Vehicular ad hoc network (VANET) nodes are characterized by their high mobility and by exhibiting different mobility patterns. Therefore, VANET clustering schemes are required to account for the mobility parameters among neighboring nodes to produce relatively stable clustering schemes. In this article, we propose a novel cluster-head (CH) selection scheme for VANETs. This scheme is based on a fuzzy logic-powered, k-hop distributed clustering algorithm. It deals efficiently with scalability and stability issues of VANETs and is able to achieve highly stable clustering topologies as compared with other schemes. Our proposed clustering scheme strives to maintain a safe intervehicle distance as a one prime metric for CH selection. Moreover, a major contribution of our work is the proposal of a novel strategy for constructing fuzzy logic-based clustering algorithms useful for VANETs. This proposed solution is useful in an Internet of things-based setting that involves controlled vehicle-to-vehicle communication. We first derive mathematically, a new average distance estimation formula that is used as a metric for selecting CHs, leading to safer clusters that avoid collisions with front and rear vehicles. Furthermore, the new proposed scheme creates stable clusters by reducing reclustering overhead and prolonging clusters' lifetimes.

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Many applications introduced by Vehicular Ad-Hoc Networks (VANETs), such as intelligent transportation and roadside advertisement, make VANETs become an important component of metropolitan area networks. In VANETs, mobile nodes are vehicles which are equipped with wireless antennas; and they can communicate with each other by wireless communication on ad-hoc mode or infrastructure mode. Clustering vehicles into different groups can introduce many advantages for VANETs as it can facilitate resource reuse and increase system capacity. The main contribution of our work is a new strategy for clustering a VANET and improvements in many classical clustering metrics. One of the main ideas is the definition of a new optimized selection metric for the clustering of vehicular nodes, in the framework of Next Generation Vehicular ad-hoc Network. These metrics should select clusterheads which provide safe clusters and avoid collisions with adjacent vehicle nodes and intend to create stable clusters by reducing reclustering overhead and prolonging cluster lifetime

A computational Fluid Dynamics (CFD) code for steady simulations solves a set of non-linear partial differential equations using an iterative time stepping process, which could follow an explicit or an implicit scheme. On the CPU, the difference between both time stepping methods with respect to stability and performance has been well covered in the literature. However, it has not been extended to consider modern high performance computing systems such as Graphics Processing Units (GPU). In this work, we first present an implementation of the two time-stepping methods on the GPU, highlighting the different challenges on the programming approach. Then we introduce a classification of basic CFD operations, found on the degree of parallelism they expose, and study the potential of GPU acceleration for every class. The classification provides local speedups of basic operations, which are finally used to compare the performance of both methods on the GPU. The target of this work is to enable an informed-decision on the most efficient combination of hardware and method, when facing a new application. Our findings prove, that the choice between explicit and implicit time integration relies mainly on the convergence of explicit solvers and the efficiency of preconditioners on the GPU.

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