INTESA@ESWEEK ) (2018), pp. 53–55
61.
Zurück zum Zitat H. Kim, J. Park, M. Bennis, S. Kim, Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2020)CrossRef H. Kim, J. Park, M. Bennis, S. Kim, Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279–1283 (2020)CrossRef
62.
Zurück zum Zitat J. Kang, Z. Xiong, D. Niyato, S. Xie, J. Zhang, Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6(6), 10700–10714 (2019)CrossRef J. Kang, Z. Xiong, D. Niyato, S. Xie, J. Zhang, Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6(6), 10700–10714 (2019)CrossRef
63.
Zurück zum Zitat J.D. Harris, B. Waggoner, Decentralized and collaborative AI on blockchain, in IEEE International Conference on Blockchain (Blockchain) (2019), pp. 368–375 J.D. Harris, B. Waggoner, Decentralized and collaborative AI on blockchain, in IEEE International Conference on Blockchain (Blockchain) (2019), pp. 368–375
64.
Zurück zum Zitat M. Cao, B. Cao, W. Hong, Z. Zhao, X. Bai, L. Zhang, DAG-FL: Direct acyclic graph-based blockchain empowers on-device federated learning, in IEEE International Conference on Communications, Montreal (ICC) (2021), pp. 1–6 M. Cao, B. Cao, W. Hong, Z. Zhao, X. Bai, L. Zhang, DAG-FL: Direct acyclic graph-based blockchain empowers on-device federated learning, in IEEE International Conference on Communications, Montreal (ICC) (2021), pp. 1–6
65.
Zurück zum Zitat Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Low-latency federated learning and blockchain for edge association in digital twin empowered 6g networks. IEEE Trans. Ind. Inf. 17(7), 5098–5107 (2021)CrossRef Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Low-latency federated learning and blockchain for edge association in digital twin empowered 6g networks. IEEE Trans. Ind. Inf. 17(7), 5098–5107 (2021)CrossRef
66.
Zurück zum Zitat R. Doku, D.B. Rawat, IFLBC: On the edge intelligence using federated learning blockchain network, in 6th IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) (2020), pp. 221–226 R. Doku, D.B. Rawat, IFLBC: On the edge intelligence using federated learning blockchain network, in 6th IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) (2020), pp. 221–226
67.
Zurück zum Zitat U. Majeed, C.S. Hong, Flchain: Federated learning via mec-enabled blockchain network, in 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) (2019), pp. 1–4 U. Majeed, C.S. Hong, Flchain: Federated learning via mec-enabled blockchain network, in 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) (2019), pp. 1–4
68.
Zurück zum Zitat Y. Qu, L. Gao, T.H. Luan, Y. Xiang, S. Yu, B. Li, G. Zheng, Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet Things J. 7(6), 5171–5183 (2020)CrossRef Y. Qu, L. Gao, T.H. Luan, Y. Xiang, S. Yu, B. Li, G. Zheng, Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet Things J. 7(6), 5171–5183 (2020)CrossRef
69.
Zurück zum Zitat L. Lyu, J. Yu, K. Nandakumar, Y. Li, X. Ma, J. Jin, H. Yu, K.S. Ng, Towards fair and privacy-preserving federated deep models. IEEE Trans. Parallel Distributed Syst. 31(11), 2524–2541 (2020)CrossRef L. Lyu, J. Yu, K. Nandakumar, Y. Li, X. Ma, J. Jin, H. Yu, K.S. Ng, Towards fair and privacy-preserving federated deep models. IEEE Trans. Parallel Distributed Syst. 31(11), 2524–2541 (2020)CrossRef
70.
Zurück zum Zitat H. Kim, S. Kim, J.Y. Hwang, C. Seo, Efficient privacy-preserving machine learning for blockchain network. IEEE Access 7, 136481–136495 (2019)CrossRef H. Kim, S. Kim, J.Y. Hwang, C. Seo, Efficient privacy-preserving machine learning for blockchain network. IEEE Access 7, 136481–136495 (2019)CrossRef
71.
Zurück zum Zitat S. Rathore, J.H. Park, A blockchain-based deep learning approach for cyber security in next generation industrial cyber-physical systems. IEEE Trans. Ind. Inf. 17(8), 5522–5532 (2021)CrossRef S. Rathore, J.H. Park, A blockchain-based deep learning approach for cyber security in next generation industrial cyber-physical systems. IEEE Trans. Ind. Inf. 17(8), 5522–5532 (2021)CrossRef
72.
Zurück zum Zitat M. Al-Quraan, L.S. Mohjazi, L. Bariah, A. Centeno, A. Zoha, S. Muhaidat, M. Debbah, M.A. Imran, Edge-native intelligence for 6g communications driven by federated learning: A survey of trends and challenges (2021). Preprint arXiv: 2111.07392 M. Al-Quraan, L.S. Mohjazi, L. Bariah, A. Centeno, A. Zoha, S. Muhaidat, M. Debbah, M.A. Imran, Edge-native intelligence for 6g communications driven by federated learning: A survey of trends and challenges (2021). Preprint arXiv: 2111.07392
73.
Zurück zum Zitat J. Kang, Z. Xiong, D. Niyato, Y. Zou, Y. Zhang, M. Guizani, Reliable federated learning for mobile networks. IEEE Wirel. Commun. 27(2), 72–80 (2020)CrossRef J. Kang, Z. Xiong, D. Niyato, Y. Zou, Y. Zhang, M. Guizani, Reliable federated learning for mobile networks. IEEE Wirel. Commun. 27(2), 72–80 (2020)CrossRef
74.
Zurück zum Zitat Y. Zhao, J. Zhao, L. Jiang, R. Tan, D. Niyato, Z. Li, L. Lyu, Y. Liu, Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet Things J. 8(3), 1817–1829 (2021)CrossRef Y. Zhao, J. Zhao, L. Jiang, R. Tan, D. Niyato, Z. Li, L. Lyu, Y. Liu, Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet Things J. 8(3), 1817–1829 (2021)CrossRef
75.
Zurück zum Zitat Y. Liu, J. Peng, J. Kang, A.M. Iliyasu, D. Niyato, A.A.A. El-Latif, A secure federated learning framework for 5g networks. IEEE Wirel. Commun. 27(4), 24–31 (2020)CrossRef Y. Liu, J. Peng, J. Kang, A.M. Iliyasu, D. Niyato, A.A.A. El-Latif, A secure federated learning framework for 5g networks. IEEE Wirel. Commun. 27(4), 24–31 (2020)CrossRef
76.
Zurück zum Zitat Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans. Veh. Technol. 694, 4298–4311 (2020)CrossRef Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans. Veh. Technol. 694, 4298–4311 (2020)CrossRef
77.
Zurück zum Zitat J. Li, J. Wu, J. Li, A.K. Bashir, M.J. Piran, A. Anjum, Blockchain-based trust edge knowledge inference of multi-robot systems for collaborative tasks. IEEE Commun. Mag. 59(7), 94–100 (2021)CrossRef J. Li, J. Wu, J. Li, A.K. Bashir, M.J. Piran, A. Anjum, Blockchain-based trust edge knowledge inference of multi-robot systems for collaborative tasks. IEEE Commun. Mag. 59(7), 94–100 (2021)CrossRef
78.
Zurück zum Zitat X. Jiang, F.R. Yu, T. Song, V.C.M. Leung, Edge intelligence for object detection in blockchain-based internet of vehicles: convergence of symbolic and connectionist AI. IEEE Wirel. Commun. 28(4), 49–55 (2021)CrossRef X. Jiang, F.R. Yu, T. Song, V.C.M. Leung, Edge intelligence for object detection in blockchain-based internet of vehicles: convergence of symbolic and connectionist AI. IEEE Wirel. Commun. 28(4), 49–55 (2021)CrossRef
Metadaten
Titel
Blockchain Driven Edge Intelligence
verfasst von
Xiaofei Wang
Chao Qiu
Xiaoxu Ren
Zehui Xiong
Victor C. M. Leung
Dusit Niyato
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-10186-1_4

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