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A New Human-Centric Computing Age at Edge

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Social Edge Computing

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Abstract

The recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have given rise to a pyramid of intelligent mobile applications such as autonomous driving, smart health monitoring, and virtual reality, which are running on ubiquitous edge devices such as smartphones and vehicles.

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Notes

  1. 1.

    Cisco estimates that nearly 850 ZetaBytes of data will be generated by all people, machines, and things at the network edge by 2021 [12].

References

  1. A. Al-Shuwaili, O. Simeone, Energy-efficient resource allocation for mobile edge computing-based augmented reality applications. IEEE Wirel. Commun. Lett. 6(3), 398–401 (2017)

    Article  Google Scholar 

  2. C.A. Ardagna, M. Cremonini, S. De Capitani di Vimercati, P. Samarati, A privacy-aware access control system. J. Comput. Secur. 16(4), 369–397 (2008)

    Article  Google Scholar 

  3. Aws deeplens. https://aws.amazon.com/deeplens/. Accessed 23 Apr 2019

  4. S. Branson, C. Wah, F. Schroff, B. Babenko, P. Welinder, P. Perona, S. Belongie, Visual recognition with humans in the loop, in European Conference on Computer Vision (Springer, 2010), pp 438–451

    Google Scholar 

  5. Y. Chen, J. Wang, C. Yu, W. Gao, X. Qin, Fedhealth: A federated transfer learning framework for wearable healthcare. Preprint. arXiv:1907.09173 (2019)

    Google Scholar 

  6. M. Duan, Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications. Preprint. arXiv:1907.01132 (2019)

    Google Scholar 

  7. T. Giannetsos, S. Gisdakis, P. Papadimitratos, Trustworthy people-centric sensing: Privacy, security and user incentives road-map, in Ad Hoc Networking Workshop (MED-HOC-NET), 2014 13th Annual Mediterranean (IEEE, 2014), pp 39–46

    Google Scholar 

  8. K. Habak, M. Ammar, K.A. Harras, E. Zegura, Femto clouds: Leveraging mobile devices to provide cloud service at the edge, in 2015 IEEE 8th International Conference on Cloud Computing (CLOUD) (IEEE, 2015), pp. 9–16

    Google Scholar 

  9. K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 1026–1034

    Google Scholar 

  10. Y.C. Hu, M. Patel, D. Sabella, N. Sprecher, V. Young, Mobile edge computing—a key technology towards 5g. ETSI White Paper 11(11), 1–16 (2015)

    Google Scholar 

  11. C.-C. Hung, G. Ananthanarayanan, P. Bodik, L. Golubchik, M. Yu, P. Bahl, M. Philipose, Videoedge: Processing camera streams using hierarchical clusters, in 2018 IEEE/ACM Symposium on Edge Computing (SEC) (IEEE, 2018), pp. 115–131

    Google Scholar 

  12. C.G.C. Index, Forecast and methodology, 2016–2021 white paper. Updated: February, 1, 2018 (2018)

    Google Scholar 

  13. N. Japkowicz, S. Stephen, The class imbalance problem: A systematic study. Intelligent Data Anal. 6(5), 429–449 (2002)

    Article  MATH  Google Scholar 

  14. J. Konečnỳ, H.B. McMahan, F.X. Yu, P. Richtárik, A.T. Suresh, D. Bacon, Federated learning: Strategies for improving communication efficiency. Preprint. arXiv:1610.05492 (2016)

    Google Scholar 

  15. F. Laws, C. Scheible, H. Schütze, Active learning with Amazon mechanical turk, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2011), pp. 1546–1556

    Google Scholar 

  16. E. Li, Z. Zhou, X. Chen, Edge intelligence: On-demand deep learning model co-inference with device-edge synergy, in Proceedings of the 2018 Workshop on Mobile Edge Communications (ACM, 2018), pp. 31–36

    Google Scholar 

  17. H.B. McMahan, E. Moore, D. Ramage, S. Hampson, et al., Communication-efficient learning of deep networks from decentralized data. Preprint. arXiv:1602.05629 (2016)

    Google Scholar 

  18. B. Nushi, E. Kamar, E. Horvitz, D. Kossmann, On human intellect and machine failures: Troubleshooting integrative machine learning systems, in AAAI (2017), pp. 1017–1025

    Google Scholar 

  19. B. Nushi, E. Kamar, E. Horvitz, Towards accountable AI: Hybrid human-machine analyses for characterizing system failure. Preprint. arXiv:1809.07424 (2018)

    Google Scholar 

  20. Y. O’Connor, W. Rowan, L. Lynch, C. Heavin, Privacy by design: informed consent and internet of things for smart health. Procedia Comput. Sci. 113, 653–658 (2017)

    Article  Google Scholar 

  21. Z. Sanaei, S. Abolfazli, A. Gani, R. Buyya, Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Commun. Surv. Tutorials 16(1), 369–392 (2014)

    Article  Google Scholar 

  22. W. Shi, J. Cao, Q. Zhang, Y. Li, L. Xu, Edge computing: Vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  23. A. Sorokin, D. Forsyth, Utility data annotation with Amazon mechanical turk, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW’08 (IEEE, 2008), pp. 1–8

    Google Scholar 

  24. D. Wang, B.K. Szymanski, T. Abdelzaher, H. Ji, L. Kaplan, The age of social sensing. Computer 52(1), 36–45 (2019)

    Article  Google Scholar 

  25. S. Wang, G. Ananthanarayanan, Y. Zeng, N. Goel, A. Pathania, T. Mitra, High-throughput CNN inference on embedded arm big. little multi-core processors. Preprint. arXiv:1903.05898 (2019)

    Google Scholar 

  26. 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 

  27. D.Y. Zhang, D. Wang, An integrated top-down and bottom-up task allocation approach in social sensing based edge computing systems, in IEEE INFOCOM 2019-IEEE Conference on Computer Communications (IEEE, 2019), pp. 766–774

    Google Scholar 

  28. Q. Zhang, X. Zhang, Q. Zhang, W. Shi, H. Zhong, Firework: Big data sharing and processing in collaborative edge environment, in 2016 Fourth IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb) (IEEE, 2016), pp. 20–25

    Google Scholar 

  29. D.Y. Zhang, Y. Ma, Y. Zhang, S. Lin, X.S. Hu, D. Wang, A real-time and non-cooperative task allocation framework for social sensing applications in edge computing systems, in 2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) (IEEE, 2018), pp. 316–326

    Google Scholar 

  30. D.Y. Zhang, Y. Ma, C. Zheng, Y. Zhang, X.S. Hu, D. Wang, Cooperative-competitive task allocation in edge computing for delay-sensitive social sensing, in 2018 IEEE/ACM Symposium on Edge Computing (SEC) (IEEE, 2018), pp. 243–259

    Google Scholar 

  31. D. Zhang, N. Vance, D. Wang, When social sensing meets edge computing: Vision and challenges, in 2019 28th International Conference on Computer Communication and Networks (ICCCN) (IEEE, 2019), pp. 1–9

    Google Scholar 

  32. D.Y. Zhang, Y. Zhang, Q. Li, T. Plummer, D. Wang, Crowdlearn: A crowd-ai hybrid system for deep learning-based damage assessment applications, in 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) (IEEE, 2019), pp. 1221–1232

    Google Scholar 

  33. Y. Zhang, H. Wang, D.Y. Zhang, D. Wang, Deeprisk: A deep transfer learning approach to migratable traffic risk estimation in intelligent transportation using social sensing, in 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) (IEEE, 2019), pp. 123–130

    Google Scholar 

  34. D. Zhang, Y. Ma, X.S. Hu, D. Wang, Toward privacy-aware task allocation in social sensing-based edge computing systems. IEEE Internet Things J. 7(12), 11384–11400 (2020)

    Article  Google Scholar 

  35. D.Y. Zhang, Y. Huang, Y. Zhang, D. Wang, Crowd-assisted disaster scene assessment with human-AI interactive attention, in Proceedings of the AAAI Conference on Artificial Intelligence (2020), pp. 2717–2724

    Google Scholar 

  36. D.Y. Zhang, Z. Kou, D. Wang, Fairfl: A fair federated learning approach to reducing demographic bias in privacy-sensitive classification models, in 2020 IEEE International Conference on Big Data (Big Data) (IEEE, 2020), pp. 1051–1060

    Google Scholar 

  37. D.Y. Zhang, Z. Kou, D. Wang, Fedsens: A federated learning approach for smart health sensing with class imbalance in resource constrained edge computing, in IEEE INFOCOM 2021-IEEE Conference on Computer Communications (IEEE, 2021), pp. 1–10

    Google Scholar 

  38. G. Zhong, A. Dubey, C. Tan, T. Mitra. Synergy: An HW/SW framework for high throughput CNNS on embedded heterogeneous SoC. ACM Trans. Embedded Comput. Syst. (TECS) 18(2), 13 (2019)

    Google Scholar 

  39. 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 

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Authors and Affiliations

  1. Champaign, IL, USA

    Dong Wang

  2. Amazon Alexa AI, Seattle, WA, USA

    Daniel ‘Yue’ Zhang

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  1. Dong Wang
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Wang, D., Zhang, D.‘. (2023). A New Human-Centric Computing Age at Edge. In: Social Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26936-3_1

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  • DOI: https://doi.org/10.1007/978-3-031-26936-3_1

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