Mobile Adhoc Network (MANET) is a dynamically reconstruct wireless network with no fixed infrastructure. MANET’s Routing protocols have been discussed widely in recent years. In the network, each node acts as host and a router, then it moves randomly. The implementation of suitable routing method for real time applications in MANET makes it efficient in terms of performance. Such real time applications strictly follow the Quality of Service (QoS) parameters like shortest path, overall throughput, delay, power and stand time. Existing techniques are involved in identification of suitable path and routing packets through them. In this new proposed algorithm to route the packets through a discriminated path based on neural network. Each node is provided with updated node value to determine the efficient path. Reinforcement learning technique is used for weight determination of the host in the MANET. Priority value determines quality packets and identifies packet transmission path. This QoS routing algorithm improves the quality of service interms of overhead, delivery ratio of a packet, security and energy consumption. Simulation is done by using NS2 simulator showed that compared to existing algorithms our proposed one reduces the delay significantly with the increase in packet delivery ratio.
Volume 11 | 10-Special Issue
Pages: 868-877
DOI: 10.5373/JARDCS/V11SP10/20192882