A NOVEL PENDING INTEREST TABLE SHARING SCHEME USING NEURO FUZZY LOGIC FOR NAMED DATA NETWORKING COMMUNICATION

At present times, Information-Centric Networking (ICN) becomes familiar as a distinct standard for nextgeneration Internet and exhibits the significance of restoring the present host-centric model. The data transmission in ICN is based on the name of the contents instead of the addresses of the host. Besides, named data networking (NDN) is another hot research area, which permits the user to request data with no earlier details relevant to the hosting entity. Though earlier studies on NDN offers mobility and security over the classical Internet, it suffers from the problem of pending interest table (PIT) management. Therefore, a proficient PIT management strategy is essential for the utilization of PIT memory space. For effective management of the existing PIT memory space and improvise the cache usage, a novel PIT sharing strategy using neuro fuzzy logic called NFPIT is presented. In order to select an optimal friendly node (FN) for the requestor node (RN) which has less amount of PIT space, neuro fuzzy method will be executed. In addition, we employ a deep learning model by employing the Convolution Neural Network (CNN) for rule generation. The proposed method is simulated using NS3 simulator and the simulation outcome has been experimented under different aspects. The experimental values ensure the superiority of the NFPIT method by achieving a maximum cache hit ratio of 56.4%, content diversity of 67% with less content delivery time of 7.26ms.