RESUMEN
As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (WLANs) can be a good choice as IoT infrastructure when high throughput is required. On the other hand, wireless mesh networks (WMNs), which are WLANs with mesh topology following the IEEE802.11s standard, have many advantages compared to conventional WLANs. Nevertheless, there are some problems that need solutions. One of them is the node placement problem. In this work, we propose and implement a hybrid intelligent system that solves this problem by determining the position of mesh nodes by maximizing the mesh connectivity and the coverage of IoT devices. The system is based on particle swarm optimization (PSO), simulated annealing (SA), and distributed genetic algorithm (DGA). We compare the performance of three router replacement methods: constriction method (CM), random inertia weight method (RIWM), and rational decrement of Vmax method (RDVM). The simulation results show that RIWM achieves better performance compared to CM and RDVM because it achieves the highest connectivity while covering more clients than the other two methods.
RESUMEN
The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In this work, we propose an intelligent approach to flexibly and efficiently manage resources in these networks. The proposed approach makes use of an integrated fuzzy logic system that determines the most appropriate resources that vehicles should use when set under various circumstances. These circumstances cover the quality of the network created between the vehicles, its size and longevity, the number of available resources, and the requirements of applications. We evaluated the proposed approach by computer simulations. The results demonstrate the feasibility of the proposed approach in coordinating and managing the available SDN-VANETs resources.
RESUMEN
The highly competitive and rapidly advancing autonomous vehicle race has been on for several years now, and it has made the driver-assistance systems a shadow of their former self. Nevertheless, automated vehicles have many obstacles on the way, and until we have them on the roads, promising solutions that can be achievable in the near future should be sought-after. Driving-support technologies have proven themselves to be effective in the battle against car crashes, and with Vehicular Ad hoc Networks (VANETs) supporting them, their efficiency is expected to rise steeply. In this work, we propose and implement a driving-support system which, on the one hand, could immensely benefit from major advancement of VANETs, but on the other hand can effectively be implemented as a stand-alone system. The proposed system consists of a non-intrusive integrated fuzzy-based system able to detect a risky situation in real time and alert the driver about the danger. It makes use of the information acquired from various in-car sensors as well as from communications with other vehicles and infrastructure to evaluate the condition of the considered parameters. The parameters include factors that affect the driver's ability to drive, such as his/her current health condition and the inside environment in which he/she is driving, the vehicle speed, and factors related to the outside environment such as the weather and road condition. We show the effect of these parameters on the determination of the driving risk level through simulations and experiments and explain how these risk levels are translated into actions that can help the driver to manage certain risky situations, thus improving the driving safety.
