FREE HIT A Novel History based Reinforcement Approach for Fast Path Construction in Vehicular Ad hoc Networks
Vehicular ad hoc networks playing significant role in development of intelligent transport system and rise in demand for accessing fascinated applications such as entertainment and advertisements in vehicles via internet leverages to build efficient routing mechanisms. Due to rapid vehicles flow in Vanets, network often subjected to link breaks and creates delay in communication which affects overall network performance, hence to address these serious issue earlier approaches focused on parameters like rate estimation, feedback and link-expiration-time but being different from earlier our Instant Look up and Immediate Action(ILU-IA) technique uses adaptive learning thru past knowledge. ILU-IA identifies immediate neighbour (Free-Hit-Node) efficiently at point of link failure using Case- Based-Learning and Q-learning techniques. Simulation analysis shows that proposed approach performance improved in terms of throughput with negligible delay also reduces network overhead.
