Öz
Kablosuz sensör ağları, çeşitli uygulama alanlarında geniş bir yelpazede kullanılmakta olup, düğüm konumlarının doğru bir şekilde belirlenmesi, ağ performansı ve enerji verimliliği açısından kritik öneme sahiptir. Geleneksel yöntemlerin yerini alan meta-sezgisel algoritmalar, daha etkili ve hızlı çözümler sunarak bu alanda önemli avantajlar sağlamaktadır. Bu çalışmada, Parçacık Sürü Optimizasyonu (PSO), Balina Optimizasyon Algoritması (WOA), Monark Kelebeği Optimizasyon Algoritması (MBOA) ve Coatí Optimizasyon Algoritması (COA) kullanılarak kablosuz sensör ağlarında düğüm noktalarının konum tespiti gerçekleştirilmiştir. Önerilen algoritmaların parametreleri belirlenirken, grid-search hiperparametre optimizasyonu gerçekleştirilmiştir. Bulunan optimum parametre değerleriyle birlikte, meta-sezgisel algoritmaların ortalama hata değerleri kıyaslanarak sonuçlar gözlemlenmiştir. Sonuçlar, kullanılan algoritmaların performansını değerlendirerek, en uygun yöntemin seçilmesine olanak tanımaktadır.
Anahtar Kelimeler
Kablosuz Sensör Ağları Coati Optimizasyon Algoritması Konum Tespiti Hiperparametre Optimizasyonu
Kaynakça
- Arora, S., & Singh, S. (2017). Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm. Arabian Journal for Science and Engineering, 42(8), 3325–3335. https://doi.org/10.1007/s13369-017-2471-9
- Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2), 281–305.
- Dehghani, M., Montazeri, Z., Trojovská, E., & Trojovský, P. (2023). Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011. https://doi.org/10.1016/J.KNOSYS.2022.110011
- Gou, P., He, B., & Yu, Z. (2021). A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks. Wireless Communications and Mobile Computing, 2021, 1–17. https://doi.org/10.1155/2021/7523938
- Kanoosh, H. M., Houssein, E. H., & Selim, M. M. (2019). Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks. Journal of Computer Networks and Communications, 2019, 1–12. https://doi.org/10.1155/2019/1028723
- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
- 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
- Mohar, S. S., Goyal, S., & Kaur, R. (2021). Optimized Sensor Nodes Deployment in Wireless Sensor Network Using Bat Algorithm.Wireless Personal Communications, 116(4), 2835–2853. https://doi.org/10.1007/s11277-020-07823-z
- Rajakumar, R., Amudhavel, J., Dhavachelvan, P., & Vengattaraman, T. (2017). GWO-LPWSN: Grey Wolf Optimization Algorithm for Node Localization Problem in Wireless Sensor Networks. Journal of Computer Networks and Communications, 2017. https://doi.org/10.1155/2017/7348141
- Sekhar, P., Lydia, E. L., Elhoseny, M., Al-Akaidi, M., Selim, M. M., & Shankar, K. (2021). An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication. Physical Communication, 48, 101411. https://doi.org/10.1016/j.phycom.2021.101411
- Wang, G. G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014. https://doi.org/10.1007/S00521-015-1923-Y/TABLES/7
- Wang, W., Liu, X., Li, M., Wang, Z., & Wang, C. (2019). Optimizing Node Localization in Wireless Sensor Networks Based on Received Signal Strength Indicator. IEEE Access, 7, 73880–73889. https://doi.org/10.1109/ACCESS.2019.292027
Öz
Wireless sensor networks are utilized in a wide range of applications, where accurate determination of node positions is critical for network performance and energy efficiency. Metaheuristic algorithms, which have replaced traditional methods, offer significant advantages by providing more effective and faster solutions. In this study, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Monarch Butterfly Optimization Algorithm (MBOA), and Coati Optimization Algorithm (COA) were used to determine the positions of nodes in wireless sensor networks. The parameters of the proposed algorithms were determined using grid-search hyperparameter optimization. With the obtained optimal parameter values, the average error values of the metaheuristic algorithms were compared, and the results were observed. The results allow for evaluating the performance of the used algorithms and selecting the most suitable method.
Anahtar Kelimeler
Wireless Sensor Networks Coati Optimization Algorithm Localization Hyperparameter Optimization”
Kaynakça
- Arora, S., & Singh, S. (2017). Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm. Arabian Journal for Science and Engineering, 42(8), 3325–3335. https://doi.org/10.1007/s13369-017-2471-9
- Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2), 281–305.
- Dehghani, M., Montazeri, Z., Trojovská, E., & Trojovský, P. (2023). Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011. https://doi.org/10.1016/J.KNOSYS.2022.110011
- Gou, P., He, B., & Yu, Z. (2021). A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks. Wireless Communications and Mobile Computing, 2021, 1–17. https://doi.org/10.1155/2021/7523938
- Kanoosh, H. M., Houssein, E. H., & Selim, M. M. (2019). Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks. Journal of Computer Networks and Communications, 2019, 1–12. https://doi.org/10.1155/2019/1028723
- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
- 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
- Mohar, S. S., Goyal, S., & Kaur, R. (2021). Optimized Sensor Nodes Deployment in Wireless Sensor Network Using Bat Algorithm.Wireless Personal Communications, 116(4), 2835–2853. https://doi.org/10.1007/s11277-020-07823-z
- Rajakumar, R., Amudhavel, J., Dhavachelvan, P., & Vengattaraman, T. (2017). GWO-LPWSN: Grey Wolf Optimization Algorithm for Node Localization Problem in Wireless Sensor Networks. Journal of Computer Networks and Communications, 2017. https://doi.org/10.1155/2017/7348141
- Sekhar, P., Lydia, E. L., Elhoseny, M., Al-Akaidi, M., Selim, M. M., & Shankar, K. (2021). An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication. Physical Communication, 48, 101411. https://doi.org/10.1016/j.phycom.2021.101411
- Wang, G. G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014. https://doi.org/10.1007/S00521-015-1923-Y/TABLES/7
- Wang, W., Liu, X., Li, M., Wang, Z., & Wang, C. (2019). Optimizing Node Localization in Wireless Sensor Networks Based on Received Signal Strength Indicator. IEEE Access, 7, 73880–73889. https://doi.org/10.1109/ACCESS.2019.292027
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektronik |
| Bölüm | Articles |
| Yazarlar | |
| Erken Görünüm Tarihi | 4 Temmuz 2025 |
| Yayımlanma Tarihi | |
| Gönderilme Tarihi | 25 Temmuz 2024 |
| Kabul Tarihi | 21 Aralık 2024 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 17 Sayı: 2 |
All Rights Reserved. Kırıkkale University, Faculty of Engineering and Natural Science.