Log in

Efficient computation for task offloading in 6G mobile computing systems

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

This research focuses on achieving efficient computation for complex tasks within the overlap** coverage of 6G network base stations. A multi-access edge computing network model that involves multiple base stations and IoT devices is constructed by addressing task offloading challenges while considering task latency, energy consumption, societal impacts, and economic incentives. Joint optimization of base station pricing, IoT device base station selection, and task offloading strategies aim to maximize base station profits and IoT device utilities. A many-to-one matching game model tackles IoT device base station selection, and a Stackelberg game theory-based two-stage model handles pricing and task offloading interactions. The proposed game theory-based optimal pricing and best response algorithm (OBGT) achieves equilibrium strategies, demonstrating rapid convergence in simulations and enhancing base station profits and IoT device utility. This study incorporates Data-driven Mobile Computing Systems Assurance for advancing efficient task offloading and optimization.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /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 includes VAT (Germany)

Instant access to the full article PDF.

Fig. 1
Algorithm 1
Algorithm 2:
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Lin, C., Han, G., Jiang, J., Li, C., Shah, S.B.H., Liu, Q.: Underwater pollution tracking based on software-defined multi-tier edge computing in 6G-based underwater wireless networks. IEEE J. Sel. Areas Commun.Commun. 41(2), 491–503 (2023). https://doi.org/10.1109/JSAC.2022.3233625

    Article  Google Scholar 

  2. Shukla, A., Ahmed, N., Roy, A., Misra, S.C.: Softwarized management of 6G network for green Internet of Things. Comput. Commun.. Commun. 187, 103–114 (2022). https://doi.org/10.1016/j.comcom.2022.01.018

    Article  Google Scholar 

  3. Zhang, Q., Bizon, I., Kumar, A., Martinez, A.B., Chafii, M., Fettweis, G.: A novel approach for cancelation of nonaligned inter spreading factor interference in LoRa systems. IEEE Open J. Commun. Soc. 3, 718–728 (2022). https://doi.org/10.1109/OJCOMS.2022.3166596

    Article  Google Scholar 

  4. Xu, L., Zhou, X., Tao, Y., Yu, X., Yu, M., Khan, F.: AF relaying secrecy performance prediction for 6G mobile communication networks in industry 5.0. IEEE Trans. Industr. Inf.Industr. Inf. 18(8), 5485–5493 (2022). https://doi.org/10.1109/TII.2021.3120511

    Article  Google Scholar 

  5. Taneja, A., Rani, S., Breñosa, J., Tolba, A., Kadry, S.: An improved WiFi sensing based indoor navigation with reconfigurable intelligent surfaces for 6G enabled IoT network and AI explainable use case. Future Gen. Comput. Syst. 149, 294–303 (2023). https://doi.org/10.1016/j.future.2023.07.016

    Article  Google Scholar 

  6. Kukliński, S., Tomaszewski, L., Kołakowski, R., Chemouil, P.: 6G-LEGO: a framework for 6G network slices. J. Commun. Netw.Commun. Netw. 23(6), 442–453 (2021). https://doi.org/10.23919/JCN.2021.000025

    Article  Google Scholar 

  7. Magsi, A.H., Ghulam, A., Memon, S., Javeed, K., Alhussein, M., Rida, I.: A machine learning-based attack detection and prevention system in vehicular named data networking. Comput. Mater. Contin. 77(2), 1445–1465 (2023)

    Google Scholar 

  8. Mahmood, N.H., Berardinelli, G., Khatib, E.J., Hashemi, R., De Lima, C., Latva-aho, M.: A functional architecture for 6G special-purpose industrial IoT networks. IEEE Trans. Ind. Inf. 19(3), 2530–2540 (2023). https://doi.org/10.1109/TII.2022.3182988

    Article  Google Scholar 

  9. Sitharthan, R., Rajesh, M., Vimal, S., Saravana Kumar, E., Yuvaraj, S., Kumar, A., Jacob Raglend, I., Vengatesan, K.: A novel autonomous irrigation system for smart agriculture using AI and 6G enabled IoT network. Microprocess. Microsyst.. Microsyst. 101, 104905 (2023). https://doi.org/10.1016/j.micpro.2023.104905

    Article  Google Scholar 

  10. Yan, S., Cao, X., Liu, Z., Liu, X.: Interference management in 6G space and terrestrial integrated networks: challenges and approaches. Intell. Conv. Netw. 1(3), 271–280 (2020). https://doi.org/10.23919/ICN.2020.0022

    Article  Google Scholar 

  11. Suzuki, N., Miura, H., Mochizuki, K., Matsuda, K.: Simplified digital coherent-based beyond-100G optical access systems for B5G/6G [Invited]. J. Opt. Commun. Netw. 14(1), A1–A10 (2022). https://doi.org/10.1364/JOCN.438884

    Article  Google Scholar 

  12. Irshaid, M.B., Salameh, H.B., Jararweh, Y.: Intelligent multichannel cross-layer framework for enhanced energy-efficiency in 6G-IoT wireless networks. Sustain. Energy Technol. Assess. 57, 103211 (2023). https://doi.org/10.1016/j.seta.2023.103211

    Article  Google Scholar 

  13. Karam, G.M., Gruber, M., Adam, I., Boutigny, F., Miche, Y., Mukherjee, S.: The evolution of networks and management in a 6G world: an inventor’s view. IEEE Trans. Netw. Serv. Manage.Netw. Serv. Manage. 19(4), 5395–5407 (2022). https://doi.org/10.1109/TNSM.2022.3188200

    Article  Google Scholar 

  14. Zhang, P., Li, L., Niu, K., Li, Y., Lu, G., Wang, Z.: An intelligent wireless transmission toward 6G. Intell. Converg. Netw. 2(3), 244–257 (2021). https://doi.org/10.23919/ICN.2021.0017

    Article  Google Scholar 

  15. Rahmani, R., Firouzi, R., Sadique, K.M.: Cognitive controller for 6G-enabled edge autonomic. Procedia Comput. Sci. 220, 71–77 (2023). https://doi.org/10.1016/j.procs.2023.03.012

    Article  Google Scholar 

  16. Cao, H., et al.: Toward tailored resource allocation of slices in 6G networks with softwarization and virtualization. IEEE Internet Things J. 9(9), 6623–6637 (2022). https://doi.org/10.1109/JIOT.2021.3111644

    Article  Google Scholar 

  17. Ye, N., Yu, J., Wang, A., Zhang, R.: Help from space: grant-free massive access for satellite-based IoT in the 6G era. Digital Commun. Netw. 8(2), 215–224 (2022). https://doi.org/10.1016/j.dcan.2021.07.008

    Article  Google Scholar 

  18. Sizer, T., et al.: Integrated solutions for deployment of 6G mobile networks. J. Lightw. Technol. 40(2), 346–357 (2022). https://doi.org/10.1109/JLT.2021.3110436

    Article  CAS  ADS  Google Scholar 

  19. Shen, X., Gao, J., Wu, W., Li, M., Zhou, C., Zhuang, W.: Holistic network virtualization and pervasive network intelligence for 6G. IEEE Commun. Surv. Tutor. 24(1), 1–30 (2022). https://doi.org/10.1109/COMST.2021.3135829

    Article  Google Scholar 

  20. Du, J., Zhang, Y., Chen, Y., Li, X., Cheng, Y., Rajesh, M.V.: Hybrid beamforming NOMA for mmWave half-duplex UAV relay-assisted B5G/6G IoT networks. Comput. Commun.. Commun. 180, 232–242 (2021). https://doi.org/10.1016/j.comcom.2021.09.025

    Article  Google Scholar 

  21. Zhang, X., Wang, J., Poor, H.V.: Statistical delay and error-rate bounded QoS provisioning over mmWave cell-free M-MIMO and FBC-HARQ-IR based 6G wireless networks. IEEE J. Sel. Areas Commun.Commun. 38(8), 1661–1677 (2020). https://doi.org/10.1109/JSAC.2020.3000804

    Article  Google Scholar 

  22. Tang, S., Zhou, W., Chen, L., Lai, L., **a, J., Fan, L.: Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks. Phys. Commun. 47, 101381 (2021). https://doi.org/10.1016/j.phycom.2021.101381

    Article  Google Scholar 

  23. Wang, K., Xu, P., Chen, C.-M., Kumari, S., Shojafar, M., Alazab, M.: Neural architecture search for robust networks in 6G-enabled massive IoT domain. IEEE Internet Things J. 8(7), 5332–5339 (2021). https://doi.org/10.1109/JIOT.2020.3040281

    Article  Google Scholar 

  24. Jang, H.S., Jung, B.C., Quek, T.Q.S., Sung, D.K.: Resource-hop**-based grant-free multiple access for 6G-enabled massive IoT networks. IEEE Internet of Things J. 8(20), 15349–15360 (2021). https://doi.org/10.1109/JIOT.2021.3064872

    Article  Google Scholar 

  25. Alotaibi, A., Barnawi, A.: IDSoft: A federated and softwarized intrusion detection framework for massive internet of things in 6G network. J. King Saud Univ. Comput. Inf. Sci. 35(6), 101575 (2023). https://doi.org/10.1016/j.jksuci.2023.101575

    Article  Google Scholar 

  26. Wan, S., Hu, J., Chen, C., Jolfaei, A., Mumtaz, S., Pei, Q.: Fair-hierarchical scheduling for diversified services in space, air and ground for 6G-dense internet of things. IEEE Trans. Netw. Sci. Eng. 8(4), 2837–2848 (2021). https://doi.org/10.1109/TNSE.2020.3035616

    Article  Google Scholar 

  27. Khan, W.U., Jameel, F., Jamshed, M.A., Pervaiz, H., Khan, S., Liu, J.: Efficient power allocation for NOMA-enabled IoT networks in 6G era. Phys. Commun. 39, 101043 (2020). https://doi.org/10.1016/j.phycom.2020.101043

    Article  Google Scholar 

  28. Dong, T., et al.: Intelligent joint network slicing and routing via GCN-powered multi-task deep reinforcement learning. IEEE Trans. Cogn. Commun. Netw. 8(2), 1269–1286 (2022). https://doi.org/10.1109/TCCN.2021.3136221

    Article  Google Scholar 

  29. Panwar, P., Shabaz, M., Nazir, S., Keshta, I., Rizwan, A., Sugumar, R.: Generic edge computing system for optimization and computation offloading of unmanned aerial vehicle. Comput. Electr. Eng.. Electr. Eng. 109, 108779 (2023). https://doi.org/10.1016/j.compeleceng.2023.108779

    Article  Google Scholar 

  30. Barbosa, R., Ogobuchi, O.D., Joy, O.O., Saadi, M., Rosa, R.L., Otaibi, S.A., Rodriguez, D.Z.: IoT based real-time traffic monitoring system using images sensors by sparse deep learning algorithm. Comput. Commun.. Commun. (2023). https://doi.org/10.1016/j.comcom.2023.08.007

    Article  Google Scholar 

  31. Zheng, Z., Wang, L., Zhu, F., Liu, L.: Potential technologies and applications based on deep learning in the 6G networks. Comput. Electr. Eng.. Electr. Eng. 95, 107373 (2021). https://doi.org/10.1016/j.compeleceng.2021.107373

    Article  Google Scholar 

  32. Ma, Z., Yuan, X., Liang, K., Feng, J., Zhu, L., Zhang, D., Yu, F.R.: Blockchain-escorted distributed deep learning with collaborative model aggregation towards 6G networks. Future Gen. Comput. Syst. 141, 555–566 (2023)

    Article  Google Scholar 

  33. Zhang, T.: An intelligent routing algorithm for energy prediction of 6G-powered wireless sensor networks. Alex. Eng. J. 76, 35–49 (2023). https://doi.org/10.1016/j.aej.2023.06.038

    Article  ADS  Google Scholar 

  34. Zou, S., Wu, J., Yu, H., Wang, W., Huang, L., Ni, W., Liu, Y.: Efficiency-optimized 6G: a virtual network resource orchestration strategy by enhanced particle swarm optimization. Digital Commun. Netw. (2023). https://doi.org/10.1016/j.dcan.2023.06.008

    Article  Google Scholar 

Download references

Funding

There is no funding to this research.

Author information

Authors and Affiliations

Authors

Contributions

P.K., B.T. and P.K. wrote the original draft of the manuscript and provided the methodology. A.J.H., T.R.V.L. and M.W.B. have prepared the figures, investigated and validated the research. All authors reviewed the manuscript.

Corresponding author

Correspondence to Mohammed Wasim Bhatt.

Ethics declarations

Conflict of interest

The authors have no conflict of Interest.

Human and animal participation

There is no human or animal participation done in this research.

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

Khatri, P., Tongli, B., Kumar, P. et al. Efficient computation for task offloading in 6G mobile computing systems. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00506-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41060-024-00506-z

Keywords

Navigation