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Social Edge Trends and Applications

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Social Edge Computing

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Abstract

The advent of edge computing pushes the frontier of computation, service, and data along the cloud-to-things continuum to the edge of the network, and brings new opportunities for human-centric applications (e.g., social sensing, smart mobile computing, edge intelligence). By coupling those applications with edge computing, the individually owned edge devices form a federation of computational nodes where the data collected from them can be processed and consumed “on the spot”. In this chapter, we offer a high-level view of the SEC paradigm, its background, motivation, trends, enabling technologies, and examples of applications.

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Notes

  1. 1.

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References

  1. M. Aazam, E.-N. Huh, Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT, in 2015 IEEE 29th International Conference on Advanced Information Networking and Applications (IEEE, 2015), pp. 687–694

    Google Scholar 

  2. A. Artikis, M. Weidlich, F. Schnitzler, I. Boutsis, T. Liebig, N. Piatkowski, C. Bockermann, K. Morik, V. Kalogeraki, J. Marecek et al., Heterogeneous stream processing and crowdsourcing for urban traffic management, in EDBT (2014), pp. 712–723

    Google Scholar 

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

  4. K. Bilal, O. Khalid, A. Erbad, S.U. Khan, Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. Comput. Netw. 130, 94–120 (2018)

    Article  Google Scholar 

  5. F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC ’12, New York (ACM, 2012), pp. 13–16

    Google Scholar 

  6. 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 

  7. Y. Cong, J. Yuan, J. Liu, Abnormal event detection in crowded scenes using sparse representation. Pattern Recogn. 46(7), 1851–1864 (2013)

    Article  Google Scholar 

  8. E. D’Andrea, P. Ducange, B. Lazzerini, F. Marcelloni, Real-time detection of traffic from twitter stream analysis. IEEE Trans. Intell. Transp. Syst. 16(4), 2269–2283 (2015)

    Article  Google Scholar 

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

    Google Scholar 

  10. W. Gao, Opportunistic peer-to-peer mobile cloud computing at the tactical edge, in Military Communications Conference (MILCOM), 2014 IEEE (IEEE, 2014), pp. 1614–1620

    Google Scholar 

  11. 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 

  12. S. Han, H. Mao, W.J. Dally, Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. Preprint. arXiv:1510.00149 (2015)

    Google Scholar 

  13. 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 

  14. 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 

  15. N.P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers et al., In-datacenter performance analysis of a tensor processing unit, in 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) (IEEE, 2017), pp. 1–12

    Google Scholar 

  16. S. Kosta, A. Aucinas, P. Hui, R. Mortier, X. Zhang, Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading, in Infocom, 2012 Proceedings IEEE (IEEE, 2012), pp. 945–953

    Google Scholar 

  17. K. Kumar, Y.-H. Lu, Cloud computing for mobile users: Can offloading computation save energy? Computer 43(4), 51–56 (2010)

    Article  Google Scholar 

  18. 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 

  19. X. Li, D. Caragea, H. Zhang, M. Imran, Localizing and quantifying damage in social media images, in 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE, 2018)

    Google Scholar 

  20. C. Lu, J. Shi, J. Jia, Abnormal event detection at 150 fps in matlab, in Proceedings of the IEEE International Conference on Computer Vision (2013), pp. 2720–2727

    Google Scholar 

  21. P. Mach, Z. Becvar, Mobile edge computing: A survey on architecture and computation offloading. Preprint. arXiv:1702.05309 (2017)

    Google Scholar 

  22. X. Mao, X. Miao, Y. He, X.-Y. Li, Y. Liu, Citysee: Urban co 2 monitoring with sensors, in 2012 Proceedings IEEE INFOCOM (IEEE, 2012), pp. 1611–1619

    Google Scholar 

  23. 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 

  24. A. Mtibaa, K.A. Harras, A. Fahim, Towards computational offloading in mobile device clouds, in 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom), vol. 1 (IEEE, 2013), pp. 331–338

    Google Scholar 

  25. J. Ni, A. Zhang, X. Lin, X.S. Shen, Security, privacy, and fairness in fog-based vehicular crowdsensing. IEEE Commun. Mag. 55(6), 146–152 (2017)

    Article  Google Scholar 

  26. R.W. Ouyang, L.M. Kaplan, A. Toniolo, M. Srivastava, T.J. Norman, Parallel and streaming truth discovery in large-scale quantitative crowdsourcing. IEEE Trans. Parallel Distribut. Syst. 27(10), 2984–2997 (2016)

    Article  Google Scholar 

  27. T. Sakaki, M. Okazaki, Y. Matsuo, Earthquake shakes twitter users: real-time event detection by social sensors, in Proceedings of the 19th International Conference on World Wide Web (ACM, 2010), pp. 851–860

    Google Scholar 

  28. M. Satyanarayanan, The emergence of edge computing. Computer 50(1), 30–39 (2017)

    Article  Google Scholar 

  29. M. Satyanarayanan, P. Bahl, R. Caceres, N. Davies, The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14 (2009)

    Google Scholar 

  30. E. Saurez, K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwälder, Incremental deployment and migration of geo-distributed situation awareness applications in the fog, in Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems (ACM, 2016), pp. 258–269

    Google Scholar 

  31. 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 

  32. N. Vance, D.Y. Zhang, Y. Zhang, D. Wang, Privacy-aware edge computing in social sensing applications using ring signatures, in 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) (IEEE, 2018), pp. 755–762

    Google Scholar 

  33. N. Vance, D. Zhang, D. Wang, Edgecache: a game-theoretic edge-based content caching system for crowd video sharing, in 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (IEEE, 2019), pp. 750–757

    Google Scholar 

  34. D. Wang, L. Kaplan, H. Le, T. Abdelzaher, On truth discovery in social sensing: A maximum likelihood estimation approach, in Proc. ACM/IEEE 11th Int Information Processing in Sensor Networks (IPSN) Conf (2012), pp. 233–244

    Google Scholar 

  35. D. Wang, T. Abdelzaher, L. Kaplan, C.C. Aggarwal, Recursive fact-finding: A streaming approach to truth estimation in crowdsourcing applications, in 2013 IEEE 33rd International Conference on Distributed Computing Systems (IEEE, 2013), pp. 530–539

    Google Scholar 

  36. D. Wang, T. Abdelzaher, L. Kaplan, Social Sensing: Building Reliable Systems on Unreliable Data (Morgan Kaufmann, 2015)

    Google Scholar 

  37. J. Wang, M. Li, Y. He, H. Li, K. Xiao, C. Wang, A blockchain based privacy-preserving incentive mechanism in crowdsensing applications. IEEE Access 6, 17545–17556 (2018)

    Article  Google Scholar 

  38. 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 

  39. 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 

  40. S. Wang, T. Tuor, T. Salonidis, K.K. Leung, C. Makaya, T. He, K. Chan, Adaptive federated learning in resource constrained edge computing systems. IEEE J. Sel. Areas Commun. 37(6), 1205–1221 (2019)

    Article  Google Scholar 

  41. Y. Xiao, S. Nazarian, P. Bogdan, Self-optimizing and self-programming computing systems: A combined compiler, complex networks, and machine learning approach. IEEE Trans. Very Large Scale Integration (VLSI) Syst. 27(6), 1416–1427 (2019)

    Google Scholar 

  42. S. Yi, Z. Hao, Z. Qin, Q. Li, Fog computing: Platform and applications, in 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb) (IEEE, 2015), pp. 73–78

    Google Scholar 

  43. X. You, C. Zhang, X. Tan, S. Jin, H. Wu, Ai for 5g: research directions and paradigms. Sci. China Inf. Sci. 62(2), 21301 (2019)

    Google Scholar 

  44. 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 

  45. 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 

  46. D.Y. Zhang, C. Zheng, D. Wang, D. Thain, X. Mu, G. Madey, C. Huang, Towards scalable and dynamic social sensing using a distributed computing framework, in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) (IEEE, 2017), pp. 966–976

    Google Scholar 

  47. Q. Zhang, Q. Zhang, W. Shi, H. Zhong, Enhancing amber alert using collaborative edges: Poster, in Proceedings of the Second ACM/IEEE Symposium on Edge Computing (ACM, 2017), pp. 27

    Google Scholar 

  48. 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 

  49. 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 

  50. Y. Zhang, Y. Lu, D.Y. Zhang, L. Shang, D. Wang, Risksens: A multi-view learning approach to identifying risky traffic locations in intelligent transportation systems using social and remote sensing, in 2018 IEEE International Conference on Big Data (Big Data) (IEEE, 2018), pp. 1544–1553

    Google Scholar 

  51. Y. Zhang, D. Zhang, N. Vance, Q. Li, D. Wang, A light-weight and quality-aware online adaptive sampling approach for streaming social sensing in cloud computing, in 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) (IEEE, 2018), pp. 1–8

    Google Scholar 

  52. 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 

  53. D.Y. Zhang, T. Rashid, X. Li, N. Vance, D. Wang, Heteroedge: Taming the heterogeneity of edge computing system in social sensing, in Proceedings of the International Conference on Internet of Things Design and Implementation (IoTDI) (ACM, 2019), pp. 37–48. https://doi.org/10.1145/3302505.3310067

  54. 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 

  55. 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 

  56. C.L. Zitnick, P. Dollár, Edge boxes: Locating object proposals from edges, in European Conference on Computer Vision (Springer, 2014), pp. 391–405

    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). Social Edge Trends and Applications. In: Social Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26936-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-26936-3_2

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