TAVLAMA BENZETİMİ ALGORİTMASI İLE GENİŞ ÖLÇEKLİ KABLOSUZ ALGILAYICI AĞLARDA LEACH PROTOKOLÜNÜN OPTİMİZASYONU

Yıl 2020, Cilt: 8 Sayı: 5, 67 - 79, 29.12.2020

Gülşah Gülbaş Gürcan Çetin

https://doi.org/10.21923/jesd.824663

Öz

Kablosuz Algılayıcı Ağ (KAA) yapılarında kullanılan algılayıcı düğümler enerji, hız ve bellek kullanımı açısından sınırlı kapasiteye sahiptirler. Bu sınırlı kapasite KAA’larda her bir veri gönderim çevriminde azalmakta ve sonunda KAA kullanılamaz duruma gelmektedir. Bu çalışmada, LEACH yönlendirme protokolünü kullanan KAA’larda algılayıcı düğümlerin enerji kayıplarını azaltmak ve KAA’nın aktif kalma süresini arttırmak için Tavlama Benzetimi (TB) algoritmasına dayalı bir yöntem sunulmuştur. Yapılan çalışmada, her bir veri aktarım çevriminde kullanılan küme başlarının seçimi LEACH protokolü ile gerçekleştirilmiş, sonrasında ise TB algoritması kullanılarak, seçilen küme başı düğümlerden daha iyi komşu düğümler olup olmadığı araştırılmıştır. Test çalışmalarında, algılayıcı sayısı 100 olan, geniş ölçekli KAA modelleri seçilmiştir. Geliştirilen algoritmanın başarımı; ağın her bir veri aktarımı çevriminde tükettiği enerji ve ağın aktif olduğu süre boyunca gönderdiği veri miktarı açısından MATLAB R2015b yazılımı kullanılarak değerlendirilmiştir. Çalışma sonucunda algılayıcı sayısı 100 olan geniş ölçekli ağlarda ağın toplam yaşam süresi açısından %82 ve veri aktarımı açısından %72.2 verim elde edilmiştir.

Anahtar Kelimeler

Enerji Optimizasyonu, LEACH Protokolü, Kablosuz Algılayıcı Ağ, Tavlama Benzetimi, Paket Kaybı

OPTIMIZATION OF THE LEACH PROTOCOL ON WIDE-SCALE WIRELESS SENSOR NETWORKS WITH SIMULATED ANNEALING ALGORITHM

Öz

Sensor nodes used in Wireless Sensor Network (WSN) structures have limited capacity in terms of energy, speed and memory usage. This limited capacity decreases with each data delivery cycle in WSN, and eventually, WSN becomes unavailable. In this study, a method based on the Simulated Annealing (SA) algorithm was presented to increase the duration of WSNs active stay, and reduce the energy losses of sensor nodes in WSNs using the LEACH routing protocol. In the study, the selection of cluster heads used in each data transfer cycle was performed using the LEACH protocol and then using the SA algorithm, it was investigated whether there were better neighboring nodes than the selected cluster heads. In the tests, WSN models with 100 sensors and large-scale were selected. In the network model, the sensor nodes are randomly distributed over an area of 100x100m. The success of the developed algorithm was evaluated using MATLAB R2015b software in terms of the energy the network consumes in each data transfer cycle and the amount of data it sends during the time the network is active. As a result of the study, the efficiency of 82% in terms of the total lifetime of the network and 72.2% in terms of data transfer was achieved in large-scale networks with 100 sensors.

Anahtar Kelimeler

Energy Optimization, LEACH Protocol, Wireless Sensor Networks, Simulated Annealing, Packet Loss

Kaynakça

• Ab Wahab, M. N., Mezinani, S. N., Atyabi, A., “A comparative review on mobile robot path planning: Classical or meta-heuristic methods?”, Annual Reviwes in Control, 50, pp. 233-252, 2020.
• Aguitoni, M.C., Pavao, L.V., Ravagnani, M., 2019. Heat Exchanger Network Synthesis Combining Simulated Annealing and Differential Evolution. Energy, 81, 654-664.
• Ahmad, M., Ikram, A.A., Lela, R., Wahid, I., Ulla, R., 2017. Honey Bee Algorithm–Based Efficient Cluster Formation and Optimization Scheme in Mobile Ad Hoc Networks. International Journal of Distributed Sensor Networks. 13(6), 1–12.
• Anastasi, G., Conti, M., Francesco, M., Passarella, A., 2009. Energy Conservation in Wireless Sensor Networks: Ad Hoc Networks, 7, 537-568.
• Asha, G.R., Gowrishankar, 2018. Energy Efficient Clustering and Routing in a Wireless Sensor Networks: Procedia Computer Science. 134, 178–185.
• Cerny, V.,1985. A Thermodynamical Approach to The Traveling Salesman Problem: An Efficient Simulation Algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51.
• Çelik, Y., Yildiz, İ., Karadeniz, A. T., “Son Üç Yılda Geliştirilen Metasezgisel Algoritmalar Hakkında Kısa Bir İnceleme”, European Journal of Science and Technology, pp. 463-477, 2019.
• Dong, Y., Zhang, S., Dong, Z., Cui, Y., 2011. ZigBee based Energy Efficient Reliable Routing in Wireless Sensor Network: Study and Application. In IEEE 3rd International Conference on Communication Software and Networks. 464-467.
• Dorigo, M.,and Gambardella, L. M. "Ant colonies for the traveling salesman problem", BioSystems, vol. 43, no. 2, pp. 73-81, 1997.
• Erdelj, M., Mitton, N., Natalizio, E., 2013. Applications of Industrial Wireless Sensor Networks. Güngör, Ç., Hancke, G.P., (Edt.), Industrial Wireless Sensor Networks içinde (s.3-27) Taylor & Francis.
• Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H., 2000. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. 2, 10.
• Ihsan, A., Saghar, K., Fatima, T., Hasan, O., 2019. Formal Comparison of LEACH and Its Extensions. Computer Standards & Interfaces, 119-127.
• Javidrad, F., Nazari, M., 2017. A New Hybrid Particle Swarm and Simulated Annealing Stochastic Optimization Method. Applied Soft Computing, 60, 634-654.
• Karaboğa, D., “An Idea Based On Honey Bee Swarm For Numerical Optimization”, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
• Kaveh, A., Hamedani, K. B., Hosseini, S., M., Bakhshpoori, T., “Optimal design of planar steel frame structures utilizing meta-heuristic optimization algorithms”, Structures, 25, pp. 335-346, 2020.
• Kennedy, J., and Eberhart, R., "Particle swarm optimization", Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, 4, pp. 1942-1948, 1995.
• Kirkpatrick, S., Gelatt, C. D., Vecchi, M.P., 1983. Optimization by Simulated Annealing. Science, 220, 671–680.
• Madhu, A., Sreekumar, A., 2014. Wireless Sensor Network Security in Military Application using Unmanned Vehicle. International Journal of Electronics and Communication Engineering. 51-58.
• Mehra, P.S., Doja, M. N., Alam, B., 2020. Fuzzy Based Enhanced Cluster Head Selection (FBECS) for WSN. Journal of King Saud University – Science. 32, 390-401.
• Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A., Teller, E., 1953. Equation of State Calculations by Fast Computing Machines. J. Chem. Phys. 21, 1087–1092.
• Mostafaie, T., Kyihabani, F. M., Navimipour, N. J., “A systematic study on meta-heuristic approaches for solving the graph coloring problem”, Computers & Operations Research, 120, 2020.
• Radhika, S., Pangarajan, P., 2019. On Improving the Lifespan of Wireless Sensor Networks with Fuzzy Based Clustering and Machine Learning Based Data Reduction. Applied Soft Computing Journal. 83, 1-9.
• Rajput, A., Kumaravelu, V.B., 2019. Scalable and Sustainable Wireless Sensor Networks for Agricultural Application of Internet of Things Using Fuzzyc-Means Algorithm. Sustainable Computing: Informatics and Systems. 22, 62–74.
• Ramluckun, N., Bassoo, V., 2020. Energy-Efficient Chain-Cluster Based Intelligent Routing Technique for Wireless Sensor Networks. Applied Computing and Informatics.
• Rashedi, E., Nezamabadi, H., Saryazdi, S., “GSA: A Gravitational Search Algorithm”, Information Sciences, 179, 2232-2248, 2009.
• Shieh, H.L., Kuo, C.C., Chiang, C.M., 2011.Modified Particle Swarm Optimization Algorithm with Simulated Annealing Behavior and Its Numerical Verification. Applied Mathematics and Computation, 218, 4365-4383.
• Sivakumar, P., Radhika, M.,2018. Performance Analysis of LEACH-GA over LEACH and LEACH-C in WSN. Procedia Computer Science, 125, 248–256.
• Sodeifian, G., Sajadian, S.A., Ardestani, N.S., 2017. Experimental Optimization and Mathematical Modeling of The Supercritical Fluid Extraction of Essential Oil from Eryngium Billardieri: Application of simulated annealing (SA) algorithm. The Journal of Supercritical Fluids, 127, 146-157.
• Thangaramya,K., Kulothungan, K., Logambigai,R., Selvi, M., Ganapathy, S. and Kannanc, A., “Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT,” Computer Networks, pp. 211-223, 2019.
• Özkaraca O, Keçebaş A, “Performance analysis and optimization for maximum exergy efficiency of a geothermal power plant using gravitational search algorithm”, Energy Conversion and Management, 185, pp. 155-168, 2019.
• Özkaraca Osman, Keçebaş Ali, Demircan Cihan, 2018. Comparative thermodynamic evaluation of a geothermal power plant by using the advanced exergy and artificial bee colony methods. ENERGY, 156, pp. 169-180, 2018.
• Özkaraca Osman, 2018. A comparative evaluation of Gravitational Search Algorithm (GSA) against Artificial Bee Colony (ABC) for thermodynamic performance of a geothermal power plant. Energy, 1665, pp. 1061-1077, 2018.
• Özkaraca Osman, Keçebaş Pınar, Demircan Cihan, Keçebaş Ali, 2017. Thermodynamic Optimization of a Geothermal- Based Organic Rankine Cycle System Using an Artificial Bee Colony Algorithm. Energies, 10(11), 2017.
• Uysal, M., Özcan, U., 2019. Süpermarket Yerleşim Problemi İçin Tavlama Benzetimi Algoritması Yaklaşımı. Karadeniz Fen Bilimleri Dergisi, 9(1), 58-69.
• Yadav, A., Kumar, S., Vijendara, S., 2018. Network Life Time Analysis of WSNs Using Particle Swarm Optimization. Procedia Computer Science. 132, 805–815.
• Yang, B., Wang, J., Zhang, X., Tu, T., Yao, W., Shu, S., Zeng, F., Sun, L., “Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification”, Energy Conversion and Management, 208, 2020.
• Yang, X S., Chien, S F., Ting, T O., “Bio-Inspired Computation and Optimization: An Overview”, in Bio-Inspired Computation in Telecommunications, pp. 1-21, 2015.
• Zhang, W., Maleki, A., Rosen, M.A., Liu, J., 2018. Optimization with a Simulated Annealing Algorithm of a Hybrid System for Renewable Energy Including Battery and Hydrogen Storage. Energy, 163, 191-207.