Skip to main content

Advertisement

Springer Nature Link
Log in

Joint optimization of IoT devices access and bandwidth resource allocation for network slicing in edge-enabled radio access networks

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

With the rapid development of the Internet-of-Things (IoT) and the continuous progress in software technologies, IoT devices (IoTDs) have been applied to various scenarios for executing computation-intensive applications. Limited by many restrictions, IoTDs usually cannot fully meet the demands of these applications. In this context, the mobile edge computing (MEC) paradigm was proposed. In MEC paradigm, IoTDs and edge servers (ESs) form an edge-enabled radio access network (E-RAN) so that the IoTDs can solve the problem of their poor computational resources by transmitting data or tasks to the ES. In order to improve the Quality-of-Service (QoS) of E-RAN, network slicing technology is widely used. Although the combination of MEC and network slicing technology has effectively made up for the deficiencies of IoTDs, the resource efficiency of network slicing is still a serious challenge. This paper considers a multi-IoTD and multi-ES network system, where there are multiple service providers (SPs) with different priorities. In this system, the joint optimization of IoTDs access and bandwidth resource allocation is formulated, whose objective is to maximize the system th. To address this problem, we develop an optimization algorithm including greedy-based devices access and bandwidth resource allocation optimization (GDABRA) algorithm. Extensive simulation results are provided to demonstrate the system throughput growth of the proposed algorithm in comparison with benchmark algorithms.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Data available on request from the authors.

Notes

  1. For ease of expression, the following uses service provider (SP) to mean edge computing service provider.

References

  1. Global System for Mobile Communications Association: Internet of Thing News. https://www.gsma.com/iot/news/

  2. Zhou X, Liang W, Wang KI-K, Wang H, Yang LT, Jin Q (2020) Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet Things J 7(7):6429–6438

    Article  Google Scholar 

  3. Dong Y, Yao Y-D (2021) Secure mmwave-radar-based speaker verification for iot smart home. IEEE Internet Things J 8(5):3500–3511

    Article  Google Scholar 

  4. Yang L, Zhang L, He Z, Cao J, Wu W (2020) Efficient hybrid data dissemination for edge-assisted automated driving. IEEE Internet Things J 7(1):148–159

    Article  Google Scholar 

  5. Garcia-Saavedra A, Iosifidis G, Costa-Perez X, Leith DJ (2018) Joint optimization of edge computing architectures and radio access networks. IEEE J Sel Areas Commun 36(11):2433–2443

    Article  Google Scholar 

  6. Miozzo M, Ali Z, Giupponi L, Dini P (2021) Distributed and multi-task learning at the edge for energy efficient radio access networks. IEEE Access 9:12491–12505

    Article  Google Scholar 

  7. Zhou F, Wang N, Luo G, Fan L, Chen W (2020) Edge caching in multi-UAV-enabled radio access networks: 3D modeling and spectral efficiency optimization. IEEE Transactions on Signal and Information Processing over Networks 6:329–341

    Article  MathSciNet  Google Scholar 

  8. Ahmad A, Ahmad S, Rehmani MH, Hassan NU (2015) A survey on radio resource allocation in cognitive radio sensor networks. IEEE Commun Surv Tutorials 17(2):888–917

    Article  Google Scholar 

  9. Wijethilaka S, Liyanage M (2021) Survey on network slicing for internet of things realization in 5G networks. IEEE Communications Surveys & Tutorials 23(2):957–994

    Article  Google Scholar 

  10. Afolabi I, Taleb T, Samdanis K, Ksentini A, Flinck H (2018) Network slicing and softwarization: A survey on principles, enabling technologies, and solutions. IEEE Commun Surv Tutorials 20(3):2429–2453

    Article  Google Scholar 

  11. Sun Y, Peng M, Mao S, Yan S (2019) Hierarchical radio resource allocation for network slicing in fog radio access networks. IEEE Trans Veh Technol 68(4):3866–3881

    Article  Google Scholar 

  12. Tang J, Shim B, Quek TQ (2019) Service multiplexing and revenue maximization in sliced C-RAN incorporated with URLLC and multicast eMBB. IEEE J Sel Areas Commun 37(4):881–895

    Article  Google Scholar 

  13. Parsaeefard S, Dawadi R, Derakhshani M, Le-Ngoc T (2016) Joint user-association and resource-allocation in virtualized wireless networks. IEEE Access 4:2738–2750

    Article  Google Scholar 

  14. Wang K, Li H, Yu FR, Wei W (2016) Virtual resource allocation in software-defined information-centric cellular networks with device-to-device communications and imperfect CSI. IEEE Trans Veh Technol 65(12):10011–10021

    Article  Google Scholar 

  15. Chen X, Li A, Guo W, Huang G et al (2015) Runtime model based approach to iot application development. Front Comp Sci 9(4):540–553

    Article  Google Scholar 

  16. Huang G, Xu M, Lin FX, Liu Y, Ma Y, Pushp S, Liu X (2017) ShuffleDog: Characterizing and adapting user-perceived latency of android apps. IEEE Trans Mob Comput 16(10):2913–2926

    Article  Google Scholar 

  17. Yu J, Han S, Li X (2020) A robust game-based algorithm for downlink joint resource allocation in hierarchical OFDMA femtocell network system. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(7):2445–2455

    Article  Google Scholar 

  18. Yan S, Peng M, Cao X (2019) A game theory approach for joint access selection and resource allocation in UAV assisted IoT communication networks. IEEE Internet Things J 6(2):1663–1674

    Article  Google Scholar 

  19. Sun Y, Peng M, Mao S (2019) A game-theoretic approach to cache and radio resource management in fog radio access networks. IEEE Trans Veh Technol 68(10):10145–10159

    Article  Google Scholar 

  20. Liu B, Liu C, Peng M, Liu Y, Yan S (2020) Resource allocation for non-orthogonal multiple access-enabled fog radio access networks. IEEE Trans Wireless Commun 19(6):3867–3878

    Article  Google Scholar 

  21. Chen X, Zhu F, Chen Z, Min G, Zheng X, Rong C (2020) Resource allocation for cloud-based software services using prediction-enabled feedback control with reinforcement learning. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2020.2992537

  22. Xiang H, Peng M, Sun Y, Yan S (2020) Mode selection and resource allocation in sliced fog radio access networks: A reinforcement learning approach. IEEE Trans Veh Technol 69(4):4271–4284

    Article  Google Scholar 

  23. Zhang X, Peng M, Yan S, Sun Y (2020) Deep-reinforcement-learning-based mode selection and resource allocation for cellular V2X communications. IEEE Internet Things J 7(7):6380–6391

    Article  Google Scholar 

  24. Wei F, Feng G, Sun Y, Wang Y, Qin S, Liang Y-C (2020) Network slice reconfiguration by exploiting deep reinforcement learning with large action space. IEEE Trans Netw Serv Manage 17(4):2197–2211

    Article  Google Scholar 

  25. Wen W, Cui Y, Zheng F-C, Jin S, Jiang Y (2018) Random caching based cooperative transmission in heterogeneous wireless networks. IEEE Trans Commun 66(7):2809–2825

    Article  Google Scholar 

  26. Caballero P, Banchs A, de Veciana G, Costa-Pérez X (2017) Multi-tenant radio access network slicing: Statistical multiplexing of spatial loads. IEEE/ACM Trans Networking 25(5):3044–3058

    Article  Google Scholar 

  27. Hu Q, Cai Y, Yu G, Qin Z, Zhao M, Li GY (2019) Joint offloading and trajectory design for UAV-enabled mobile edge computing systems. IEEE Internet Things J 6(2):1879–1892

    Article  Google Scholar 

  28. Jeong S, Simeone O, Kang J (2018) Mobile edge computing via a UAV-mounted cloudlet: Optimization of bit allocation and path planning. IEEE Trans Veh Technol 67(3):2049–2063

    Article  Google Scholar 

  29. Ye Q, Rong B, Chen Y, Al-Shalash M, Caramanis C, Andrews JG (2013) User association for load balancing in heterogeneous cellular networks. IEEE Trans Wireless Commun 12(6):2706–2716

    Article  Google Scholar 

  30. Bertsekas DP, Scientific A (2015) Convex Optimization Algorithms. Athena Scientific Belmont, Nashua

  31. Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to Algorithms. MIT press, Englond

  32. Guo W, Lin B, Chen G, Chen Y, Liang F (2018) Cost-driven scheduling for deadline-based workflow across multiple clouds. IEEE Trans Netw Serv Manage 15(4):1571–1585

    Article  Google Scholar 

Download references

Acknowledgements

This work is partly supported by the Natural Science Foundation of China under Grant No. 62072108, the Natural Science Foundation of Fujian Province for Distinguished Young Scholar No. 2020J06014, the Natural Science Foundation of Fujian Province under Grant No. 2019J01286 and No. 2019J01427, and the Young and Middle-aged Teacher Education Foundation of Fujian Province under Grant No. JT180098.

Author information

Authors and Affiliations

  1. College of Computer and Control Engineering, Minjiang University, Fuzhou, China

    Jianshan Zhang

  2. College of Computer and Data Science, Fuzhou University, Fuzhou, China

    Ming Li

  3. College of Physics and Energy, Fujian Normal University, Fuzhou, China

    Bing Lin

  4. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, China

    Jianshan Zhang, Ming Li & Bing Lin

  5. Institut für Informatik, Freie Universität Berlin, Berlin, Germany

    Katinka Wolter

Authors
  1. Jianshan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bing Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katinka Wolter
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jianshan Zhang and Ming Li developed the model and performed experiments. Bing Lin wrote the main part of the manuscript, while Katinka Wolter provided the support for writing materials. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Bing Lin.

Ethics declarations

Ethics approval

This work does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Li, M., Lin, B. et al. Joint optimization of IoT devices access and bandwidth resource allocation for network slicing in edge-enabled radio access networks. Peer-to-Peer Netw. Appl. 16, 2879–2891 (2023). https://doi.org/10.1007/s12083-023-01535-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-023-01535-4

Keywords