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.

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.

The fifth generation (5G) network is expected to be flexible to satisfy quality of service (QoS) requirements, and the software-defined network (SDN) with network slicing will be a good approach for admission control. In this paper, the authors present and compare two fuzzy-based schemes to evaluate the QoS (FSQoS). They call these schemes FSQoS1 and FSQoS2. The FSQoS1 considers three parameters: slice throughput (ST), slice delay (SD), and slice loss (SL). In FSQoS2, they consider as an additional parameter the slice reliability (SR). So, FSQoS2 has four input parameters. They carried out simulations for evaluating the performance of the proposed schemes. From simulation results, they conclude that the considered parameters have different effects on the QoS performance. The FSQoS2 is more complex than FSQoS1, but it has a better performance for evaluating QoS. When ST and SR are increasing, the QoS parameter is increased. But, when SD and SL are increasing, the QoS is decreased. When ST is 0.1, SD is 0.1, SL is 0.1, and the QoS is increased by 32.02% when SR is increased from 0.3 to 0.8.

In the authors' previous work, they proposed an integrated fuzzy-based admission control system (IFACS) for admission control in 5G wireless networks. In this paper, they present a new system by considering service level agreement (SLA) as a new parameter. They call this system IFACS-Q3S. They also implemented a new scheme for slice overloading cost (SOC), called fuzzy-based scheme for SOC (FSSOC). The SOC is used as a new input for the IFACS-Q3S system. The other input parameters for IFACS-Q3S are quality of service (QoS), slice priority (SP), and SLA. From simulation results, they conclude that the considered parameters have different effects on the acceptance decision. The increase of QoS, SP, and SLA caused an increase in the AD value, whereas the increase in SOC resulted in a decrease in the AD value. For SOC 0.3, when the QoS value is 0.1 and the SP value is 0.1, in the case when SLA is increased from 0.1 to 0.5 and 0.5 to 0.9, the AD is increased by 5% and 11%, respectively. On the other side, when the SLA value is 0.9, we see that AD is decreased 14% by increasing the SOC values from 0.3 to 0.8.