Skip to main content
Log in

Enhanced channel allocation scheme for cross layer management in wireless network based on interference management

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The entire spectrum of communication in networks relies on transmitting data from node to node in a scalable manner. Though elastic nature of networks is made possible through protocols and communication, it still remains an open question of how different applications can work together. Hence though a proper channel allocation, it can provide services to users thereby taking care of the application level requirements. This paper proposes an enhanced channel allocation using the strengths of cross layer functionalities. The proposed formulae enhance the cross layer handling considering the interference issues in the wireless network. The proposed algorithm, simulated for the wireless network for different range of nodes and performance, is presented based on routing overhead, packet loss, packet delivery ratio, average delay, energy consumption, and throughput. It is concluded from the results that the performance of the wireless network is improved by using the proposed cross layer technique.

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. Kang, S.H., Nguyen, T.: Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Commun. Lett. 16(9), 1396–1399 (2012). https://doi.org/10.1109/LCOMM.2012.073112.120450

    Article  Google Scholar 

  2. Ni, Q., Pan, Q., Du, H., Cao, C., Zhai, Y.: A novel cluster head selection algorithm based on fuzzy clustering and particle swarm optimization. IEEE/ACM Trans. Comput. Biol. Bioinform. 14(1), 76–84 (2017). https://doi.org/10.1109/TCBB.2015.2446475

    Article  Google Scholar 

  3. Jia, D., Zhu, H., Zou, S., Hu, P.: Dynamic cluster head selection method for wireless sensor network. IEEE Sens. J. 16(8), 2746–2754 (2016). https://doi.org/10.1109/JSEN.2015.2512322

    Article  Google Scholar 

  4. Bhorkar, A., Naghshvar, M., Javidi, T.: Opportunistic routing with congestion diversity in wireless ad hoc networks. IEEE/ACM Trans. Netw. 24(2), 1167–1180 (2016). https://doi.org/10.1109/TNET.2015.2413398

    Article  Google Scholar 

  5. Ramneek, P., Choi, W., Seok, W.: Congestion detection for QoS-enabled wireless networks and its potential applications. J. Commun. Netw. 18(3), 513–522 (2016). https://doi.org/10.1109/JCN.2016.000066

    Article  Google Scholar 

  6. Li, C., Xie, R., Huang, T., Liu, Y.: Jointly optimal congestion control, forwarding strategy and power control for named-data multihop wireless network. IEEE Access 5, 1013–1026 (2017). https://doi.org/10.1109/ACCESS.2016.2634525

    Article  Google Scholar 

  7. Hasan, Z., Bansal, G., Hossain, E., Bhargava, V.K.: Energy-efficient power allocation in OFDM-based cognitive radio systems: a risk-return model. IEEE Trans. Wirel. Commun. 8(12), 6078–6088 (2009). https://doi.org/10.1109/TWC.2009.12.090394

    Article  Google Scholar 

  8. Ekstrom, M.C., Bergblomma, M., Linden, M., Bjorkman, M., Ekstrom, M.: A bluetooth radio energy consumption model for low-duty-cycle applications. IEEE Trans. Instrum. Meas. 61(3), 609–617 (2012). https://doi.org/10.1109/TIM.2011.2172997

    Article  Google Scholar 

  9. Arienzo, L., Tarchi, D.: Statistical modeling of spectrum sensing energy in multi-hop cognitive radio networks. IEEE Signal Process. Lett. 22(3), 356–360 (2015). https://doi.org/10.1109/LSP.2014.2360234

    Article  Google Scholar 

  10. Varma, V.S., Lasaulce, S., Hayel, Y., Elayoubi, S.E.: A cross-layer approach for distributed energy-efficient power control in interference networks. IEEE Trans. Veh. Technol. 64(7), 3218–3232 (2015)

    Google Scholar 

  11. Shim, V.A., Tan, K.C., Cheong, C.Y., Chia, J.Y.: Enhancing the scalability of multi-objective optimization via restricted Boltzmann machine-based estimation of distribution algorithm. Inf. Sci. 248, 191–213 (2013)

    Article  MathSciNet  Google Scholar 

  12. Lee, H.J., Hong, K.S.: Class-specific mid-level feature learning with the discriminative group-wise Beta-Bernoulli process restricted Boltzmann machines. Pattern Recognit. Lett. 80, 8–14 (2016)

    Article  Google Scholar 

  13. Gao, J., Yang, J., Wang, G., Li, M.: A novel feature extraction method for scene recognition based on centered convolutional restricted Boltzmann Machines. Neurocomputing 214, 708–717 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. V. Rukmani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rukmani, K.V., Nagarajan, N. Enhanced channel allocation scheme for cross layer management in wireless network based on interference management. Cluster Comput 22 (Suppl 4), 9825–9835 (2019). https://doi.org/10.1007/s10586-017-1596-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1596-7

Keywords

Navigation