Optimal Fault Tolerance Using Intuitionistic Fuzzy and Selection of Cluster Head in MANET - SP , Swornambiga (International Journal of Computer Sciences and Engineering, 2019)
In recent years exploration of wireless devices with small in size and platforms based on processing mobile have bring great attention towards the ad hoc networks. These Ad hoc networks are generally consists of temporary links connected among the nodes. MANET is such a dynamic network which compromises many good qualities to handle the wireless communication devices which are roaming randomly. MANET holds certain restriction on them because of the energy, battery consumption, load management, etc. Due to the nodes mobility nature and the characteristic of error prone nature the wireless medium pretense a lot of confronts, together with recurrent route change and packet losses, in the way of conferencing the necessities of QoS. Such disputes amplify packet delay, reduces throughput and lesser network failure. The first phase adapts the behavior of ants to discover the route and the intuitionistic fuzzy estimation is used to find the direction for searching the optimal route and the fault occurred in the node or link is handled using the check point and fault manger. In nature MANET are logically realized as a set of clusters by grouping together nodes which are in close proximity with one another or through another wireless node. The nodes communicate to the base station directly for data transfer. But it is not efficient in mobile environment because of the energy restriction on each node. The second phase focuses on such issue by creating the cluster topology to group similar nodes by adapting intuitionistic fuzzy k means and electing the cluster heads using the evolutionary algorithms known as genetic algorithm. Then elected cluster head collects the data from other nodes within the cluster and aggregates the data packets for transferring it to the base station directly or by gateway or cluster heads of other clusters. The simulation model for each phase is deployed using MAT lab and the outcome of the result shows that in each phase the proposed techniques performs better than other existing techniques discussed in each phase due to their enhancement in the proposed techniques.