Nodes in mobile ad-hoc networks can freely roam and communicate with each other across a wireless network in their frequency range. The routes are not stable due to the dynamic topology. As a result, one of the primary challenges is sending data packets between nodes. The methods that are consistent with the network changes caused by node migrations are quite important. Route brevity and route stability should both be considered while minimising data packet transmission time between nodes. Our approach to Artificial Intelligence was widely regarded as a novel development in the world of routing protocols more than a decade ago [1,2]. Reinforcement learning was utilised to pick the resilient routing method by determining the optimal choice among neighbour nodes at any given time to transport data packets from source to destination. It uses reinforcement learning to predict the behaviour pattern of the nodes in respect to the target node. The suggested method uses a Q-learning algorithm to assess the value of actions, which is more homogeneous [3].
Author(S) Details
D. Srinivas Reddy
Department of Computer Science, Vaageswari College of Engineering – Karimnagar, Telangana, India.
V. Bapuji
Department of Computer Science, Vaageswari College of Engineering – Karimnagar, Telangana, India.
A. Govardhan
Department of Computer Science & Engineering, JNTU-Hyderabad, Telangana, India.
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