Bandwidth Optimization Of Wireless Networks Using Artificial Intelligence Technique

References

[1] S. Ahmad, R. M. Nadeem, B. Ehsan, M. Ullah and A. B. Siddique, “Wireless networks throughput enhancement using artificial intelligence,” Indian Journal of Science and Technology, vol. 10, no. 26, pp. 1 – 5, 2017.

[2] S. Orike and T. J. Alalibo, “Comparative analysis of computer network protocols in wireless communication technology,” International Journal of Electronics Communication and Computer Engineering, vol. 10, no. 3, pp. 76 – 85, 2019.

[3] F. Farid, S. Shahrestani, and C. Ruan, “Quality of service concerns in wireless and cellular networks,” Communications of the IBIMA, vol. 2013, pp. 1 – 15, 2013

[4] Z. Afzal, P. A. Shah, K. M. Awan, and Zahoor‑ur‑Rehman, “Optimum bandwidth allocation in wireless networks using differential evolution,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 4, pp. 1401 – 1412, 2019.

[5] J. Chang and H. Chen, "Dynamic-grouping bandwidth reservation scheme for multimedia wireless networks," IEEE Journal on Selected Areas in Communications, vol. 21, no. 10, pp. 1566-1574, 2003

[6] B.I. Bakare and T.J. Alalibo, “Gigabit Fidelity (GI- FI) as future wireless technology in Nigeria,” International Journal of Engineering Science Invention, vol. 7, no. 12, ver.3, pp. 1-6, 2018.

[7] S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, 2016.

[8] Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief. (2018) “LORA: Learning to Optimize for Resource Allocation in wireless networks with few training samples,” [online] arXiv preprint arXiv: 1812.07998. Available: https://arxiv.org/abs/1812.07998

[9] P. Akilandeswari, P. Gopikannan, and C. Balasubramanian, “A bandwidth allocation using genetic algorithm to providing solution for optimization problem in wireless mesh network,” International Journal of Innovation and Scientific Research, vol. 25, no. 2, pp. 445 – 456, 2016.

[10] M. L. Umashankar, and M. V. Ramakrishna, “Optimization techniques in wireless sensor networks: A survey,” International Journal of Scientific Research Engineering & Technology, vol 6, no. 7, pp. 704 – 708, 2017.

[11] L. Wang, W. Wu, J. Qi, and Z. Jia, “Wireless sensor network coverage optimization based on whale group algorithm,” Computer Science and Information Systems, vol. 15, no. 3, pp. 569-583, 2018

[12] C. Jiang, H. Zhang, Y. Ren, Z. Han, K. C. Chen, and L. Hanzo, “Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98- 105, 2017.

@article{1702018,
      author = {Tamuno-omie Joyce Alalibo, Sunny Orike, Promise Elechi},
      title = {Bandwidth Optimization Of Wireless Networks Using Artificial Intelligence Technique},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {9},
      pages = {125-130},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702018.pdf},
      abstract = {Bandwidth allocation and management play a vital role in satisfying the Quality of Service (QoS) requirements for applications and promote the move to user-centric network models. As bandwidth is a scarce resource, conventional methods for bandwidth allocation are gradually been swapped with artificial intelligence methods for better bandwidth utilization.  In this study, the Whale Optimization Algorithm (WOA) was investigated for the provision of optimum allocation of bandwidth in wireless networks. WOA is a recent swarm intelligence method that copies the foraging pattern of humpback whales.  In this study, the bandwidth was allocated to real-time users (RTUs) and non-real-time users while reserving bandwidth for future users. The simulations were implemented in MATLAB and the results were discussed in terms of connection probability with a focus on available bandwidth and the numbers of RTUs on the network. From the results, the proposed WOA technique efficiently optimized the bandwidth allocated to users and showed bandwidth management of the small amount of bandwidth.},
      keywords = {Whale Optimization Algorithm, bandwidth allocation, quality of service, wireless network, connection probability },
      month = {March},
  }