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Title: :  Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks
PaperId: :  22360
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 6 2023
DUI:    16.0415/IJARIIE-22360
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Dr P Rizwan Ahmed Singhania University

Abstract

CSE
Machine Learning, Dynamic Routing Protocols, Mobile Ad Hoc Networks
In the realm of mobile ad hoc networks (MANETs), the dynamic nature of wireless communication demands adaptive and efficient routing protocols to ensure optimal performance. Traditional routing algorithms often face challenges in dynamically changing environments, leading to suboptimal efficiency and increased communication overhead. This research proposes a novel approach by integrating machine learning techniques into the design of dynamic routing protocols for MANETs. The primary objective of this study is to enhance the efficiency of communication in MANETs through the application of machine learning algorithms. By leveraging real-time data and learning patterns from network behaviors, the proposed dynamic routing protocols adapt to changing network conditions, optimizing the selection of paths for data transmission. This adaptive capability is particularly crucial in MANETs, where nodes constantly move, join, or leave the network, affecting the topology dynamically.To achieve this, the research focuses on developing machine learning models that can accurately predict the performance of available routes based on historical and current network information. The integration of these models into dynamic routing protocols enables the network to make intelligent decisions on route selection, considering factors such as link quality, traffic load, and node mobility. The machine learning-driven approach aims to minimize latency, reduce packet loss, and enhance overall network efficiency. The experimental evaluation of the proposed framework involves simulations and real-world MANET deployments to assess its performance under various scenarios. Results indicate significant improvements in terms of reduced end-to-end delays, enhanced packet delivery ratios, and better adaptability to dynamic network conditions compared to traditional routing protocols. In conclusion, the integration of machine learning into dynamic routing protocols presents a promising avenue for addressing the challenges of efficiency in mobile ad hoc networks. This research contributes to the advancement of adaptive communication protocols, paving the way for more resilient and responsive wireless networks in dynamic environments.

Citations

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IJARIIE Dr P Rizwan Ahmed . "Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 6 2023 Page 2562-2568
MLA Dr P Rizwan Ahmed . "Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks." International Journal Of Advance Research And Innovative Ideas In Education 9.6(2023) : 2562-2568.
APA Dr P Rizwan Ahmed . (2023). Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks. International Journal Of Advance Research And Innovative Ideas In Education, 9(6), 2562-2568.
Chicago Dr P Rizwan Ahmed . "Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 6 (2023) : 2562-2568.
Oxford Dr P Rizwan Ahmed . 'Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 6, 2023, p. 2562-2568. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Machine_Learning_Driven_Dynamic_Routing_Protocols_for_Enhanced_Efficiency_in_Mobile_Ad_Hoc_Networks_ijariie22360.pdf (Accessed : 07 February 2024).
Harvard Dr P Rizwan Ahmed . (2023) 'Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks', International Journal Of Advance Research And Innovative Ideas In Education, 9(6), pp. 2562-2568IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Machine_Learning_Driven_Dynamic_Routing_Protocols_for_Enhanced_Efficiency_in_Mobile_Ad_Hoc_Networks_ijariie22360.pdf (Accessed : 07 February 2024)
IEEE Dr P Rizwan Ahmed , "Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 6, pp. 2562-2568, Nov-Dec 2023. [Online]. Available: http://ijariie.com/AdminUploadPdf/Machine_Learning_Driven_Dynamic_Routing_Protocols_for_Enhanced_Efficiency_in_Mobile_Ad_Hoc_Networks_ijariie22360.pdf [Accessed : 07 February 2024].
Turabian Dr P Rizwan Ahmed . "Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 6 (07 February 2024).
Vancouver Dr P Rizwan Ahmed . Machine Learning-Driven Dynamic Routing Protocols for Enhanced Efficiency in Mobile Ad Hoc Networks. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 07 February 2024]; 9(6) : 2562-2568. Available from: http://ijariie.com/AdminUploadPdf/Machine_Learning_Driven_Dynamic_Routing_Protocols_for_Enhanced_Efficiency_in_Mobile_Ad_Hoc_Networks_ijariie22360.pdf
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