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Quantile Regressive Fish Swarm Optimized Deep Convolutional Neural Learning for Reliable Data Transmission in IoV

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Document pages: 17 pages

Abstract: Route path identification on the Internet of Vehicles (IoV) is complicated due to the nature of high dynamic mobility, bandwidth constraints, and traffic load. A vehicle present on the IoV communicates with each other to find the status of the road and location of other vehicles for reliable data transmission. However, the existing routing algorithm does not effectively improve the packet delivery ratio and reduce the delay. To resolve these issues, A Quantile Regressive Fish Swarm Optimized Deep Convolutional Neural Learning (QRFSODCNL) technique is introduced reliable data transmission with minimum end to end delay in IoV. The Do Convolutional Neural Learning uses multiple layers such as one input layer, three hidden layers, and one output layer for vehicle location identification and optimal route path discovery. The different node characteristics of vehicle nodes are analyzed in the hidden layers using the quantile regression function. Depends on the regression analysis, the neighbouring node is identified with minimal time. To improve the throughput and reduce the packet loss rate, the artificial fish swarm optimization technique is applied to choose the best route among the population based on the fitness function. Simulation is carried out to analyze the performance of QRFSODCNL technique and existing methods with different metrics such as packet delivery ratio, packet loss rate, average end to end delay, and throughput. The discussed outcome proves that the QRFSODCNL technique outperforms well as compared to the stateof-the-art methods.

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