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Joint radio resource allocation in fog radio access network for healthcare

  • Shiyuan Tong
  • Yun LiuEmail author
  • Hsin-Hung Cho
  • Hua-Pei Chiang
  • Zhenjiang Zhang
Article
  • 32 Downloads
Part of the following topical collections:
  1. Special issue on Fog Computing for Healthcare

Abstract

With the rapid development of healthcare, mobile cloud computing can improve medical efficiency by capturing and analyzing patient data. Fog computing, as an emerging paradigm to complement cloud computing, has significant advantages in local wireless signal processing, resource management and distributed storage capabilities to potentially meet future healthcare demands. However, performance is limited by the capacity of fronthaul links. In this paper, we propose a novel fog radio access network (F-RAN) model, where cooperation caching strategy and content transmission are jointly optimized. We formulate a mixed integer nonlinear programming problem in order to achieve an ultra-low delay for the proposed F-RAN. We also propose a novel matching algorithm based on the student project allocation (SPA) algorithm instead of the traditional optimization algorithm to solve the formulated problem. Numerical results reveal that the proposed joint optimization design can significantly improve the performance of the considered F-RAN.

Keywords

Fog radio access network Resource allocation Ultra-low delay Student project allocation problem 

Notes

Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities under Grant 2018YJS007, and National Natural Science Foundation of China under Grant 61772064, and Academic Discipline, Post-Graduate Education Project of the Beijing Municipal Commission of Education.

References

  1. 1.
    Stanley M (2011) The Mobile Internet report: Ramping faster than desktop Internet, the mobile Internet will be bigger than most think. Retrieved April 2, 2011 from http://www.morganstanley.com/institutional/techresearch/pdfs/Theme_6_Data_Growth.pdf
  2. 2.
    Shi YQ, Ma C (2012) The Characteristics of the Mobile SNS Social Behavior: A Case Study of College Students in Shanghia. Proceedings of the 19th International Conference on Management Science & Engineering, 129–134. Dallas: IEEEGoogle Scholar
  3. 3.
    Zhu R, Zhang X, Liu X, Shu W, Mao T, Jalaian B (2015) Erdt energy-efficient reliable decision transmission for intelligent cooperative spectrum sensing in industrial iot. IEEE Access 3:2366–2378CrossRefGoogle Scholar
  4. 4.
    Gao H, Liu CH, Wang W et al (2015) A survey of incentive mechanisms for participatory sensing[J]. IEEE Commun Surv Tutorials 17(2):918–943CrossRefGoogle Scholar
  5. 5.
    Liu W, Park E (2014) Big data as an e-health service. In Computing, Networking and Communications (ICNC), 2014 International Conference on(pp. 982–988). IEEEGoogle Scholar
  6. 6.
    Stantchev V, Colomo-Palacios R, Niedermayer M (2014, Apr) Cloud computing based systems for healthcare. Sci World J 2014:1–2CrossRefGoogle Scholar
  7. 7.
    Zhang ZJ, Lai CF, Chao HC (2014) A green data transmission mechanism for wireless multimedia sensor networks using information fusion. IEEE Wirel Commun Mag 21(4):14–19Google Scholar
  8. 8.
    Zhang B, Liu CH, Tang J, Xu Z, Ma J, Wang W (2018) Learning-based energy-efficient data collection by unmanned vehicles in smart cities[J]. IEEE Trans Ind Inf 14(4):1666–1676CrossRefGoogle Scholar
  9. 9.
    Fog Computing and the Internet of Things: Extend the Cloud to Where the Things are, San Jose, CA, USA:CISCO, 2015. [Online]. Available: https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdf. Accessed 10 June 2018
  10. 10.
    Zhang K, Liang X, Ni J, Yang K, Shen X (2016) Exploiting social network to enhance human-to-human infection analysis without privacy leakage. IEEE Trans Dependable Secure ComputGoogle Scholar
  11. 11.
    Dsouza C, Ahn G-J, Taguinod M (2014) Policy-driven security management for fog computing: preliminary framework and a case study. Proc IEEE IRI, pp 16–23Google Scholar
  12. 12.
    Yi S et al (2015) Fog computing: platform and applications. Third IEEE workshop on hot topics in web systems and technologies IEEE computer society, pp 73–78Google Scholar
  13. 13.
    Zhou Y, Tang W, Zhang D, Lan X, Zhang Y (2017) A case for software-defined code scheduling based on transparent computing. Peer-to-Peer Networking and Applications, pp 1–11Google Scholar
  14. 14.
    Hsu H, Chen K-C (Jan. 2016) A resource allocation perspective on caching to achieve low latency. IEEE Commun Lett 20(1):145–148CrossRefGoogle Scholar
  15. 15.
    Rodrigues TG, Suto K, Nishiyama H, Kato N (May 2017) Hybrid method forminimizing service delay in edge cloud computing through VM migration and transmission power control. IEEE Trans Comput 66(5):810–819MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zhang W, Zhang Z, Chao HC (2017) Cooperative fog computing for dealing with big data in the internet of vehicles: architecture and hierarchical resource management. IEEE Commun Mag 55(12):60–67CrossRefGoogle Scholar
  17. 17.
    Aazam M, Huh E (2015) E-HAMC: leveraging fog computing for emergency alert service. IEEE international conference on pervasive computing and communication workshops, pp 518–523Google Scholar
  18. 18.
    Gu L, Zeng D, Guo S, Barnawi A, Xiang Y (2017) Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans Emerg Topics Comput 5(1):108–119Google Scholar
  19. 19.
    Dubey H et al (2016) Fog data: enhancing telehealth big data through fog computing In: Proceedings of the ASE BigData & SocialInformatics 2015, 14.. ACMGoogle Scholar
  20. 20.
    Chen Z, Lee J, Quek TQS, Kountouris M (2016) Cooperative caching and transmission design in cluster-centric small cell networks in, Jan. 2016, [online] Available: https://arxiv.org/abs/1601.00321
  21. 21.
    Shih Y-Y et al (2017) Enabling low-latency applications in fog-radio access networks. IEEE Netw 31(1):52–58CrossRefGoogle Scholar
  22. 22.
    Bertsimas D, Tsitsiklis JN (1997) Introduction to linear optimization, volume 6 of Athena scientific series in optimization and neural computation. Athena ScientificGoogle Scholar
  23. 23.
    El-Atta AHA, Moussa MI (2009) Student project allocation withpreference lists over (student, project) pairs. In: Second international conference on computer and electrical engineering, Dubai, Dec 2009Google Scholar
  24. 24.
    Roth AE, Sotomayor MAO (1990) Two-sided matching: a study in game-theoretic modeling and analysis. Econometric Society Monographs. Cambridge University Press, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shiyuan Tong
    • 1
  • Yun Liu
    • 1
    Email author
  • Hsin-Hung Cho
    • 2
  • Hua-Pei Chiang
    • 3
  • Zhenjiang Zhang
    • 4
  1. 1.School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of EducationBeijing Jiaotong UniversityBeijingChina
  2. 2.Department of Computer Science and Information EngineeringNational Ilan UniversityTaipeiTaiwan
  3. 3.Network and Technology DivisionFarEasTone Telecommunications Company LimitedTaipeiTaiwan
  4. 4.School of Software Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of EducationBeijing Jiaotong UniversityBeijingChina

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