http://iet.metastore.ingenta.com
1887

Energy-efficiency fog computing resource allocation in cyber physical internet of things systems

Energy-efficiency fog computing resource allocation in cyber physical internet of things systems

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Cyber physical internet of things systems (CPIoTs), taking advantages of cyber physical systems, have been considered as a promising technology to provide better interaction and interoperability among various machines. However, the development of CPIoTs suffers severely from big data. In this context, fog computing is proposed to handle the big data bottleneck of CPIoTs. In this study, the authors focus on the joint optimisation of the communication resources and computation resources in fog computing-based CPIoTs to maximise the overall system energy efficiency, in which multiple fog nodes and end users are taken into consideration. Moreover, since the channel estimation error will become serious with the expanding scale, the imperfect channel state information is considered in this study. The formulated optimisation problem is a mixed integer non-linear problem which is indeed non-deterministic polynomial hard, hence a probability distribution method is proposed to reformulate the problem into a non-probability form, and the resource allocation algorithm based on Dinkelbach algorithm and Lagrange duality approach is adopted to tackle the problem efficiently. The simulation results confirm the effectiveness of the proposed scheme, especially when the scales are enormous.

References

    1. 1)
      • 1. Al-Fuqaha, A., Guizani, M., Mohammadi, M., et al: ‘Internet of things: a survey on enabling technologies, protocols and applications’, IEEE Commun. Surv. Tutor., 2015, 17, (4), pp. 23472376.
    2. 2)
      • 2. Singh, J., Pasquier, T., Bacon, J.M., et al: ‘Twenty security considerations for cloud-supported internet of things’, IEEE Internet Things J., 2017, 3, (3), pp. 269284.
    3. 3)
      • 3. Jalali, F., Hinton, K., Ayre, R., et al: ‘Fog computing may help to save energy in cloud computing’, IEEE J. Sel. Areas Commun., 2016, 34, (5), pp. 17281739.
    4. 4)
      • 4. Hou, X., Li, Y., Chen, M., et al: ‘Vehicular fog computing: a viewpoint of vehicles as the infrastructures’, IEEE Trans. Veh. Technol., 2016, 65, (6), pp. 38603873.
    5. 5)
      • 5. Peng, M., Yan, S., Zhang, K., et al: ‘Fog computing based radio access networks: issues and challenges’, IEEE Netw., 2015, 30, (4), pp. 4653.
    6. 6)
      • 6. Lin, J., Yu, W., Zhang, N., et al: ‘A survey on internet of things: architecture, enabling technologies, security and privacy, and applications’, IEEE Internet Things J., 2017, 4, (5), pp. 11251142.
    7. 7)
      • 7. Wei, Y., Yu, R.F., Song, M., et al: ‘Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor-critic deep reinforcement learning’, IEEE Internet Things J., 2019, 6, (2), pp. 20612073.
    8. 8)
      • 8. Zhou, Y., Yu, R.F., Chen, J., et al: ‘Robust energy-efficient resource allocation for ioT-powered cyber-physical-social smart systems with virtualization’, IEEE Internet Things J., 2019, 6, (2), pp. 24132426.
    9. 9)
      • 9. Zhou, Z., Hu, J., Liu, Q., et al: ‘Fog computing-based cyber-physical machine tool system’, IEEE Access, 2018, 6, pp. 4458044590.
    10. 10)
      • 10. Al-Jaroodi, J., Mohamed, N.: ‘PsCPS: a distributed platform for cloud and fog integrated smart cyber-physical systems’, IEEE Access, 2018, 6, pp. 4143241449.
    11. 11)
      • 11. Gu, L., Zeng, D., Guo, S., et al: ‘Cost efficient resource management in fog computing supported medical cyber-physical system’, IEEE Trans. Emerg. Top. Comput., 2017, 5, (1), pp. 108119.
    12. 12)
      • 12. Lin, C., Yang, J.: ‘Cost-efficient deployment of fog computing systems at logistics centers in industry 4.0’, IEEE Trans. Ind. Inf., 2018, 14, (10), pp. 46034611.
    13. 13)
      • 13. Li, S., Ni, Q., Sun, Y., et al: ‘Energy-efficient resource allocation for industrial cyber-physical IoT systems in 5G era’, IEEE Trans. Ind. Inf., 2018, 14, (6), pp. 26182628.
    14. 14)
      • 14. Guan, M., Bai, B., Wang, L., et al: ‘Joint optimization for computation offloading and resource allocation in internet of things’. 2017 IEEE 86th Vehicular Technology Conf. (VTC-Fall), Toronto, Canada, September 2017, pp. 15.
    15. 15)
      • 15. Yu, Y., Bu, X., Yang, K., et al: ‘Green large-scale fog computing resource allocation using joint benders decomposition, Dinkelbach algorithm, ADMM, and branch-and-bound’, IEEE Internet Things J., doi: 10.1109/JIOT.2018.2875587.
    16. 16)
      • 16. Gu, Y., Chang, Z., Pan, M., et al: ‘Joint radio and computational resource allocation in‘fog computing’, IEEE Trans. Veh. Technol., 2018, 67, (8), pp. 74757484.
    17. 17)
      • 17. Du, J., Zhao, L., Feng, J., et al: ‘Computation offloading and resource allocation in mixed fog/cloud computing systems with min–max fairness guarantee’, IEEE Trans. Commun., 2018, 66, (4), pp. 15941608.
    18. 18)
      • 18. Liu, Y., Yu, F.R., Li, X., et al: ‘Distributed resource allocation and computation offloading in fog and cloud networks with non-orthogonal multiple access’, IEEE Trans. Veh. Technol., 2018, 67, (12), pp. 1213712151.
    19. 19)
      • 19. Boyd, S., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, Cambridge, 2004).
    20. 20)
      • 20. Dinkelbach, W.: ‘On nonlinear fractional programming’, Manage. Sci., 1967, 13, (7), pp. 492498.
    21. 21)
      • 21. Chen, J., Zhou, Y., Kuo, Y., et al: ‘Energy-efficiency resource allocation for cognitive heterogeneous networks with imperfect channel state information’, IET Commun., 2016, 10, (11), pp. 13121319.
    22. 22)
      • 22. Saki, H., Shikh-Bahaei, M.: ‘Cross-layer resource allocation for video streaming over OFDMA cognitive radio networks’, IEEE Trans. Multimed., 2015, 17, (3), pp. 333345.
    23. 23)
      • 23. Cox, D.R., Reid, N.: ‘Approximations to non-central distributions’, Can. J. Stat., 1987, 15, (2), pp. 105114.
    24. 24)
      • 24. Ng, K.W.D., Lo, S.E., Schober, R., et al: ‘Energy-efficient resource allocation in OFDMA systems with large numbers of base station antennas’, IEEE Trans. Wirel. Commun., 2012, 11, (9), pp. 32923304.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2019.0121
Loading

Related content

content/journals/10.1049/iet-com.2019.0121
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address