End to End Delay using Aodv-Artificial Neural Networks (Ann) To Improve Performance of Manets
Amarjit Singh1, Tripatdeep Singh2
1Amarjit Singh, Department of Computer Applications from IKG Punjab Technical University.
2Dr. Tripat Deep Singh, Department of Computer Applications from Punjab Technical University, Jalndhar.

Manuscript received on 08 April 2019 | Revised Manuscript received on 16 May 2019 | Manuscript published on 30 May 2019 | PP: 662-666 | Volume-8 Issue-1, May 2019 | Retrieval Number: F2789037619/19©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: MANETs consist of the nodes that move continuously in the random directions. The frequent topology changes leads to broken links which increases the delay in sending the data to the end node. In the traditional direction-finding protocols such as AODV, DSR or DSDV, do not focus on reducing the back-to-back stoppage of the transmitted packets and remaining energy of the network. In healthcare applications where the urgency of the data is on the highest priority, the routing protocol that can reduce the back-to-back delay is required. To get better quality of service (Qos). We have implemented the AODV-ANN predicts the delay dependencies based on distance between two nodes, relative mobility and congestion index and it helps in choosing the energy efficient and delay aware optimized path to send data from starting place to end node. The presentation of the system has been computed based in back-to-back stoppage, remaining energy of the system. The proposed AODV-ANN predicts the enhancement of the (quality of service) networks. The AODV-ANN simulation is better results and increase the network lifetime.
Keyword: MANETs, ANN, Congestion Index, Network Lifetime

Scope of the Article: Network Performance; Protocols; Sensors