Optimizing QoS parameters using computational intelligence in MANETS

MANETS are heterogeneous, self configuring networks in which topology changes very frequently due to the mobility of nodes. Mobile nature of wireless nodes requires periodic updation of routing information and updating of other parameters. This paper majorly concentrates on the idea of discovering the Quality of service (QoS) parameters in Mobile Ad hoc Networks (MANETS) by providing an hybrid approach to automate and give a provision for the better QOS. In MANETS, mobility makes the connectivity and the behaviour uncertain Automatic Neuro-Fuzzy Inference System (ANFIS) and Kalman Filter are used in this paper to optimize the learning and discovery of ever changing parametric values intelligently and automatically. A number of parameters are actually responsible for the functioning of any MANET, these parameters needs to be filtered and optimized to find out more optimized connectivity. This NeuroFuzzy system gives the speed and intelligence to MANETS, optimum optimality is being provided by the multi-heuristic and stochastic approach of Kalman filtration. The parameter values are checked and analyzed on data set of simulations carried earlier updating enhancements are found.