The recent need of supporting the diffusion of electric mobility around the world to progressively substitute petrol transport means, leads to the development of new hardware and software technologies to make even more convenient the use of electric vehicles (EVs). High purchasing costs and long recharging times are two major factors slowing this transition. In addition, from the end users perspective, using EV in long distance journeys is still not convenient despite the increasing diffusion of fast charging infrastructures. In this context, to facilitate traveling with EVs in long distance trips, this paper proposes a trip planner prototype based on Deep Reinforcement Learning (DRL). The trip planner prototype has the goal to suggest to the drivers the best charge stops to be performed during the trip according to the user needs and preferences. Charging stops are optimized, using the available Charging Points (CPs) along the route from origin to destination, and are shown to the user on a map taking into account important information like the EV State of Charge (SoC), the cruise velocity, and the presence of point of interest (e.g. restaurant, hotel, shops, etc.) close around. The trip plan can be done according to three objectives: minimizing the travel time, minimizing the charging costs, optimizing travel time and cost. The proposed DRL approach is compared against Genetic Algorithm (GA), heuristic, and optimization approaches considering a real-world EV trip.

In the age of intelligent transportation systems and smart cities, teleoperated driving aims to bridge the gap between human and fully autonomous driving. However, the reliability of teleoperated driving is heavily dependent on the quality of the cellular networks, a limitation that could be addressed by 6G networks, which aims to enhance ultralow latency and high reliability. This study proposes an integrated simulator for teleoperated driving by utilizing Deep Reinforcement Learning (DRL) in a framework of 6G and Network Digital Twin. The presented simulation framework combines different tools (i.e., SUMO, OMNeT++, and Simu5G) to model realistic traffic and network dynamics. In addition, the Random Forest algorithm is used for the coverage prediction system and maintaining stable connectivity, and a DRL model optimizes vehicle routing by balancing path length and signal coverage. A case study is simulated considering the city of Bari (Italy). The framework demonstrates robust communication between teleoperated vehicles and 6G Digital Twin infrastructure.

This paper introduces an innovative approach to designing a user-based Heating, Ventilation, and Air-Conditioning (HVAC) system management connected with the District Energy Management System. By classifying the users into dynamic energy consumption classes to reward energy efficiency and penalize excessive use, users can modify their behavior to pass to a less expensive and more virtuous consumption class. To this aim, a blockchain platform determines the rewards and penalties and, by a K-means clustering algorithm, categorizes users into respective groups. Then, a Class Follower Problem is formulated and solved by a Model Predictive Control (MPC) strategy integrated with a Long Short-Term Memory network as a predictive model. If the users follow the suggestions proposed by the controller, i.e., the thermostat set-points and the time intervals in which the HVAC system must be switched off or on, the users can be located in a more virtuous consumption class. A case study conducted within an energy district in Bari (Italy) shows how the proposed architectural framework tuned thermal regulation in intelligent buildings while concurrently achieving energy optimization.Note to Practitioners—This paper addresses the challenge of efficiently managing HVAC systems in smart districts through a novel blockchain-based framework and an optimization strategy solved by an MPC approach. The objective is to incentivize users to optimize their energy consumption by introducing dynamic Consumption Classes that reward energy efficiency and penalize inefficient utilization. For practitioners, this strategy translates to a granular level of energy management that not only adapts to individual behaviors but also aligns with broader sustainability goals. Integrating the blockchain platform ensures a transparent and secure method for managing and recording energy usage. At the same time, adopting MPC with Long Short-Term Memory Networks offers accurate forecasts and adjustments to enhance system responsiveness. Although the study focuses on HVAC systems, the principles may be extended to other energy-intensive applications, providing a comprehensive tool for energy management and user engagement in smart cities. Future research could integrate renewable energy sources and explore the implications of user-driven adjustments on the overall energy distribution and efficiency.

The rapid advancement and deployment of Autonomous Vehicles (AVs) necessitate innovative solutions for reliable and efficient navigation. In this context, a crucial aspect is the unequivocal identification of intersections. This paper proposes a novel methodology for uniquely identifying intersections by applying a hash algorithm that generates a distinct fingerprint of each intersection, inspired by the operational mechanisms within blockchain platforms, particularly mimicking the generation of Transaction Hashes. The solution’s core is creating a hash tree to unequivocally identify the intersection for the AVs’ navigation. The application to a real complex case study shows the applicability of the proposed approach.

Urban autonomous driving has the potential to enhance both safety and efficiency of transportation in environments also in complex traffic conditions. However, new services and approaches are necessary to manage Autonomous Vehicles in the real traffic. This paper introduces a novel approach to optimize routing in the urban settings by Deep Reinforcement Learning (DRL) techniques. A modular DRL architecture is proposed to obtain a route able to minimize the length of the paths, minimize the number of turns during the travel and select the dedicated lanes. The proposed DRL is implemented on a case study where the agents are trained in a simulation environment for the city center of Bari, a town of Southern Italy.

The development of Quantum Computing has brought great advantages in terms of computational power that can be seen as an opportunity or as a potential threat to currently implemented systems. The security of a platform can be easily broken if its founding algorithms are not quantum-safe. For this reason, it is crucial to understand how quantum computers work and how much time is needed to switch to quantum-safe platforms. The main contribution of this paper consists of a software architecture for modular Blockchains to let multiple encryption algorithms coexist in order to mine new quantum-safe blocks without discarding old, validated, quantum-broken ones. Old blocks will still be unsafe for post-quantum cryptography, but this is not a threat to the chain integrity.

The increasing volume of traffic worldwide enlightens the problem of ensuring driving safety and preventing collisions at unsignalized intersections. In this regard, with the advent of Connected Autonomous Vehicles (CAV), Collision Avoidance (CA) and Autonomous Intersection Management (AIM) problems have been intensively studied and many cooperative and optimization-based approaches have been proposed. In this paper, we introduce a double-level collision-free control system, performed by a Central Optimizer (CO) and local CAV controllers, that allows CAVs to safely cross the intersection at the same time. The CO collects data from CAVs and imposes waiting times at specific waypoints that are then used by the low-level controllers to regulate the vehicle speed. The main advantage of the presented method is that a less complex optimization problem is formulated by imposing waiting times rather than determining the speed profile. A simulation campaign conducted on Matlab shows the performance of the proposed approach.