Skip to main content
Log in

Heterogeneous network bandwidth management scheme based on the principal-agent game model

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In order to provide more comprehensive network services, a concept of integrated heterogeneous network system was introduced. Until now, lots of researchers have focused on how to efficiently integrate different types of wireless and mobile networks. To exploit the heterogeneous network system operation, an important issue is how to properly manage the network bandwidth. In this study, a new bandwidth management scheme has been proposed by employing the principal-agent game model. Among heterogeneous networks, we have analyzed the asymmetric information situation and developed an effective bandwidth allocation algorithm. Under diverse network condition changes, our principal-agent game approach is essential to provide a suitable tradeoff between conflicting requirements. Simulation results demonstrate that the proposed scheme can obtain a better network performance and bandwidth efficiency than other existing schemes.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Shen, W., & Zeng, Q.-A. (2008). Resource management schemes for multiple traffic in integrated heterogeneous wireless and mobile networks. In IEEE ICCCN’08, pp. 1–6.

  2. Xue, P., Gong, P., Park, J. H., Park, D., & Kim, D. K. (2012). Radio resource management with proportional rate constraint in the heterogeneous networks. IEEE Transactions on Wireless Communications, 11(3), 1066–1075.

    Article  Google Scholar 

  3. Kim, Kwang Sik, Uno, Shintaro, & Kim, Moo Wan. (2010). Adaptive QoS mechanism for wireless mobile network. JCSE, 4(2), 153–172.

    Google Scholar 

  4. Ferri, F., Grifoni, P., Kondratova, I., D’Ulizia, A., & Caschera, M. C. (2010). Preface for the special issue on mobile and networking technologies for modelling social applications and services. JCSE, 4(1), 52.

    Google Scholar 

  5. Kim, S. (2014). Game theory applications in network design. Hershey: IGI Global.

    Book  Google Scholar 

  6. Kathleen, M. (1989). Eisenhardt, “Agency theory: An assessment and review”. Academy of Management Review, 14, 57–74.

    Google Scholar 

  7. Haptonstahl, S. Competing solutions to the principal-agent model. Working paper. Saint Louis: Washington University, http://www.polmeth.wustl.edu/media/Paper/Haptonstahl2009_PrincipalAgentStatisticalModel.pdf.

  8. Lopez-Benitez, M., & Gozalvez, J. (2011). Common radio resource management algorithms for multimedia heterogeneous wireless networks. IEEE Transactions on Mobile Computing, 10(9), 1201–1213.

    Article  Google Scholar 

  9. Xue, P., Gong, P., Park, J. H., Park, D., & Kim, D. K. (2012). Radio resource management with proportional rate constraint in the heterogeneous networks. IEEE Transactions on Wireless Communications, 11(3), 1066–1075.

    Article  Google Scholar 

  10. Li, L., Li, S., & Zhao, S. (2014). QoS-aware scheduling of services-oriented internet of things. IEEE Transactions on Industrial Informatics, 10(2), 1497–1505.

    Article  MathSciNet  Google Scholar 

  11. Zhang, H., Jiang, C., Beaulieu, N. C., Chu, X., Wen, X., & Tao, M. (2014). Resource allocation in spectrum-sharing OFDMA femtocells with heterogeneous services. IEEE Transactions on Communications, 62(7), 2366–2377.

    Article  Google Scholar 

  12. Zhang, H., Jiang, C., Beaulieu, N., Chu, X., Wang, X., & Quek, T. (2015). Resource allocation for cognitive small cell networks: A cooperative bargaining game theoretic approach. IEEE Transactions on Wireless Communications, 14(6), 3481–3493.

    Article  Google Scholar 

  13. Jin, X., Chun, S., Jung, J., & Lee, K.-H. (2014). IoT service selection based on physical service model and absolute dominance relationship. In IEEE SOCA’2014, pp. 65–72.

  14. Lohi, M., Weerakoon, D., & Aghvami, A. H. (1999). Trends in multi-layer cellular system design and handover design. In IEEE wireless communications and networking conference, pp. 898–902.

  15. Ning, G., Zhu, G., Li, Q., & Wu, R. (2006). Dynamic load balancing based on sojourn time in multitier cellular systems. In IEEE vehicular technology conference, pp. 111–116.

  16. Yuan-xiang, J., & Hong-lian, G. (2013). The principal-agent game analysis among accounting firm, enterprise customer and government. In IEEE ICSSSM’2013, pp. 623–627.

  17. Liu, Y. (2010). The game model of logistics finance based on the principal-agent theory. In IEEE ICEEE’2010, pp. 1–4.

  18. Alnwaimi, G., Vahid, S., & Moessner, K. (2015). Dynamic heterogeneous learning games for opportunistic access in lte-based macro/femtocell deployments. IEEE Transactions on Wireless Communications, 14(4), 2294–2308.

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2015-H8501-15-1018) supervised by the IITP (Institute for Information & Communications Technology Promotion).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sungwook Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, S. Heterogeneous network bandwidth management scheme based on the principal-agent game model. Wireless Netw 23, 279–287 (2017). https://doi.org/10.1007/s11276-015-1167-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1167-x

Keywords

Navigation