VANET is a promising technology for intelligent transport systems (ITS). It offers new opportunities aiming at improving the circulation of vehicles on the roads and improving road safety. However, vehicles are interconnected by wireless links and without using any infrastructure, which exposes the vehicular network to many attacks. This paper presents a new solution for the exchange of security keys to protect information exchanged between vehicles. In addition to securing the inter-vehicular communication, the proposed solution has considerably decreased the time for the exchange of keys, thus improving the performance of VANET.

Detecting fish in submarine environment is a challenge due to the properties of the water such as light absorption and scattering. In this work, we present a method for preprocessing images in submarine environment. In the first step, we model the underwater environment as overlapp of two processes. The first process is considered as a Poisson distribution, while the second one is considered as a Gaussian mixture. The resulting distribution is called Poisson-Gaussian mixture (PGM). To estimate the noise parameters, we propose an iterative algorithm based on the expectation maximization approach. This allows us to jointly estimate the scale of the Poisson parameter as well as the standard deviation and the mean of all Gaussian distributions. In order to facilitate the detection of objects, to correct the illumination problem of the scene and to restore the colors, we integrate a color correction algorithm. Finally, detection and localization of fish complete the pre-processing in the images. To obtain medium or small regions, the mean shift algorithm is used with a reduced threshold. In the segmentation process, the proposed detector scan the image region by region. This detector allows to estimate statistically the type of the region (object or non-object). The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms conventional methods.

This article considers the problem of fish monitoring in an underwater environment, where many problems might occur, including occlusion, pose changes, and complexity of the scene. Recognizing fish behavior is very important to develop various types of technologies able to provide more precise estimations and monitoring of fish populations in a long term. In this paper, we propose a novel method for underwater fish monitoring (shape modeling and pose estimation). Two main aspects of underwater image processing will be studied: classification and localisation. Additionally, we extract key point features from fish patterns. The fish position and motion are not sufficient features to avoid scene problems. Skeleton extraction could offer us a large range of additional information. It models an object as a set of points of a certain manifold. The 3-dimensional fish pose, along the track of its 3D motion, could depend on curve segments of the underlying manifold. Faster reccurent conventional neural networks (faster R-CNNs) will be used to extract the fish skeleton in different poses. Also, a 3-dimensional trajectory of multiple fish will be derived using a Kalman filter based on the previous feature matching process. The simulation is made for live fish in a fish tank. Experimental results show that our method outperforms relevant models in terms of precision, achieving a minimal accuracy of 94.2%.