Cascaded ANN Based Clustering and Optimized Routing Path Selection in Mobile Adhoc Networks
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 6 |
| Year of Publication : 2023 |
| Authors : K. Paul Joshua, D. Srinivasa Rao, Govinda Patil, Mohit Kadwal, Jitendra Choudhary |
10.14445/23488379/IJEEE-V10I6P109
How to Cite?
K. Paul Joshua, D. Srinivasa Rao, Govinda Patil, Mohit Kadwal, Jitendra Choudhary, "Cascaded ANN Based Clustering and Optimized Routing Path Selection in Mobile Adhoc Networks," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 6, pp. 81-93, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I6P109
Abstract:
Mobile Ad Hoc Networks (MANETs) permit wireless communication terminals that establish communication networks at any time and from any location because they do not require any established infrastructure. As a result, MANETs have a high application potential and have become a popular study area in recent years. However, MANETs continue to confront many hard issues that significantly impact their performance and use in real-world scenarios. The two problems involved in handling MANET topology are scalability and energy limitation. In this proposed system, clustering and routing mechanism are employed to resolve these issues. The novel clustering algorithm based on Cascaded Artificial Neural Network and routing path selection uses hydridized Ant Colony Optimization (ACO), and Salp Swarm Optimization (SSO) is proposed to support massive mobile ad hoc networks. A novel clustering technique assists in solving routing protocol issues and improving scalability. Clustering in MANETs offers a robust technique that optimally deploys resources while ensuring network architectural integrity. To examine the proposed system, MATLAB software is used to run simulations. According to the simulation results, the MANET network performance factors such as throughput, Packet delivery ratio, delay, and Average energy have improved.
Keywords:
Cascaded Artificial Neural Network (CANN), Ant Colony Optimization (ACO) and Salp Swarm Optimization (SSO).
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