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