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2022 | OriginalPaper | Buchkapitel

1. Introduction

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

Along with the wave of informatization technology, a booming era of artificial intelligence (AI) has emerged. In specific, with the proliferation of wireless communication, immense volumes of data are generated by mega-scale terminal devices instead of traditional cloud datacenters. According to the prediction by Ericsson (IoT connections outlook: NB-IoT and CAT-M technologies will account for close to 45 percent of cellular IoT connections in 2024. [Online]. Available: https://​www.​ericsson.​com/​en/​mobilityreport/​reports/​june-2019/​iot-connections-outlook ) and international data corporation (IDC) (Ericsson, Cisco Annual Internet Report (2018–2023). er[Online]. Available: https://​www.​cisco.​com/​c/​en/​us/​solutions/​collateral/​executive-perspectives/​annual-internet-report/​white-paper-c11-741490.​pdf ), internet of things (IoT) devices will generate 45% of the 40 zettabytes (ZB) global Internet data in 2024, while there will be 5.3 billion total Internet users and 29.3 billion networked devices by 2023. Nevertheless, global devices transferring extremely vast data to cloud datacenters will demand high bandwidth and powerful computational resources (Heintz et al. (Optimizing grouped aggregation in geo-distributed streaming analytics, in Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing, HPDC, ed. by T. Kielmann, D. Hildebrand, M. Taufer (2015), pp. 133–144)), thus creating a bottleneck on the restricted network transmission capabilities, computing power of computing infrastructures, strict delay requirements, etc. Edge intelligence (EI), as a complementary processing architecture by combining edge computing (EC) (Shi et al. (IEEE Int Things J 3(5):637–646, 2016)) and AI, pushes the AI frontier from the cloud to the network edge to open the path for low latency and critical-computation (Li et al. (Edge intelligence: On-demand deep learning model co-inference with device-edge synergy, in Proceedings of the 2018 Workshop on Mobile Edge Communications (MECOMM@SIGCOMM) (2018), pp. 31–36) ).

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Literatur
1.
Zurück zum Zitat Z. Chang, S. Liu, X. Xiong, Z. Cai, G. Tu, A survey of recent advances in edge-computing-powered artificial intelligence of things. IEEE Int Things J. 8(18), 13849–13875 (2018)CrossRef Z. Chang, S. Liu, X. Xiong, Z. Cai, G. Tu, A survey of recent advances in edge-computing-powered artificial intelligence of things. IEEE Int Things J. 8(18), 13849–13875 (2018)CrossRef
3.
Zurück zum Zitat Z. Zhao, K.M. Barijough, A. Gerstlauer, Deepthings: Distributed adaptive deep learning inference on resource-constrained IoT edge clusters. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37(11), 2348–2359 (2018)CrossRef Z. Zhao, K.M. Barijough, A. Gerstlauer, Deepthings: Distributed adaptive deep learning inference on resource-constrained IoT edge clusters. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37(11), 2348–2359 (2018)CrossRef
4.
Zurück zum Zitat Y. Liu, C. Yang, L. Jiang, S. Xie, Y. Zhang, Intelligent edge computing for iot-based energy management in smart cities. IEEE Netw. 33(2), 111–117 (2019)CrossRef Y. Liu, C. Yang, L. Jiang, S. Xie, Y. Zhang, Intelligent edge computing for iot-based energy management in smart cities. IEEE Netw. 33(2), 111–117 (2019)CrossRef
5.
Zurück zum Zitat S. Zhang, W. Li, Y. Wu, P. Watson, A.Y. Zomaya, Enabling edge intelligence for activity recognition in smart homes, in 15th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2018, Chengdu, October 9–12, 2018. (IEEE Computer Society, Washington, 2018), pp. 228–236 S. Zhang, W. Li, Y. Wu, P. Watson, A.Y. Zomaya, Enabling edge intelligence for activity recognition in smart homes, in 15th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2018, Chengdu, October 9–12, 2018. (IEEE Computer Society, Washington, 2018), pp. 228–236
8.
Zurück zum Zitat R.C. Merkle, A digital signature based on a conventional encryption function, in A Conference on the Theory and Applications of Cryptographic Techniques (CRYPTO), vol. 293 (1987), pp. 369–378 R.C. Merkle, A digital signature based on a conventional encryption function, in A Conference on the Theory and Applications of Cryptographic Techniques (CRYPTO), vol. 293 (1987), pp. 369–378
10.
Zurück zum Zitat M.S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, M.H. Rehmani, Applications of blockchains in the internet of things: a comprehensive survey. IEEE Commun. Surv. Tutorials 21(2), 1676–1717 (2019)CrossRef M.S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, M.H. Rehmani, Applications of blockchains in the internet of things: a comprehensive survey. IEEE Commun. Surv. Tutorials 21(2), 1676–1717 (2019)CrossRef
11.
Zurück zum Zitat M. Vukolic, The quest for scalable blockchain fabric: Proof-of-work vs. BFT replication, in Open Problems in Network Security - IFIP WG 11.4 International Workshop (iNetSec ), vol. 9591 (2015), pp. 112–125 M. Vukolic, The quest for scalable blockchain fabric: Proof-of-work vs. BFT replication, in Open Problems in Network Security - IFIP WG 11.4 International Workshop (iNetSec ), vol. 9591 (2015), pp. 112–125
12.
Zurück zum Zitat D.C. Nguyen, P.N. Pathirana, M. Ding, A. Seneviratne, Blockchain for 5g and beyond networks: a state of the art survey. J. Netw. Comput. Appl. 166, 102693 (2020)CrossRef D.C. Nguyen, P.N. Pathirana, M. Ding, A. Seneviratne, Blockchain for 5g and beyond networks: a state of the art survey. J. Netw. Comput. Appl. 166, 102693 (2020)CrossRef
13.
Zurück zum Zitat M.B. Mollah, J. Zhao, D. Niyato, Y.L. Guan, C. Yuen, S. Sun, K. Lam, L.H. Koh, Blockchain for the internet of vehicles towards intelligent transportation systems: a survey. IEEE Internet Things J. 8(6), 4157–4185 (2021)CrossRef M.B. Mollah, J. Zhao, D. Niyato, Y.L. Guan, C. Yuen, S. Sun, K. Lam, L.H. Koh, Blockchain for the internet of vehicles towards intelligent transportation systems: a survey. IEEE Internet Things J. 8(6), 4157–4185 (2021)CrossRef
14.
Zurück zum Zitat D.C. Nguyen, P.N. Pathirana, M. Ding, A. Seneviratne, Integration of blockchain and cloud of things: architecture, applications and challenges. IEEE Commun. Surv. Tutorials 22(4), 2521–2549 (2020)CrossRef D.C. Nguyen, P.N. Pathirana, M. Ding, A. Seneviratne, Integration of blockchain and cloud of things: architecture, applications and challenges. IEEE Commun. Surv. Tutorials 22(4), 2521–2549 (2020)CrossRef
15.
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
16.
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
17.
Zurück zum Zitat R. Wang, M. Luo, Y. Wen, L. Wang, K.R. Choo, D. He, The applications of blockchain in artificial intelligence. Secur. Commun. Netw. 2021, 6126247:1–6126247:16 (2021) R. Wang, M. Luo, Y. Wen, L. Wang, K.R. Choo, D. He, The applications of blockchain in artificial intelligence. Secur. Commun. Netw. 2021, 6126247:1–6126247:16 (2021)
18.
Zurück zum Zitat D.C. Nguyen, M. Ding, P.N. Pathirana, A. Seneviratne, Blockchain and ai-based solutions to combat coronavirus (covid-19)-like epidemics: a survey. IEEE Access 9, 95730–95753 (2021)CrossRef D.C. Nguyen, M. Ding, P.N. Pathirana, A. Seneviratne, Blockchain and ai-based solutions to combat coronavirus (covid-19)-like epidemics: a survey. IEEE Access 9, 95730–95753 (2021)CrossRef
19.
Zurück zum Zitat Y. Liu, F.R. Yu, X. Li, H. Ji, V.C.M. Leung, Blockchain and machine learning for communications and networking systems. IEEE Commun. Surv. Tutorials 22(2), 1392–1431 (2020)CrossRef Y. Liu, F.R. Yu, X. Li, H. Ji, V.C.M. Leung, Blockchain and machine learning for communications and networking systems. IEEE Commun. Surv. Tutorials 22(2), 1392–1431 (2020)CrossRef
20.
Zurück zum Zitat Y. Wu, Z. Wang, Y. Ma, V.C.M. Leung, Deep reinforcement learning for blockchain in industrial iot: a survey. Comput. Netw 191, 108004 (2021)CrossRef Y. Wu, Z. Wang, Y. Ma, V.C.M. Leung, Deep reinforcement learning for blockchain in industrial iot: a survey. Comput. Netw 191, 108004 (2021)CrossRef
Metadaten
Titel
Introduction
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_1

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