Quantum Computing-Inspired Genetic Algorithm for Network Optimization in WSN

Authors

  • Shakir Mahoomed Abas Department of Computer Science, Cihan University-Duhok, Duhok, Iraq
  • Salar Faisal Noori Department of Computer Science, Cihan University-Duhok, Duhok, Iraq
  • D. Yuvaraj Department of Computer Science, Cihan University-Duhok, Duhok, Iraq
  • S. Shanmuga Priya Department of Computer Science Engineering, SRM Institute of Science and Technology, Trichy, India

Keywords:

Quantum computing, genetic algorithm, network optimization, wireless sensor network (WSN), quantum-inspired routing

Abstract

This study presents a pioneering Quantum Computing-Inspired Genetic Algorithm (QIGA) designed for the efficient optimization of Wireless Sensor Networks (WSN). Leveraging the principles of quantum computing, QIGA employs a unique approach to address the complex routing challenges in WSNs. The algorithm starts with the quantum encoding of candidate routes, utilizing quantum bits (qubits) to represent multiple routes simultaneously through principles like superposition and entanglement. Genetic operations, including crossover and mutation, are then applied in the quantum domain to explore diverse solution spaces. The quantum-encoded routes are subsequently decoded into classical routes, and their fitness is evaluated based on crucial WSN optimization criteria, such as energy efficiency, latency, and reliability. The study integrates quantum-inspired selection strategies to determine the next generation of routes, fostering adaptability and efficiency in the optimization process. Through iterative refinement, QIGA aims to converge towards optimal routing solutions for WSNs. The proposed algorithm showcases a quantum-inspired paradigm that holds promise for addressing the intricate challenges of network optimization in WSNs. The study contributes to the evolving landscape of quantum computing applications in networking and lays the foundation for future advancements in quantum-inspired algorithms tailored for practical implementation in WSN environments.

Downloads

Download data is not yet available.

References

Zhang, Y., & Cai, W. (2022). The Key Technology of Wireless Sensor Network and Its Application in the Internet of Things. Journal of Sensors, 2022, 1-11.

Taghavirashidizadeh, A., Zarei, A. B., & Farsi, A. (2022). Analysis of the attack and its solution in wireless sensor networks. arXiv preprint arXiv:2207.08014.

Kumar, M., Dohare, U., Kumar, S., & Kumar, N. (2023). Blockchain Based Optimized Energy Trading for E-Mobility Using Quantum Reinforcement Learning. IEEE Transactions on Vehicular Technology.

Ding, Q., Zhu, R., Liu, H., & Ma, M. (2021). An overview of machine learning-based energy-efficient routing algorithms in wireless sensor networks. Electronics, 10(13), 1539.

Juárez-Ramírez, R., Navarro, C.X., Jiménez, S. et al. A Taxonomic View of the Fundamental Concepts of Quantum Computing–A Software Engineering Perspective. Program Comput Soft 49, 682–704 (2023). https://doi.org/10.1134/S0361768823080108

Hevia, J. L., Peterssen, G., Ebert, C., & Piattini, M. (2021). Quantum computing. IEEE Software, 38(5), 7-15.

Sigov, A., Ratkin, L., & Ivanov, LA. Quantum Information Technology. Journal of Industrial Information Integration, 28, 100365.

Albadr, M. A., Tiun, S., Ayob, M., & Al-Dhief, F. (2020). Genetic algorithm based on natural selection theory for optimization problems. Symmetry, 12(11), 1758.

Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia tools and applications, 80, 8091-8126.

Xiao, J., and Zhou, J. (2021). Quantum Close Elite Genetic Algorithm- Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks. Journal of Sensors, 2021, 1-14.

Khudair Madhloom, J., Abd Ali, H. N., Hasan, H. A., Hassen, O. A., & Darwish, S. M. (2023). A Quantum-Inspired Ant Colony Optimization Approach for Exploring Routing Gateways in Mobile Ad Hoc Networks. Electronics, 12(5), 1171.

Yu, S., Zhu, J., & Lv, C. (2023). A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks. Sensors, 23(2), 782.

Rani, S., Babbar, H., Kaur, P., & Ali Khan, A. (2023). A novel approach of localization with single mobile anchor using quantum-based Salp swarm algorithm in wireless sensor networks. Soft Computing, 1-15.

Mirhosseini, M., Fazlali, M., Malazi, H. T., Izadi, S. K., & Nezamabadi-pour, H. (2021). Parallel quadri-valent quantum-inspired gravitational search algorithm on a heterogeneous platform for wireless sensor networks. Computers & Electrical Engineering, 92, 107085.

Wang, Z., Ding, H., Li, B., Bao, L., & Yang, Z. (2020). An energy efficient routing protocol based on improved artificial bee colony algorithm for wireless sensor networks. ieee access, 8, 133577-133596.

Downloads

Published

07.02.2024

How to Cite

Abas, S. M. ., Noori, S. F. ., Yuvaraj, D. ., & Priya, S. S. . (2024). Quantum Computing-Inspired Genetic Algorithm for Network Optimization in WSN. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 188–194. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4733

Issue

Section

Research Article

Most read articles by the same author(s)

Similar Articles

You may also start an advanced similarity search for this article.