A Soft Computing Approach to Analyse Aodv Routing Protocol
Rafia Kulsum1, Santosh Anand2, Somnath Sinha3
1Rafia Kulsum, Department of Computer Science, Amrita School of Arts and Sciences,Amrita Vishwa Vidyapeetham, Mysuru, (Karnataka), India.
2Santosh Anand, Department of Computer Science, Amrita School of Arts and Sciences,Amrita Vishwa Vidyapeetham, Mysuru, (Karnataka), India.
3Somnath Sinha, Department of Computer Science, Amrita School of Arts and Sciences,Amrita Vishwa Vidyapeetham, Mysuru, (Karnataka), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 1443-1446 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7513068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mobile ad hoc networks (MANETs) conceivably considered the latest model or framework for mobile wireless infrastructure – less systems. Mobile ad hoc networks are widely recognized, its mechanization has attracted a huge range of diverse software. The main challenge a MANET faces is routing, which is caused because of the arbitrary differences that occur with the structure of the topology, considering the lack of centralization that results in the generation of unsystematic and recurrent motion of the nodes. Use of NN within this field is very rare. We assess the capability of proactive and reactive routing protocols, and Destination Sequenced Distance Vector (DSDV) and Ad hoc on Demand Distance Vector (AODV) and Dynamic Source Routing (DSR)using soft computing. The performance is analyzed with respect to Distance, Density and System Throughput. A trained NN is used to analyze the performance of any given unknown communication protocol in MANET.
Keyword: MANET, Neural Network, Back Propagation, AODV Routing Protocol, NS2.
Scope of the Article: Grid Networking/ Computing.