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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

2. Overview of Edge Intelligence and Blockchain

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

In this chapter, we introduce and explain the basic aspects in EI and BC such as the concept, architecture, background, characteristics, classification, working principle, development, and application. From these aspects, it can be conscious of the motivation and necessity of integration of EI and BC.

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Vorheriges Kapitel Introduction
Nächstes Kapitel Motivations for Integrating Edge Intelligence with Blockchain
Literatur
1.
R. Tiwari, N. Sharma, I. Kaushik, A. Tiwari and B. Bhushan, Evolution of IoT and data analytics using deep learning, in 2019 International Conference on Computing, Communication, and Intelligent Systems(ICCCIS) (2019), pp. 418–423
2.
V. Mittal, A. Tyagi, B. Bhushan, Smart surveillance systems with edge intelligence: convergence of deep learning and edge computing, in Proceedings of the International Conference on Innovative Computing & Communications (ICICC) (2020), pp. 1–5
3.
A. Pazienza, G. Polimeno, F. Vitulano, Y. Maruccia, Towards a digital future: An innovative semantic IoT integrated platform for industry 4.0 healthcare and territorial control, in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC) (2019), pp. 587–592
4.
Y. Zhang, B. Li, Y. Tan, Making AI available for everyone at anywhere: a survey about edge intelligence. J. Phy. Conf. Ser. 1757(1), 012076 (2021)
5.
6.
X. Zhang, Y. Wang, S. Lu, L. Liu, L. Xu, W. Shi, Openei: An open framework for edge intelligence, in 39th IEEE International Conference on Distributed Computing Systems (ICDCS) (2019), pp. 1840–1851
7.
X. Wang, Y. Han, V.C.M. Leung, D. Niyato, X. Yan, X. Chen, Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun. Surv. Tutorials 22(2), 869–904 (2020)CrossRef
8.
Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, J. Zhang, Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8), 1738–1762 (2019)CrossRef
9.
D. Xu, T. Li, Y. Li, X. Su, S. Tarkoma, P. Hui, A survey on edge intelligence (2020). Preprint arXiv: 2003.12172
10.
A. Fayez, A. Mohammed, A. Elshakankiry, A proactive caching and offloading technique using machine learning for mobile edge computing users. Comput. Commun. 181, 224–235, (2022)CrossRef
11.
S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, A.Y. Zomaya, Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Int. Things J. 7(8), 7457–7469 (2020)CrossRef
12.
S. Wang, T. Tuor, T. Salonidis, K.K. Leung, C. Makaya, T. He, K. Chan, When edge meets learning: Adaptive control for resource-constrained distributed machine learning, in 2018 IEEE Conference on Computer Communications (INFOCOM) (2018), pp. 63–71
13.
T. Nishio, R. Yonetani, Client selection for federated learning with heterogeneous resources in mobile edge, in 2019 IEEE International Conference on Communications (ICC) (2019), pp. 1–7
14.
F. Jalali, K. Hinton, R. Ayre, T. Alpcan, and RS. Tucker, Fog computing may help to save energy in cloud computing. IEEE J. Sel. Area. Comm. 34(5), 1728–1739 (2016)
15.
16.
S. Jiang, D. He, C. Yang, C. Xu, G. Luo, Y. Chen, Y. Liu, J. Jiang, Accelerating mobile applications at the network edge with software-programmable fpgas, in 2018 IEEE Conference on Computer Communications (INFOCOM) (2018), pp. 55–62
17.
N.P. Jouppi, C. Young, N. Patil, D.A. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, In-datacenter performance analysis of a tensor processing unit, in Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA) (2017), pp. 1–12
18.
19.
20.
D. Bernstein, Containers and cloud: from LXC to docker to kubernetes. IEEE Cloud Comput. 1(3), 81–84 (2014)CrossRef
21.
22.
M.S.H. Abad, E. Ozfatura, D. Gündüz, Ö. Erçetin, Hierarchical federated learning ACROSS heterogeneous cellular networks, in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020), pp. 8866–8870
23.
Z. Chang, L. Lei, Z. Zhou, S. Mao, T. Ristaniemi, Learn to cache: machine learning for network edge caching in the big data era. IEEE Wirel. Commun. 25(3), 28–35 (2018)CrossRef
24.
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, M. Chen, In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw. 33(5), 156–165 (2019)CrossRef
25.
A. Sadeghi, F. Sheikholeslami, G.B. Giannakis, Optimal and scalable caching for 5g using reinforcement learning of space-time popularities. IEEE J. Sel. Top. Signal Process. 12(1), 180–190 (2018)CrossRef
26.
A.N. Elmachtoub, J.C.N. Liang, R. McNellis, Decision trees for decision-making under the predict-then-optimize framework, in Proceedings of the 37th International Conference on Machine Learning, (ICML) (2020), pp. 2858–2867
27.
M.M. Bukhari, T.M. Ghazal, S. Abbas, M.A. Khan, U. Farooq, H. Wahbah, M. Ahmad, M.A. Khan, An intelligent proposed model for task offloading in fog-cloud collaboration using logistics regression. Comput. Intell. Neurosci. 2022, 3606068:1–3606068:25 (2022)
28.
L. Huang, S. Bi, Y.A. Zhang, Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks. IEEE Trans. Mob. Comput 19(11), 2581–2593 (2020)CrossRef
29.
L. Shao, F. Zhu, X. Li, Transfer learning for visual categorization: a survey. IEEE Trans. Neural Netw. Learn. Syst 26(5), 1019–1034 (2015)MathSciNetCrossRef
30.
N.H. Tran, W. Bao, A.Y. Zomaya, M.N.H. Nguyen, C.S. Hong, Federated learning over wireless networks: Optimization model design and analysis, in 2019 IEEE Conference on Computer Communications (INFOCOM) (2019), pp. 1387–1395
31.
W.Y.B. Lim, N.C. Luong, D.T. Hoang, Y. Jiao, Y. Liang, Q. Yang, D. Niyato, C. Miao, Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun. Surv. Tutorials 22(3), 2031–2063 (2020)CrossRef
32.
D. Narayanan, A. Harlap, A. Phanishayee, V. Seshadri, N.R. Devanur, G.R. Ganger, P.B. Gibbons, M. Zaharia, Pipedream: Generalized pipeline parallelism for DNN training, in Proceedings of the 27th ACM Symposium on Operating Systems Principles (SOSP), ed. by T. Brecht, C. Williamson (2019), pp. 1–15
33.
M. Blot, D. Picard, M. Cord, N. Thome, Gossip training for deep learning (2016). Preprint arXiv:1611.09726
34.
A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: Efficient convolutional neural networks for mobile vision applications (2017). Preprint arXiv: 1704.04861
35.
F.N. Iandola, M.W. Moskewicz, K. Ashraf, S. Han, W.J. Dally, K. Keutzer, Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size (2016). Preprint arXiv: 1602.07360
36.
X. Zhang, X. Zhou, M. Lin, J. Sun, Shufflenet: An extremely efficient convolutional neural network for mobile devices, in 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp. 6848–6856
37.
A. Kusupati, M. Singh, K. Bhatia, A. Kumar, P. Jain, M. Varma, Fastgrnn: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network, in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems (NeurIPS) (2018), pp. 9031–9042
38.
S. Yao, Y. Zhao, A. Zhang, L. Su, T.F. Abdelzaher, Deepiot: Compressing deep neural network structures for sensing systems with a compressor-critic framework, in Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems (SenSys) (2017), pp. 4:1–4:14
39.
W.J.D. Song Han, H. Mao, Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. Fiber 56(4), 3–7 (2015)
40.
Y. Cheng, D. Wang, P. Zhou, T. Zhang, A survey of model compression and acceleration for deep neural networks (2017). Preprint arXiv: 1710.09282
41.
S. Teerapittayanon, B. McDanel, H.T. Kung, Branchynet: Fast inference via early exiting from deep neural networks, in 23rd International Conference on Pattern Recognition (ICPR) (2016), pp. 2464–2469
42.
C. Lo, Y. Su, C. Lee, S. Chang, A dynamic deep neural network design for efficient workload allocation in edge computing, in 2017 IEEE International Conference on Computer Design (ICCD) (2017), pp. 273–280
43.
D. Stamoulis, T.R. Chin, A.K. Prakash, H. Fang, S. Sajja, M. Bognar, D. Marculescu, Designing adaptive neural networks for energy-constrained image classification, in Proceedings of the International Conference on Computer-Aided Design (ICCAD) (2018), p. 23
44.
J. Mao, Z. Yang, W. Wen, C. Wu, L. Song, K.W. Nixon, X. Chen, H. Li, Y. Chen, MeDNN: A distributed mobile system with enhanced partition and deployment for large-scale DNNs, in 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) (2017), pp. 751–756
45.
C. Hu, W. Bao, D. Wang, F. Liu, Dynamic adaptive DNN surgery for inference acceleration on the edge, in 2019 IEEE Conference on Computer Communications (INFOCOM) (2019), pp. 1423–1431
46.
S. Jain, J. Jiang, Y. Shu, G. Ananthanarayanan, J. Gonzalez, ReXCam: Resource-efficient, cross-camera video analytics at enterprise scale (2018). Preprint arXiv: 1811.01268
47.
J. Wang, Z. Feng, Z. Chen, S.A. George, M. Bala, P. Pillai, S. Yang, M. Satyanarayanan, Bandwidth-efficient live video analytics for drones via edge computing, in 2018 IEEE/ACM Symposium on Edge Computing (SEC) 2018, pp. 159–173
48.
D. Sun, S. Xue, H. Wu, A data stream cleaning system using edge intelligence for smart city industrial environment. IEEE Trans. Ind. Inf. 18(2), 1–1 (2022)CrossRef
49.
50.
51.
F. Lin, Y. Zhou, X. An, I. You, K.-K.R. Choo, Fair resource allocation in an intrusion-detection system for edge computing: ensuring the security of internet of things devices. IEEE Consum. Electron. Mag. 7(6), 45–50 (2018)CrossRef
52.
L. Cesarano, A. Croce, L.D.C. Martins, D. Tarchi, A.A. Juan, A real-time energy-saving mechanism in internet of vehicles systems. IEEE Access 9, 157842–157858 (2021)CrossRef
53.
Y. Zhang, C. Wu, R. Roman, H. Liu, Guest editorial introduction of the special issue on edge intelligence for internet of vehicles. IEEE Trans. Intell. Transp. Syst 22(4), 2178–2182 (2021)CrossRef
54.
S.C. Lin, Y. Zhang, C.H. Hsu, M. Skach, M.E. Haque, L. Tang, J. Mars, The architectural implications of autonomous driving: Constraints and acceleration, in Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems(ASPLOS) (2018), pp. 751–766
55.
Y. Wang, S. Liu, X. Wu, W. Shi, CAVBench: A benchmark suite for connected and autonomous vehicles, in 2018 IEEE/ACM Symposium on Edge Computing (SEC) (2018), pp. 30–42
56.
W. Shi, S. Dustdar, The promise of edge computing. Computer 49(5), 78–81 (2016)CrossRef
57.
R. Aggarwal, A. Singhal, Augmented reality and its effect on our life, in Proceedings of the 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (2019), pp. 510–515
58.
H.R. Hasan, K. Salah, R. Jayaraman, I. Yaqoob, M. Omar, S. Ellahham, Blockchain-enabled telehealth services using smart contracts. IEEE Access 9, 151944–151959 (2021)CrossRef
59.
J.A. Rincon, A. Costa, P. Novais, V. Julian, C. Carrascosa, Using non-invasive wearables for detecting emotions with intelligent agents, in International Joint Conference SOCO’16-CISIS’16- ICEUTE’16 (2016), pp. 73–84
60.
M. Ryu, J. Yun, T. Miao, I.Y. Ahn, S.C. Choi, J. Kim, Design and implementation of a connected farm for smart farming system, in IEEE SENSORS - Proceedings (2015), pp. 1–4
61.
P.K. Sethy, N.K. Barpanda, A.K. Rath, S.K. Behera, Nitrogen deficiency prediction of rice crop based on convolutional neural network. J. Ambient. Intell. Humanized Comput. 11(11), 5703–5711 (2020)CrossRef
62.
C. Liu, C. Xu, S. Liu, D. Xu, X. Yu, Study on identification of rice false smut based on CNN in natural environment, in Proceedings of the 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (2017), pp. 1–5
63.
S.Y. Zhang, T. Fei, Y.H. Ran, Diagnosis of heavy metal cross contamination in leaf of rice based on hyperspectral image: A greenhouse experiment, in Proceedings of the IEEE International Conference on Advanced Manufacturing (ICAM) (2018), pp. 159–162
64.
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
65.
M. Belotti, N. Bozic, G. Pujolle, S. Secci, A vademecum on blockchain technologies: when, which, and how. IEEE Commun. Surv. Tutorials 21(4), 3796–3838 (2019)CrossRef
66.
Y. Lu, The blockchain: state-of-the-art and research challenges. J.Ind. Inf. Integr. 15, 80–90 (2019)
67.
68.
69.
C. Cachin, Architecture of the hyperledger blockchain fabric, in Workshop on Distributed Cryptocurrencies and Consensus Ledgers (2016)
70.
Z. Zheng, S. Xie, H. Dai, X. Chen, H. Wang, An overview of blockchain technology: Architecture, consensus, and future trends, in IEEE BigData Congress (2017), pp. 557–564
71.
Y. Yuan, F. Wang, Towards blockchain-based intelligent transportation systems, in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) (2016), pp. 2663–2668
72.
G. Cui, K. Shi, Y. Qin, L. Liu, B. Qi, B. Li, Application of block chain in multi-level demand response reliable mechanism, in (ICIM) (2017), pp. 337–341
73.
X. Xu, I. Weber, M. Staples, L. Zhu, J. Bosch, L. Bass, C. Pautasso, P. Rimba, A taxonomy of blockchain-based systems for architecture design, in (ICSA) (2017), pp. 243–252
74.
75.
M. Castro, B. Liskov, Practical byzantine fault tolerance and proactive recovery. ACM Trans. Comput. Syst. 20(4), 398–461 (2002)CrossRef
76.
T.M. Fernandez-Carames, P. Fraga-Lamas, A review on the use of blockchain for the internet of things. IEEE Access 6, 32979–33001 (2018)CrossRef
77.
A.E. Kosba, A. Miller, E. Shi, Z. Wen, C. Papamanthou, Hawk: The blockchain model of cryptography and privacy-preserving smart contracts, in IEEE Symposium on Security and Privacy (SP) (2016), pp. 839–858
78.
M. Wu, K. Wang, X. Cai, S. Guo, M. Guo, C. Rong, A comprehensive survey of blockchain: from theory to IoT applications and beyond. IEEE Int. Things J. 6(5), 8114–8154 (2019)CrossRef
79.
F. Tschorsch, B. Scheuermann, Bitcoin and Beyond: a technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutorials 18(3), 2084–2123 (2016)CrossRef
80.
L. Cong, Y. Li, N. Wang, Tokenomics: Dynamic adoption and valuation. Review of Financial Studies 34(3), 1105–1155 (2021)CrossRef
81.
E. Altman, A. Reiffers, D.S. Menasché, M. Datar, S. Dhamal, C. Touati, Mining competition in a multi-cryptocurrency ecosystem at the network edge: a congestion game approach. SIGMETRICS Perform. Evaluation Rev. 46(3), 114–117 (2018)CrossRef
82.
G. Li, Q. Zhao, M. Song, D. Du, J. Yuan, X. Chen, H. Liang, Predicting global computing power of blockchain using cryptocurrency prices, in 2019 International Conference on Machine Learning and Cybernetics (ICMLC) (2019), pp. 1–6
83.
J. Xu, K. Xue, S. Li, H. Tian, J. Hong, P. Hong, N. Yu, Healthchain: a blockchain-based privacy preserving scheme for large-scale health data. IEEE Int. Things J. 6(5), 8770–8781 (2019)CrossRef
84.
V. Ramani, T. Kumar, A. Bracken, M. Liyanage, M. Ylianttila, Secure and efficient data accessibility in blockchain based healthcare systems, in 2018 IEEE Global Communications Conference (GLOBECOM) (2018), pp. 206–212
85.
S. Jiang, J. Cao, H. Wu, Y. Yang, M. Ma, J. He, BlocHIE: A blockchain-based platform for healthcare information exchange, in 2018 IEEE International Conference on Smart Computing (SMARTCOMP) (2018), pp. 49–56
86.
E.Y. Daraghmi, Y.-A. Daraghmi, S.-M. Yuan, MedChain: A design of blockchain-based system for medical records access and permissions management. IEEE Access 7, 164595–164613 (2019)CrossRef
87.
A. Azaria, A. Ekblaw, T. Vieira, A. Lippman, MedRec: Using blockchain for medical data access and permission management, in 2016 2nd International Conference on Open and Big Data (OBD) (2016), pp. 25–30
88.
J. Liu, X. Li, L. Ye, H. Zhang, X. Du, M. Guizani, BPDS: A blockchain based privacy-preserving data sharing for electronic medical records, in 2018 IEEE Global Communications Conference (GLOBECOM) (2018), pp. 1–6
89.
J. Vora, A. Nayyar, S. Tanwar, S. Tyagi, N. Kumar, M.S. Obaidat, J.J.P.C. Rodrigues, BHEEM: A blockchain-based framework for securing electronic health records, in 2018 IEEE Globecom Workshops (2018), pp. 1–6
90.
A. Kusiak, Smart manufacturing. Int. J. Produ. Res. 56(1–2), 508-517 (2018)CrossRef
91.
J. Leng, D. Yan, Q. Liu, K. Xu, J. Leon Zhao, R. Shi, L. Wei, D. Zhang, X. Chen, ManuChain: Combining permissioned blockchain with a holistic optimization model as bi-level intelligence for smart manufacturing. IEEE Trans. Syst. Man Cybern. Syst. 50(1), 182–192 (2020)CrossRef
92.
Z. Li, A.V. Barenji, G.Q. Huang, Toward a blockchain cloud manufacturing system as a peer to peer distributed network platform. Rob. Comput. Integr. Manuf. 54, 133–144 (2018)CrossRef
93.
C.K.M. Lee, Y. Huo, S. Zhang, K.K.H. Ng, Design of a smart manufacturing system with the application of multi-access edge computing and blockchain technology. IEEE Access 8, 28659–28667 (2020)CrossRef
94.
M.E. Peck, D. Wagman, Energy trading for fun and profit buy your neighbor’s rooftop solar power or sell your own-it’ll all be on a blockchain. IEEE Spectr. 54(10), 56–61 (2017)CrossRef
95.
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
96.
S. Wang, A.F. Taha, J. Wang, K. Kvaternik, A. Hahn, Energy crowdsourcing and peer-to-peer energy trading in blockchain-enabled smart grids. IEEE Trans. Syst. Man Cybern. Syst. 49(8), 1612–1623 (2019)CrossRef
97.
M.A. Ferrag, L.A. Maglaras, DeepCoin: a novel deep learning and blockchain-based energy exchange framework for smart grids. IEEE Trans. Eng. Manag. 67(4), 1285–1297 (2020)CrossRef
98.
K. Gai, Y. Wu, L. Zhu, M. Qiu, M. Shen, Privacy-preserving energy trading using consortium blockchain in smart grid. IEEE Trans. Ind. Inf. 15(6), 3548–3558 (2019)CrossRef
99.
K. Gai, Y. Wu, L. Zhu, L. Xu, Y. Zhang, Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks. IEEE Int. Things J. 6(5), 7992–8004 (2019)CrossRef
Metadaten
Titel
Overview of Edge Intelligence and Blockchain
verfasst von
Xiaofei Wang
Chao Qiu
Xiaoxu Ren
Zehui Xiong
Victor C. M. Leung
Dusit Niyato
Copyright-Jahr
2022
Buch
Integrating Edge Intelligence and Blockchain

Print ISBN: 978-3-031-10185-4

Electronic ISBN: 978-3-031-10186-1

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-031-10186-1_2

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