Review: the principle of algorithms in wireless communication networks

Authors

  • Israa Falih Muslm Department of Quality Assurance and Academic Performance, University of Babylon, Iraq

Keywords:

Artificial intelligence, Genetic algorithms, Bird flock algorithm

Abstract

There are many challenges facing artificial intelligence technologies, including the availability and quality of internet service in different locations. This is where network science comes in. The importance of this science lies in its use to address the various obstacles and challenges that prevent us from achieving optimal internet service. Evolutionary algorithms are a branch of artificial intelligence that simulate the natural distribution of some living organisms. These algorithms rely on biological techniques such as reproduction, reassortment, and selection mechanisms, and utilize concepts of biological evolution, such as natural selection, to solve various problems and achieve the best results.

References

Li X, et al., "A review of industrial wireless networks in the context of industry 4.0," Wireless networks. 2017;23:23-41.

Cisco. Cisco Visual Networking Index: Forecast and Trends, 2017C2022.

Rouse M. "Internet of Things (IoT), 2018," ed, 2018.

Gubbi J, et al. "Internet of Things (IoT): A vision, architectural elements, and future directions," Future generation computer systems. 2013;29:1645-1660.

Whitley D. "A genetic algorithm tutorial." Statistics and computing. 1994;4(2):65-85.

Eberhart R, Kennedy J. Particle swarm optimization. Proceedings of the IEEE international conference on neural networks, Citeseer, 1995.

Zedadra O, et al. "Swarm intelligence and IoT-based smart cities: A review," in The Internet of Things for Smart Urban Ecosystems, ed: Springer, 2019, p177-200.

Yang X-S. Nature-inspired metaheuristic algorithms: Luniver press, 2010.

Abdulshahed AM, et al. "The application of ANFIS prediction models for thermal error compensation on CNC machine tools," Applied Soft Computing. 2015;27:158-168.

Abdulshahed A, et al. "A particle swarm optimisation-based Grey prediction model for thermal error compensation on CNC machine tools," in Laser Metrology and Machine Performance XI, LAMDAMAP 2015, Huddersfield, 2015, 369-378.

Kaur M, et al. "Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home," The Journal of Supercomputing, 2019, p1-24.

Yang N, Xiong M, et al. A three dimensional indoor positioning algorithm based on the optimization model. 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, 2017.

Kouhbor S, Ugon J, et al. Optimal placement of access point in WLAN based on a new algorithm. International Conference on Mobile Business (ICMB'05), IEEE, 2005.

Vilović I, Burum N. "Location optimization of wlan access points based on a neural network model and evolutionary algorithms." Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije. 2014;55(3):317-329.

Yigit T, Ersoy M. "Testing and design of indoor WLAN using artificial intelligence techniques." Elektronika ir Elektrotechnika. 2014;20(6):154-157.

Downloads

Published

2025-04-07

How to Cite

Muslm, I. F. (2025). Review: the principle of algorithms in wireless communication networks. Journal of Advance Multidisciplinary Research, 4(2), 09–12. Retrieved from https://www.synstojournals.com/multi/article/view/160

Issue

Section

Articles