Hybrid whale optimisation algorithm for energy efficient cognitive radio network

References

• Aljarah, I., Faris, H., & Mirjalili, S. (2018). Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Computing, 22(1), 1–15. https://doi.org/10.1007/s00500-016-2442-1
   Web of Science ®Google Scholar
• Anamalamudi, S., Rashid Sangi, A., Alkatheiri, M., & Mohammed Ahmed, A. (2018). AODV routing protocol for cognitive radio access based internet of things (IoT). Future Generation Computer Systems, 83, 228–238. https://doi.org/10.1016/j.future.2017.12.060
   Web of Science ®Google Scholar
• Aslam, S., Ejaz, W., & Ibnkahla, M. (2018). Energy and spectral efficient cognitive radio sensor networks for Internet of Things. IEEE Internet of Things Journal, 5(4), 3220–3233. https://doi.org/10.1109/JIOT.2018.2837354
   Web of Science ®Google Scholar
• Bin Zikria, Y., Ishmanov, F., Khalil Afzal, M., Won Kim, S., Yeob Nam, S., & Heejung, Y. (2018). Opportunistic channel selection MAC protocol for cognitive radio ad hoc sensor networks in the internet of things. Sustainable Computing: Informatics and Systems, 18, 112–120. https://doi.org/10.1016/J.SUSCOM.2017.07.003
   Google Scholar
• Fayyaz Qureshi, F., Iqbal, R., & Nabeel Asghar, M. (2017). Energy efficient wireless communication technique based on cognitive radio for internet of. Journal of Network and Computer Applications, 89(C), 14–25. https://doi.org/10.1016/j.jnca.2017.01.003
   Web of Science ®Google Scholar
• Galvez, A., & Iglesias, A. (2016). New memetic self-adaptive firefly algorithm for continuous optimisation. International Journal of Bio-Inspired Computation, 8(5), 300–317. https://doi.org/10.1504/IJBIC.2016.079570
   Web of Science ®Google Scholar
• Harizan, S., & Kuila, P. (2019). Coverage and connectivity aware energy efficient scheduling in target based wireless sensor networks: An improved genetic algorithm based approach. Wireless Networks, 25(4), 1995–2011. https://doi.org/10.1007/s11276-018-1792-2
   Web of Science ®Google Scholar
• Hashemi Mehne, H., & Mirjalili, S. (2018). A parallel numerical method for solving optimal control problems based on whale optimization algorithm. Knowledge-Based Systems, 151, 114–123. https://doi.org/10.1016/j.knosys.2018.03.024
   Web of Science ®Google Scholar
• Jiao, Y., & Joe, I. (2016). Energy-efficient resource allocation for heterogeneous cognitive radio network based on two-tier crossover genetic algorithm. Journal of Communications and Networks, 18(1), 112–122. https://doi.org/10.1109/JCN.2016.000014
   Web of Science ®Google Scholar
• Kaveh, A., & Ilchi Ghazaan, M. (2017). Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mechanics Based Design of Structures and Machines, 45(3), 345–362. https://doi.org/10.1080/15397734.2016.1213639
   Web of Science ®Google Scholar
• Liu, X., & Zhang, X. (2018). Rate and energy efficiency improvements for 5G-based IoT with simultaneous transfer. IEEE Internet of Things Journal, 6(4), 5971–5980. https://doi.org/10.1109/JIOT.2018.2863267
   Web of Science ®Google Scholar
• Liu, X., & Zhang, X. (2019). NOMA-based resource allocation for cluster-based cognitive industrial internet of things. IEEE Transactions on Industrial Informatics, 16(8), 5379–5388. https://doi.org/10.1109/TII.2019.2947435
   Web of Science ®Google Scholar
• Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
   Web of Science ®Google Scholar
• Olatomiwa, L., Mekhilef, S., Shamshirband, S., Mohammadi, K., Petković, D., & Sudheer, C. (2015). A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy, 115, 632–644. https://doi.org/10.1016/j.solener.2015.03.015
   Web of Science ®Google Scholar
• Ozger, M., Alagoz, F., & Akan, O. B. (2018). Clustering in multi-channel cognitive radio ad hoc and sensor networks. IEEE Communications Magazine, 56(4), 156–162. https://doi.org/10.1109/MCOM.2018.1700767
   Web of Science ®Google Scholar
• Raikel, B., Montejo Sánchez, S., Baraldi Mafra, S., Demo Souza, R., Luiz Rebelatto, J., & Martin Garcia Fernandez, E. (2016). Energy efficient power allocation schemes for a two-user network-coded cooperative cognitive radio network. IEEE Transactions on Signal Processing, 64(7), 1654–1667. https://doi.org/10.1109/TSP.2015.2507550
   Web of Science ®Google Scholar
• Ren, J., Junying, H., Zhang, D., Guo, H., Zhang, Y., & Shen, X. (2018). RF energy harvesting and transfer in cognitive radio sensor networks: Opportunities and challenges. IEEE Communications Magazine, 56(1), 104–110. https://doi.org/10.1109/MCOM.2018.1700519
   Web of Science ®Google Scholar
• Sadeghian Kerdabadi, M., Ghazizadeh, R., Farrokhi, H., & Najimi, M. (2019). Energy consumption minimization and throughput improvement in cognitive radio networks by joint optimization of detection threshold, sensing time and user selection. Wireless Networks, 25(4), 2065–2079. https://doi.org/10.1007/s11276-018-1797-x
   Web of Science ®Google Scholar
• Shaowei, W., Shi, W., & Wang, C. (2015). Energy-efficient resource management in OFDM-based cognitive radio networks under channel uncertainty. IEEE Transactions on Communications, 63(9), 3092–3102. https://doi.org/10.1109/TCOMM.2015.2452251
   Web of Science ®Google Scholar
• Syed, T., & Safdar, G. (2020). Energy-Efficient GCSA medium access protocol for infrastructure-based cognitive radio networks. IEEE Systems Journal, 14(1), 288–297. https://doi.org/10.1109/JSYST.2019.2917836
   Web of Science ®Google Scholar
• Tang, M., & Xin, Y. (2016). Energy efficient power allocation in cognitive radio network using coevolution chaotic particle swarm optimization. Computer Networks, 100, 1–11. https://doi.org/10.1016/j.comnet.2016.02.010
   Web of Science ®Google Scholar
• Wang, H., Wang, W., Zhou, X., Sun, H., Zhao, J., Xiang, Y., & Cui, Z. (2017). Firefly algorithm with neighborhood attraction. Information Sciences, 382(C), 374–387. https://doi.org/10.1016/j.ins.2016.12.024
   Web of Science ®Google Scholar
• Yang, Z., Han, R., Chen, Y., & Wang, X. (2018). Green-RPL: An energy-efficient protocol for cognitive radio enabled AMI network in smart grid. IEEE Access, 6, 18335–18344. https://doi.org/10.1109/ACCESS.2018.2812191
   Web of Science ®Google Scholar
• Zhang, D., Chen, Z., Khattar Awad, M., Zhang, N., Zhou, H., & Sherman Shen, X. (2016). Utility-optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks. IEEE Journal on Selected Areas in Communications, 34(12), 3552–3565. https://doi.org/10.1109/JSAC.2016.2611960
   Web of Science ®Google Scholar
• Zhang, X., Hongxiang, L., Yanhui, L., & Zhou, B. (2015). Distributed energy efficiency optimization for MIMO cognitive radio network. IEEE Communications Letters, 19(5), 847–850. https://doi.org/10.1109/LCOMM.2015.2414415
   Web of Science ®Google Scholar
• Zhang, H., Nie, Y., Cheng, J., Leung, V. C. M., & Nallanathan, A. (2017). Sensing time optimization and power control for energy efficient cognitive small cell with imperfect hybrid spectrum sensing. IEEE Transactions on Wireless Communications, 16(2), 730–743. https://doi.org/10.1016/j.ins.2016.12.024
   Web of Science ®Google Scholar
• Zhou, M., Zhao, X., & Yin, H. (2019). A robust energy-efficient power control algorithm for cognitive radio networks. Wireless Networks, 25(4), 1805–1814. https://doi.org/10.1007/s11276-017-1631-x
   Web of Science ®Google Scholar