Performance analysis in direction oriented graded cognitive network using Bayesian model approach for path determination

Some of the challenges involved in current Internet routing are limitations of the process enabling routing techniques, handling of explosion of messages and absence of awareness about the environment. This paper presents a comparative analysis using Bayesian model of a network having a randomly distributed quality parameters when subjected to quality grading and direction oriented for optimal path determination. Optimal path determination was performed upon self aware nodes using Memetic algorithm and ABC. The agents distributed among the nodes accumulate relevant information about itself and neighbouring nodes. The grading operation makes use of the agents to determine the quality of service information of the node in the network. The scheme has been simulated on various network topology for performance analysis of direction oriented graded network in terms of throughput and end-to-end delay. It has been found that graded cognitive network exhibits more flexibility and adaptability for facilitating routing.