Hybrid Extend Particle Swarm Optimization (EPSO) model for Enhancing the performance of MANET Routing Protocols
DOI:
https://doi.org/10.29304/jqcm.2023.15.1.1160Keywords:
Mobile Ad-hoc Network ,, Extend Particle Swarm Optimization,, Network Routing Protocol,, Network congestion optimization,, Network managementAbstract
The routing protocols in MANETs are designed to provide efficient and reliable communication in a highly dynamic and resource-constrained environment. It is very efficient and requires low computational and memory resources compared to most routing protocols. Therefore, mobility and the number of nodes significantly impact the performance and reliability of routing protocols. This paper proposes a hybrid extended particle swarm optimization (EPSO) model to improve the performance of MANET routing protocols. It determines the optimal mobility and the number of hubs and nodes that satisfy the best possible version of MANET. MANET requires a robust routing algorithm that can adapt to a network that arbitrarily changes its topology at any time. The proposed model in the NS2 simulator proves the model's validity in improving the performance of MANET. The proposed model sets the general parameters of routing protocols and achieves high performance with fewer discarded packets and low delay when sending and receiving over MANET. The MANET sent 167 packets in the proposed model, and the number of discarded packets was less than 1%.
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References
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[2] Abdullah, Ako Muhammad, Emre Ozen, and Husnu Bayramoglu. 2019. “Investigating the Impact of Mobility Models on MANET Routing Protocols.” International Journal of Advanced Computer Science and Applications 10(2): 25–35.
[3] Al-Janabi, Samaher, and Ayad Alkaim. 2022. “A Novel Optimization Algorithm (Lion-AYAD) to Find Optimal DNA Protein Synthesis.” Egyptian Informatics Journal 23(2): 271–90. https://www.sciencedirect.com/science/article/pii/S1110866522000044.
[4] Al-saeedi, Ali Hakem. 2016. “Binary Mean-Variance Mapping Optimization Algorithm (BMVMO).” Journal of Applied and Physical Sciences 2(2): 42–47.
[5] Al-Shammary, D., A.L. Albukhnefis, A.H. Alsaeedi, and M. Al-Asfoor. 2022. “Extended Particle Swarm Optimization for Feature Selection of High-Dimensional Biomedical Data.” Concurrency and Computation: Practice and Experience 34(10).
[6] Alabdullah, Murad Ghazy Khalaf, Bassam Mohsin Atiyah, Kaesar Sabah Khalaf, and Saber Hameed Yadgar. 2019. “Analysis and Simulation of Three MANET Routing Protocols: A Research on AODV, DSR & DSDV Characteristics and Their Performance Evaluation.” Periodicals of Engineering and Natural Sciences 7(3): 1228–38.
[7] Alfoudi, A.S. et al. 2022. “Hyper Clustering Model for Dynamic Network Intrusion Detection.” IET Communications.
[8] Alkahtani, Sultan Mohammed, and Fahd Alturki. 2021a. “Performance Evaluation of Different Mobile Ad-Hoc Network Routing Protocols in Difficult Situations.” (February).
[9] Alkahtani, Sultan Mohammed Alturki, Fahd. “Performance Evaluation of Different Mobile Ad-Hoc Network Routing Protocols in Difficult Situations.” International Journal of Advanced Computer Science and Applications 12(1): 158–67.
[10] Alnabhan, Mohammad, Mahmoud Alshuqran, Mustafa Hammad, and Mohammad Al Nawayseh. 2017. “Performance Evaluation of Unicast Routing Protocols in MANETs-Current State and Future Prospects.” International Journal of Interactive Mobile Technologies 11(1): 84–97.
[11] Elbes, Mohammed et al. 2019. “A Survey on Particle Swarm Optimization with Emphasis on Engineering and Network Applications.” Evolutionary Intelligence 12(2): 113–29. http://dx.doi.org/10.1007/s12065-019-00210-z.
[12] García-Nieto, José, and Enrique Alba. 2010. “Automatic Parameter Tuning with Metaheuristics of the AODV Routing Protocol for Vehicular Ad-Hoc Networks.” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6025 LNCS(PART 2): 21–30.
[13] Habboush, Ahmad Khader. 2019. “Ant Colony Optimization (ACO) Based MANET Routing Protocols: A Comprehensive Review.” Computer and Information Science 12(1): 82.
[14] Hadi, Suha Mohammed et al. 2022. “Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System.” International Journal of Intelligent Engineering and Systems 15(5): 252–60.
[15] Hashim, Fatma A et al. 2022. “Honey Badger Algorithm: New Metaheuristic Algorithm for Solving Optimization Problems.” Mathematics and Computers in Simulation 192: 84–110. https://www.sciencedirect.com/science/article/pii/S0378475421002901.
[16] Hernández-García, Ruber, Ricardo J. Barrientos, Cristofher Rojas, and Marco Mora. 2019. “Individuals Identification Based on Palm Vein Matching under a Parallel Environment.” Applied Sciences (Switzerland) 9(14).
[17] Jabor, Ali Hakem, and Ali Hussein Ali. 2019. “Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization.” JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27(1): 171–83.
[18] Jain, Madhavi. 2022. “Increasing Atmospheric Extreme Events and Role of Disaster Risk Management: Dimensions and Approaches.” In Extremes in Atmospheric Processes and Phenomenon: Assessment, Impacts and Mitigation, eds. Pallavi Saxena, Anuradha Shukla, and Anil Kumar Gupta. Singapore: Springer Nature Singapore, 303–28. https://doi.org/10.1007/978-981-16-7727-4_13.
[19] Mahdi, M. A. et al. 2021. “A Multipath Cluster-Based Routing Protocol For Mobile Ad Hoc Networks.” Engineering, Technology & Applied Science Research 11(5): 7635–40.
[20] Manickam, S. et al. 2022. “An Enhanced Mechanism for Detection of Domain Name System-Based Distributed Reflection Denial of Service Attacks Depending on Modified Metaheuristic Algorithms and Adaptive Thresholding Techniques.” IET Networks 11(5): 169–81.
[21] Manickam, Selvakumar et al. 2022. “An Enhanced Mechanism for Detection of Domain Name System‐based Distributed Reflection Denial of Service Attacks Depending on Modified Metaheuristic Algorithms and Adaptive Thresholding Techniques.” IET Networks 11(5): 169–81.
[22] Mansour, Hassnen Shakir et al. 2022. “Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks.” sustainability 14(15): 8980.
[23] Mehdi Ebady Manna, Adil L.Albukhnefis Ali Hakem Alsaeedi Ali Hussein Aljanabi. 2020. “A Proactive Metaheuristic Model for Optimizing Weights of Artificial Neural Network.” Indonesian Journal of Electrical Engineering and Computer Science 20(2): 976–84.
[24] Nuiaa, Riyadh Rahef, Selvakumar Manickam, and Ali Hakem Alsaeedi. 2021. “Distributed Reflection Denial of Service Attack: A Critical Review.” International Journal of Electrical & Computer Engineering (2088-8708) 11(6).
[25] Sarkar, Dipika, Swagata Choudhury, and Abhishek Majumder. 2021. “Enhanced-Ant-AODV for Optimal Route Selection in Mobile Ad-Hoc Network.” Journal of King Saud University - Computer and Information Sciences 33(10): 1186–1201. https://doi.org/10.1016/j.jksuci.2018.08.013.
[26] Vellingiri, Eswaramoorthy, Vinoth Kumar Kalimuthu, and Gopinath Samydurai. 2021. “Fuzzy Logic Based Dsr Trust Estimation Routing Protocol for MANET Using Evolutionary Algorithms.” Tehnicki Vjesnik 28(6): 2006–14.
[27] Xu, Yingkun, Xiaolong Zhou, Shengyong Chen, and Fenfen Li. 2019. “Deep Learning for Multiple Object Tracking: A Survey.” IET Computer Vision 13(4): 411–19.
[28] Yadav, M Uparosiya, N. 2014. “Survey on MANET: Routing Protocols, Advantages, Problems and Security.” International Journal of Innovative Computer Science & Engineering 1(2): 12–17. http://ijicse.in/wp-content/uploads/2014/12/12-17.pdf.
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Published
2023-04-03
How to Cite
Alsaeedi, A. H., Al-Sharqi, M. A., Alkafagi, S. S., Nuiaa, R. R., D. Alfoudi, A. S., Manickam, S., … Otebolaku, A. M. (2023). Hybrid Extend Particle Swarm Optimization (EPSO) model for Enhancing the performance of MANET Routing Protocols. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(1), Comp Page 127–136. https://doi.org/10.29304/jqcm.2023.15.1.1160
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Computer Articles
