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A novel deep deterministic policy gradient model applied to intelligent transportation system security problems in 5G and 6G network scenarios by David Augusto Ribeiro; Dick Carrillo Melgarejo; Muhammad Saadi; Renata Lopes Rosa; Demóstenes Zegarra Rodríguez is a Engineering article available to read on EtoBox.

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Traffic congestion has been an actual problem in large cities, causing personal inconvenience and environmental pollution. To solve this problem, new applications for Intelligent Transportation System (ITS) have been created, to monitor actual traffic conditions. Therefore, fast, reliable and safe systems are desirable for creating a real intelligent transportation environment. Deep learning algorithms have been proposed for a better understanding of traffic behavior from a security-related perspective. Thus, we aim to maximize the safety problems using a deep learning algorithm, where a novel policy gradient model is presented for detecting vehicular misuse. The proposed model uses a triple network replay algorithm, maximizing the network convergence speed. Three networks are selected to optimize the policy network variables. Finally, the replay algorithm is partitioned with the aim of obtaining a faster model. Simulations on a real urban map are performed in a scenario with the integration of 5G or 6G networks. An architectural model for the integration of a Vehicular Ad-hoc Network (VANET) and cellular networks is determined in software-defined networking (SDN). The results show

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Author

David Augusto Ribeiro; Dick Carrillo Melgarejo; Muhammad Saadi; Renata Lopes Rosa; Demóstenes Zegarra Rodríguez

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)