Deep Learned Ruzicka Similaritive Spectral Clustered Oppositional Dragonfly Optimized Routing In VANET

Abstract

VANET is an Ad–hoc network where all the vehicles move dynamically within the network coverage area and communicate to other vehicles in a single hop or multi-hop through the road-side unit (RSU). Due to the Ad–hoc nature of vehicles, continuously changing their locations and achieving seamless connectivity between the vehicles and communication efficiency in VANETs are the major challenging problems. A new technique called Deep Learned Ruzicka Spectral Clustering-based Oppositional Dragonfly Optimized Routing (DLRSC-ODOR) is introduced for improving the reliability of data dissemination and minimizing the end to end delay in VANET. The DLRSC-ODOR technique uses the deep learning concept comprises an input layer, two hidden layers, and an output layer. The number of vehicle nodes as given to the input layer. Then the inputs are transformed into the first hidden layer. The Ruzicka similarity-based spectral clustering is applied to group the vehicle nodes based on the different mobility parameters such as the density of vehicles, moving direction, distance, and velocity. Followed by, the cluster head is chosen for efficient data dissemination to minimize the delay. Then the clustering results are transformed into the second hidden layer where the optimal cluster head is chosen for disseminating the data packets from source to destination. The Multiattribute oppositional dragonfly optimization technique is applied for finding the global optimum cluster head. Finally, the optimal route path from the source to the destination is established at the output layer. Then the data dissemination is performed to achieve reliability.  Simulation is carried out with different metrics such as reliability, packet drop rate, end to end delay and throughput.  The observed results show that the DLRSC-ODOR technique efficiently improves the reliability, throughput and minimizes the end to end delay as well as packet drop rate than the state-of-the-art methods.