Over the past few decades, Intelligent Transportation System (ITS) has become a vital and extensive element of daily human life and activity. Vehicular Ad hoc Networks (VANETs) have become the most promising components of ITS, which promises to enhance transport efficiency, passenger safety, and comfort by exchanging traffic and infotainment information to intelligent vehicles. Moreover, VANETs have emerged with new paradigms (e.g., Cloud, SDN (Software-Defined Networking), Fog computing, Blockchain, and AI (Artificial Intelligence) techniques) to provide strategic and secure communications to increase their reliability. Therefore, efficient and robust mechanisms, such as trust management, are essential requirements in VANETs. This survey provides an extensive overview of the VANET and trust management’s main concepts. After that, we briefly review existing surveys, followed by the significant challenges of security and trust in VANETs. Then, we identify, review, classify, summarize, and compare related approaches. Finally, we give some future research directions.

Deep Learning is one of the most promising technologies compared to other methods in the context of machine translation. It has been proven to achieve impressive results on large amounts of parallel data for well-endowed languages. Nevertheless, for low-resource languages such as the Arabic Dialects, Deep Learning models failed due to the lack of available parallel corpora. In this article, we present a method to create a parallel corpus to build an effective NMT model able to translate into MSA, Tunisian Dialect texts present in social networks. For this, we propose a set of data augmentation methods aiming to increase the size of the state-of-the-art parallel corpus. By evaluating the impact of this step, we noticed that it has effectively boosted both the size and the quality of the corpus. Then, using the resulted corpus, we compare the effectiveness of CNN, RNN and transformers models to translate Tunisian Dialect into MSA. Experiments show that a better translation is achieved by the transformer model with a BLEU score of 60 vs., respectively, 33.36 and 53.98 with RNN and CNN models.