Optimizing Access Point Allocation Based on Genetic Algorithm with Channel Conflict Detection
Year 2025,
Volume: 12 Issue: 2, 153 - 166, 01.05.2025
Ramazan Kocaoğlu
,
M. Hanefi Calp
,
M. Ali Akcayol
Abstract
In recent years, high bandwidth and cost-efficient wireless network technologies have emerged as a competition factor in process of constituting own infrastructure. On the other hand, today's challenges of designing an effective layout have become an important problem for public institutions and private companies with increasing requests. There are some methods to solve this problem. In this study, a new approach based on a genetic algorithm is proposed to solve the mentioned problem. A simulation is developed to test the success of the algorithm. The most effective layout design of access points is constituted by the distance between access points and the communication channels used in the developed simulation. The obtained experimental results showed that the proposed algorithm successfully achieved the challenge of designing an access point layout in terms of total coverage area and average bandwidth per user.
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Year 2025,
Volume: 12 Issue: 2, 153 - 166, 01.05.2025
Ramazan Kocaoğlu
,
M. Hanefi Calp
,
M. Ali Akcayol
References
-
[1] J. Lee, B. Han, H. J. Lim, Y. D. Kim, N. Saxena, and T. M. Chung, ‘‘Optimizing access point allocation using genetic algorithmic approach for smart home
environments,’’ The Computer Journal, vol. 52, pp. 938–949, 2009.
-
[2] T. Scully and K. Brown, ‘‘Wireless lan load-balancing with genetic algorithms,’’ Knowledge-Based Systems, vol. 22, pp. 529–534, 2009.
-
[3] W. Yan, S. Xin-xin, and S. Yan-ming, ‘‘Study on application of genetic algorithms in the optimization of wireless network,’’ Procedia Engineering, vol. 16, pp.
348–355, 2011.
-
[4] D. Pandey, R. Dhara, S. Bhunia, and S. Kundu, ‘‘Design and analysis of a compact millimeter-wave pentaband antenna for 5g fr-2 band wireless technologies,’’
International Journal of Electronics and Communications, vol. 184, 2024.
-
[5] E. Oughton, G. Geraci, P. M., V. Shah, B. D., and S. Blue, ‘‘Reviewing wireless broadband technologies in the peak smartphone era: 6g versus wi-fi 7 and 8,’’
Telecommunications Policy, vol. 48, no. 6, 2024.
-
[6] K. Guan, X. Guo, D. He, P. Svoboda, M. Berbineau, S. Wang, B. Ai, Z. Zhong, and M. Rupp, ‘‘Key technologies for wireless network digital twin towards smart
railways,’’ High-speed Railway, vol. 2, no. 1, pp. 1–10, 2024.
-
[7] H. J. Madi, ‘‘Areviewof newdevelopments in reliability and connectivity of wireless sensor technology over recent years.’’ in Comprehensive Materials Processing
(Second Edition). Elsevier, 2024, p. 5700.
-
[8] J. H. Lee, B. J. Han, H. K. Bang, and C. T-M., ‘‘An optimal access points allocation scheme based on genetic algorithm,’’ In Proceedings of IEEE Future Generation
Communication and Networking, vol. 2, pp. 55–29, 2007.
-
[9] J. Yoshino and I. Ohtomo, ‘‘Study on efficient channel assignment method using the genetic algorithm for mobile communication systems,’’ Soft Computing,
vol. 9, no. 2, pp. 143–148, 2005.
-
[10] N. Funabiki, T. Nakanishi, Y. Nomura, T. Farag, S. Tajima, and T. Higashino, ‘‘An optimal access-point allocation for wireless infrastructure mesh networks,’’ In
Proceedings of IEEE International Conference on Computer Theory and Applications, vol. 9, no. 2, pp. 143–148, 2006.
-
[11] D. Turgut, S. K. Das, R. Elmasri, and B. Turgut, ‘‘Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach,’’ In Proceedings
of IEEE Global Telecommunications Conference, vol. 1, pp. 62–66, 2002.
-
[12] A. Singh and W. N. Bhukya, ‘‘A hybrid genetic algorithm for the minimum energy broadcast problem in wireless ad hoc networks,’’ Applied Soft Computing,
vol. 11, no. 1, pp. 667–674, 2011.
-
[13] L. E. Agustin-Blas, S. Salcedo-Sanz, P. Vidales, G. Urueta, and J. A. Portilla-Figueras, ‘‘Near optimal citywide wİfi network deployment using a hybrid grouping
genetic algorithm,’’ Expert Systems with Applications, vol. 38, no. 8, pp. 9543–9556, 2011.
-
[14] E.-S. M. El-Alfy, ‘‘Applications of genetic algorithms to optimal multilevel design of mpls-based networks,’’ Expert Systems with Applications, vol. 30, no. 9, pp.
2010–2020, 2007.
-
[15] L. Calvo-Carro, S. Sanz-Salcedo, J. A. Figueras-Portilla, and E. G. Ortiz-Garcia, ‘‘A genetic algorithm with switch-device encoding for optimal partition of
switched industrial ethernet networks,’’ Journal of Network and Computer Applications, vol. 33, pp. 375–382, 2010.
-
[16] Q. Zhang andW. Zhang, ‘‘Network partition for switched industrial ethernet using genetic algorithm,’’ Engineering Applications of Artificial Intelligence, vol. 20,
pp. 79–88, 2007.
-
[17] S. S. Sanz, J. A. P. Figueras, E. G. O. Garcia, A. M. P. Bellido, C. Thraves, A. F. Anta, and X. Yao, ‘‘Optimal switch location in mobile communication networks
using hybrid genetic algorithms,’’ Applied Soft Computing, vol. 8, pp. 1486–1497, 2008.
-
[18] M. K. Singh, S. I. Amin, and A. Choudhary, ‘‘Genetic algorithm based sink mobility for energy efficient data routing in wireless sensor networks,’’ International
Journal of Electronics and Communications, vol. 131, 2021.
-
[19] A. Ouyang, Y. Lu, Y. Liu, M. Wu, and X. Peng, ‘‘An improved adaptive genetic algorithm based on dv-hop for locating nodes in wireless sensor networks,’’
Neurocomputing, vol. 458, pp. 500–510, 2021.
-
[20] N. T. Hanh, H. T. T. Binh, N. X. Hoai, and M. S. Palaniswami, ‘‘An efficient genetic algorithm for maximizing area coverage in wireless sensor networks,’’
Information Sciences, vol. 488, pp. 58–75, 2019.
-
[21] M. H. Calp, ‘‘A hybrid anfis-ga approach for estimation of regional rainfall amount,’’ Gazi University Journal of Science, vol. 31, no. 1, pp. 145–162, 2019.
-
[22] V. Bertolini, F. Corti, M. Intravaia, A. Reatti, and Cardelli, ‘‘Optimizing power transfer in selective wireless charging systems: A genetic algorithm-based
approach,’’ Journal of Magnetism and Magnetic Materials, vol. 587, 2023.
-
[23] Y. E. M. Hamouda, ‘‘Optimally sensors nodes selection for adaptive heterogeneous precision agriculture using wireless sensor networks based on genetic algorithm
and extended kalman filter,’’ Physical Communication, vol. 63, 2024.
-
[24] L. V. Quan, N. T. Hanh, H. T. T. Binh, V. D. Toan, D. T. Ngoc, and B. T. Lam, ‘‘Optimally sensors nodes selection for adaptive heterogeneous precision agriculture
using wireless sensor networks based on genetic algorithm and extended kalman filter,’’ Expert Systems with Applications, vol. 217, 2023.
-
[25] M. H. Calp and A. M. A., ‘‘Optimization of project scheduling activities in dynamic cpm and pert networks using genetic algorithms,’’ Süleyman Demirel
Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 22, no. 2, pp. 615–627, 2018.
-
[26] D. J. Bahadur and L. Lakshmanan, ‘‘A novel method for optimizing energy consumption in wireless sensor network using genetic algorithm,’’ Microprocessors
and Microsystems, vol. 96, 2023.
-
[27] X. Gong, D. Plets, E. Tanghe, T. D. Pessemier, L. Martens, and W. Joseph, ‘‘An efficient genetic algorithm for large-scale transmit power control of dense and
robust wireless networks in harsh industrial environments,’’ Microprocessors and Microsystems, vol. 65, pp. 243–259, 2018.