Autonomous vehicles (AVs) continuously generate high-resolution sensor data on road conditions, infrastructure updates, and traffic dynamics. Despite their critical relevance for real-time navigation and urban planning, these datasets remain siloed within manufacturer-specific platforms. Motivated by the necessity to overcome such fragmentation, this paper introduces a novel decentralized, blockchain-based framework whose key innovation is a dynamic voting threshold integrated into a modular smart contract architecture. In our model, AVs can submit and validate road events –such as newly detected closures or construction sites– through a modular smart contract system employing dynamic voting thresholds that adapt acceptance criteria based on different factors. This allows urgent changes to achieve consensus while quickly minimizing malicious or erroneous reporting. Upon reaching a consensus regarding the specific event, the proposer is granted token-based incentives redeemable for operational cost reductions (e.g., charging or parking discounts). The proposed approach is validated via a Hardhat simulation on an Ethereum Virtual Machine compatible test network, demonstrating our design’s feasibility, robustness, and responsiveness under diverse scenarios.

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.