Overview of Edge Intelligence and Blockchain

  • Chapter
  • First Online:
Integrating Edge Intelligence and Blockchain

Part of the book series: Wireless Networks ((WN))

  • 209 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Google Scholar 

  5. IEC, Edge intelligence (white paper). [Online]. Available: https://basecamp.iec.ch/download/iec-white-paper-edge-intelligence-en/

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  9. D. Xu, T. Li, Y. Li, X. Su, S. Tarkoma, P. Hui, A survey on edge intelligence (2020). Preprint ar**v: 2003.12172

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Google Scholar 

  15. Nvidia turing gpu architecture. [Online]. Available: https://www.nvidia.com/en-us/geforce/turing/

  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

    Google Scholar 

  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

    Google Scholar 

  18. Huawei atlas: Ai computing solution. [Online]. Available: https://e.huawei.com/en/products/servers/ascend

  19. Latest jetson products. [Online]. Available: https://developer.nvidia.com/buy-jetson

  20. D. Bernstein, Containers and cloud: from LXC to docker to kubernetes. IEEE Cloud Comput. 1(3), 81–84 (2014)

    Article  Google Scholar 

  21. Openedge. [Online]. Available: https://www.progress.com/openedge

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  33. M. Blot, D. Picard, M. Cord, N. Thome, Gossip training for deep learning (2016). Preprint ar**v:1611.09726

    Google Scholar 

  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 ar**v: 1704.04861

    Google Scholar 

  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 ar**v: 1602.07360

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Google Scholar 

  40. Y. Cheng, D. Wang, P. Zhou, T. Zhang, A survey of model compression and acceleration for deep neural networks (2017). Preprint ar**v: 1710.09282

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  46. S. Jain, J. Jiang, Y. Shu, G. Ananthanarayanan, J. Gonzalez, ReXCam: Resource-efficient, cross-camera video analytics at enterprise scale (2018). Preprint ar**v: 1811.01268

    Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  49. Amazon. (2019) Amazon echo. [Online]. Available: https://developer.amazon.com/echo

  50. Google. (2019) Google assistant. [Online]. Available: https://assistant.google.com/platforms/speakers/

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  56. W. Shi, S. Dustdar, The promise of edge computing. Computer 49(5), 78–81 (2016)

    Article  Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  66. Y. Lu, The blockchain: state-of-the-art and research challenges. J.Ind. Inf. Integr. 15, 80–90 (2019)

    Google Scholar 

  67. S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system. [Online]. Available: https://bitcoin.org/en/bitcoin-paper

  68. D.D. Wood, Ethereum: A secure decentralised generalised transaction ledger (2014). [Online]. Available: https://www.win.tue.nl/mholende/seminar/references/ethereum

  69. C. Cachin, Architecture of the hyperledger blockchain fabric, in Workshop on Distributed Cryptocurrencies and Consensus Ledgers (2016)

    Google Scholar 

  70. Z. Zheng, S. **e, H. Dai, X. Chen, H. Wang, An overview of blockchain technology: Architecture, consensus, and future trends, in IEEE BigData Congress (2017), pp. 557–564

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  74. V. Buterin, A next generation smart contract and decentralized application platform (2014) [Online]. Available: https://github.com/ethereum/wiki/wiki/White-Paper

  75. M. Castro, B. Liskov, Practical byzantine fault tolerance and proactive recovery. ACM Trans. Comput. Syst. 20(4), 398–461 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  79. F. Tschorsch, B. Scheuermann, Bitcoin and Beyond: a technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutorials 18(3), 2084–2123 (2016)

    Article  Google Scholar 

  80. L. Cong, Y. Li, N. Wang, Tokenomics: Dynamic adoption and valuation. Review of Financial Studies 34(3), 1105–1155 (2021)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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)

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

  90. A. Kusiak, Smart manufacturing. Int. J. Produ. Res. 56(1–2), 508-517 (2018)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wang, X., Qiu, C., Ren, X., **ong, Z., Leung, V.C.M., Niyato, D. (2022). Overview of Edge Intelligence and Blockchain. In: Integrating Edge Intelligence and Blockchain. Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-10186-1_2

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10185-4

  • Online ISBN: 978-3-031-10186-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Navigation