An Effective QoS based Route Optimization Model in MANET using Machine Learning

Authors

  • M. Janardhan  M. Janardhan, Prof. S. Pallam Shetty, Prof. Prasad Reddy P.V.G.D
  • Prof. S. Pallam Shetty  M. Janardhan, Prof. S. Pallam Shetty, Prof. Prasad Reddy P.V.G.D
  • Prof. Prasad Reddy P.V.G.D  M. Janardhan, Prof. S. Pallam Shetty, Prof. Prasad Reddy P.V.G.D

Keywords:

Routing Protocols, Internal Attacks , External Attacks ,Manets

Abstract

Mobile Ad hoc network is a network which is dynamic where the mobile nodes form a temporary network in the deficiency of centralized administration, i.e. A MANET is an autonomous group of distributed mobile nodes. Due to the absence of centralized administrator in a network, routing in mobile ad hoc network (MANET) becomes the primary issue which reduces the selection of an optimal path for routing. Specific performance parameters such as latency, overhead, and packet delivery ratio (PDR) are affected unfavorably for which various techniques such as Machine Learning approach are encouraged that enhances the selection of the efficient and stable path. In our, Proposed Research works our attempt is made to select the optimal route i.e. which supports to identify the pattern for Link failure in communication and Optimized routing path for better communication to achieve the QoS for MANET environment using knowledge-based learning algorithm. The optimal path will possess the highest average sum of relay nodes and will be considered as the most optimal and reliable path. We also anticipated that analysis of throughput and PDR is better as compared to the traditional methods.

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International Journal of Scientific Research in Science and Technology

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2018-06-30

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[1]
M. Janardhan, Prof. S. Pallam Shetty, Prof. Prasad Reddy P.V.G.D, " An Effective QoS based Route Optimization Model in MANET using Machine Learning, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 8, pp.426-442, May-June-2018.