This research proposes a unique platform for energy management optimization in smart grids, based on 6G technologies. The proposed platform, applied on a virtual power plant, includes algorithms that take into account different profiles of loads and fairly schedules energy according to loads priorities and compensates for the intermittent nature of renewable energy sources. Moreover, we develop a bidirectional energy transition mechanism towards a fleet of intelligent vehicles by adopting vehicle-to-grid technology and peak clipping. Performance analysis shows that the proposed energy provides fairness to electrical vehicles, satisfies urgent loads, and optimizes smart grids energy.

Vehicle-to-Everything (V2X) communications refer to information exchange between vehicles and various elements of the Intelligent Transportation System (ITS). This communication enables a variety of V2X use cases with different Quality of Service (QoS) requirements. The messages exchange is achieved through a vehicular network that allows the coexistence of various radio access technologies. In this heterogeneous environment, the choice of the technology to deploy is a primordial issue in order to guarantee the QoS needs of V2X applications.This paper provides a network selection framework for V2X use cases based on Software Defined Networking (SDN) technology. The proposed solution is implemented as a SDN application running on top of the SDN controller. Network selection relies on a specific sigmoid utility function and game theory aiming at fulfilling user satisfaction and achieving load balancing.Simulation results show that our framework provides several improvements in terms of QoS guarantee and network load alleviation.

This research proposes a unique platform for lane change assistance for generating data-driven lane change (LC) decisions in vehicular networks. The goal is to reduce the frequency of emergency braking, the rate of vehicle collisions, and the amount of time spent in risky lanes. In order to analyze and mine the massive amounts of data, our platform uses effective Machine Learning (ML) techniques to forecast collisions and advise the driver to safely change lanes. From the unprocessed large data generated by the car sensors, kinematic information is retrieved, cleaned, and evaluated. Machine learning algorithms analyze this kinematic data and provide an action: either stay in lane or change lanes to the left or right. The model is trained using the ML techniques K-Nearest Neighbor, Artificial Neural Network, and Deep Reinforcement Learning based on a set of training data and focus on predicting driver actions. The proposed solution is validated via extensive simulations using a microscopic car-following mobility model, coupled with an accurate mathematical modelling. Performance analysis show that KNN yields up to best performance parameters. Finally, we draw conclusions for road safety stakeholders to adopt the safer technique to lane change maneuver.