Skip to main content
SpringerLink
Log in

An Extendable Layered Architecture for Collective Computing to Support Concurrent Multi-sourced Heterogeneous Tasks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Gregory D. Abowd shows a new vision of computer framework - collective computing. In this framework, kinds of remote computing devices, including even people who are regarded as a kind of computing device, are connected with each other into a group to complete a complex work. Therefore, the various computing devices with the different computing capacities can be fully used in different tasks. However, most of the relevant researches focus on improving infrastructure to address specific functions, the heterogeneous tasks performed in a common architecture and the large-scale integration are not paid enough attention. This paper presents a collective computing architecture for supporting concurrent multi-sourced heterogeneous tasks. The whole architecture is layered to provide different functions and obtain extensibility, loads balance, centralized dispatch and low delay communication. The extendible collective computing engine is used for analysing and allocating heterogeneous tasks, and the distributed device management controls heterogeneous computing devices. This architecture provides a common infrastructure for processing heterogeneous tasks by heterogeneous devices which dose not only design for some specialized systems or functions. At last, we implement a prototype system by this architecture for proving that the architecture can perform multi-sourced heterogeneous tasks well.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abowd GD (2016) Beyond weiser: from ubiquitous to collective computing. Computer 49(1):17–23

    Article  Google Scholar 

  2. Amazon mechanical turk, available at: https://www.mturk.com/

  3. Smirnov A, Ponomarev A (2015) Crowd Computing Framework for Geoinformation Tasks. Information Fusion and Geographic Information Systems (IF&GIS’ 2015). Springer International Publishing, Berlin, pp 109–123

    Google Scholar 

  4. Lasecki W, Miller C, Sadilek A, et al. (2012) Real-time captioning by groups of non-experts. ACM Symposium on User Interface Software and Technology ACM, pp 23–34

  5. Ahmad S, Battle A, Malkani Z, Kamvar S (2011) The jabberwocky programming environment for structured social computing. In: Proceedings 24th annual ACM symposium user interface software and technology, UIST ’11, pp 53–64

  6. Kittur A, Smus B, Khamkar S, Kraut RE (2011) Crowdforge: crowdsourcing complex work, In: Proceedings 24th annual ACM symposium user interface software and technology, UIST’11, pp 43–52

  7. Guo B, Zhang D, Yu Z, et al. (2013) From the internet of things to embedded intelligence. World Wide Web-internet & Web Information Systems 16(4):399–420

    Google Scholar 

  8. Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. Communications Magazine IEEE 49(11):32–39

    Article  Google Scholar 

  9. Wang Z, Zhang D, Zhou X, et al. (2014) Discovering and profiling overlapping communities in Location-Based social networks. IEEE Trans Syst Man Cybern Syst Hum 44(4):499–509

    Article  Google Scholar 

  10. Estrin DL (2010) Participatory sensing: applications and architecture. In: International Conference on Mobile Systems, Applications, and Services ACM, pp 3–4

  11. Bhuiyan MZA, Wu J, Wang G, et al. (2016) Sensing and decision making in Cyber-Physical systems: the case of structural event monitoring. IEEE Trans Ind Inf 12(6):2103–2114

    Article  Google Scholar 

  12. Sun Y, Nakata K (2010) An agent-based architecture for participatory sensing platform. Universal Communication Symposium IEEE, pp 392–400

  13. Gu W, Shangguan L, Yang Z, et al. (2016) Sleep hunter: towards fine grained sleep stage tracking with smartphones. IEEE Trans Mob Comput 15(6):1514–1527

    Article  Google Scholar 

  14. Mao X, Miao X, He Y, et al. (2012) Citysee: Urban CO2 monitoring with sensors, INFOCOM, In: 2012 Proceedings IEEE, pp 1611–1619

  15. Min M, Reddy S, Shilton K, et al. (2009) PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: International Conference on Mobile Systems, Applications, and Services DBLP, pp 55–68

  16. Hong JI, Landay JA (2004) An architecture for privacy-sensitive ubiquitous computing. In: International Conference on Mobile Systems, Applications, and Services DBLP, pp 177–189

  17. Pipes S, Chakraborty S (2014) Multitiered inference management architecture for participatory sensing. In: IEEE International Conference on Pervasive Computing and Communication Workshops IEEE, pp 74–79

  18. Robert S, Farkas K, Wiandt B (2013) Measurements of a real-time transit feed service architecture for mobile participatory sensing. Wireless Days (WD), 2013 IFIP, pp 1-4

  19. Hill R, Al-Muhtadi J, Campbell R, et al. (2004) A middleware architecture for securing ubiquitous computing cyber infrastructures. IEEE Distrib Syst Online 5(9):1–7

    Article  Google Scholar 

  20. Ben Messaoud R, Ghamri-Doudane Y (2016) QEMSS: A selection scheme for participatory sensing tasks. In: International Conference on Protocol Engineering IEEE, pp 1–6

  21. Barowy DW, Curtsinger C, Berger ED, McGregor A (2012) Automan: a platform for integrating human-based and digital computation. ACM Sigplan Not 47(10):639–654

    Article  Google Scholar 

  22. Bernstein A, Klein M, Malone TW (2012) Programming the global brain. Commun ACM 55(5):41–43

    Article  Google Scholar 

  23. Knowles, Bran, et al. (2015) Models and patterns of trust The. In: ACM Conference ACM, pp 328–338

  24. Sengupta B, Jain A, Bhattacharya K, et al. (2013) Collective problem solving using social compute units. Int J Coop Inf Syst 22(4):1–21

    Article  Google Scholar 

  25. Smirnov A, Ponomarev A, Shilov N (2014) Hybrid crowd-based decision support in business processes: the approach and reference model. Procedia Technol 16:376–385

    Article  Google Scholar 

  26. Mavridis N, Bourlai T, Ognibene D (2012) The human-robot cloud: situated collective intelligence on demand. In: 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp 360–365

  27. Liu J, Chen Y, Byna S (2015) Collective computing for scientific big data analysis. In: International Conference on Parallel Processing Workshops IEEE, pp 129–137

  28. Zhang H (2012) Computational environment design. Ph.D thesis, Harvard University Graduate School of Arts and Sciences, Cambridge, Massachusetts

  29. Das D, Mohan P, Padmanabhan V, et al. (2010) Prism: Platform for remote sensing using smartphones. In: The 8th International Conference on Mobile Systems, Applications, and Services, pp 63–76

  30. Christin D, Reinhardt A, Kanhere S, et al. (2011) A survey on privacy in mobile participatory sensing applications. J Syst Softw 84:1928–1946

    Article  Google Scholar 

  31. Li Y, Zhao Y, Ishak S, et al. (2017) An anonymous data reporting strategy with ensuring incentives for mobile crowd-sensing. J Ambient Intell Humaniz Comput 2017(B):1–15

    Google Scholar 

  32. Kulkarni AP, Can M, Hartmann B (2011) Turkomatic: automatic recursive task and workflow design for mechanical turk. In: CHI ’11 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, pp 2053–2058

  33. Li Q-S, Hua C, Man Z, et al. (2015) Collaboration strategy for software dynamic evolution of multi-agent system. J Cent South Univ 22(7):2629–2637

    Article  Google Scholar 

  34. Iverson MA, Ozguner F, Follen GJ (1996) Run-time statistical estimation of task execution times for heterogeneous distributed computing.” IEEE International Symposium on High PERFORMANCE Distributed Computing IEEE Computer Society, p 263

  35. Ye P, Doermann D (2014) Combining preference and absolute judgements in a crowd-sourced setting, ICML’13 Workshop: Machine Learning Meets Crowdsourcing

  36. Scekic O, Truong H-L, Dustdar S (2013) Incentives and rewarding in social computing. Commun ACM 56(6):72–82

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Defense Industrial Technology Development Program under Grant No. JCKY2016605B006, Six talent peaks project in Jiangsu Province under Grant No. XYDXXJS-031.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, 150001, Harbin, China

    Yang Li

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, China

    Yunlong Zhao, Zhenhua Zhang, Qian Geng & Ran Wang

  3. School of Computer Science, Northwestern Polytechnical University, 710072, Xi’an, China

    Bin Guo

Authors
  1. Yang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunlong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qian Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ran Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunlong Zhao.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Zhao, Y., Guo, B. et al. An Extendable Layered Architecture for Collective Computing to Support Concurrent Multi-sourced Heterogeneous Tasks. Mobile Netw Appl 26, 884–898 (2021). https://doi.org/10.1007/s11036-019-01331-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01331-6

Keywords

Search

Navigation