Publications by Carmine Zaccagnino

The complete workflow of the RI-PIENO framework is presented, a system for refueling itinerary optimization that extends the original PIENO design. While prior work introduced the conceptual modules of RI-PIENO, their operational pipeline was not described in detail. This study makes the workflow explicit, covering the end-to-end process from CAN Bus data acquisition and stop detection to the construction of daily trip graphs, refueling optimization, and mileage prediction. By clarifying the sequence of operations, the contribution provides a reproducible and extensible foundation for future research and development.

Efficient energy provisioning is a fundamental requirement for modern transportation systems, making refueling path optimization a critical challenge. Existing solutions often focus either on inter-vehicle communication or intravehicle monitoring, leveraging Intelligent Transportation Systems, Digital Twins, and Software-Defined Internet of Vehicles with Cloud/Fog/Edge infrastructures. However, integrated frameworks that adapt dynamically to driver mobility patterns are still underdeveloped. Building on our previous PIENO framework, we present RI-PIENO (Revised and Improved Petrolfilling Itinerary Estimation aNd Optimization), a system that combines intra-vehicle sensor data with external geospatial and fuel price information, processed via IoT-enabled Cloud/Fog services. RI-PIENO models refueling as a dynamic, time-evolving directed acyclic graph that reflects both habitual daily trips and real-time vehicular inputs, transforming the system from a static recommendation tool into a continuously adaptive decision engine. We validate RI-PIENO in a daily-commute use case through realistic multi-driver, multi-week simulations, showing that it achieves significant cost savings and more efficient routing compared to previous approaches. The framework is designed to leverage emerging roadside infrastructure and V2X communication, supporting scalable deployment within next-generation IoT and vehicular networking ecosystems.

The recent rise of intelligent transportation systems (ITS) has challenged the integration between different data sources. Reaching the goal of sustainable mobility requires properly managing and merging information coming from the vehicle (intra-) and information coming off the vehicle (inter-). In this paper, we provide a proof-of-concept leveraging on data merging between intra- and inter-networking presenting our framework: Petrol-Filling Itinerary Estimation aNd Optimization (PIENO). PIENO is a system that not only automates the search for the best fuel station but also paves the road to significant reductions in fuel consumption, making eco-driving a practical reality from a user perspective. The PIENO framework is designed to be fuel-type independent, ensuring its adaptability to different vehicles and conditions. It achieves this by merging data from the vehicle through a CAN Access Module (CAM) and data outside the vehicle through a mobile application connected to the internet. Different domains are stressed to reach the goal: microcontroller and OEM to retrieve the fuel level from the car, national authorities to retrieve the daily fuel price, AI models to predict the price trend for the next days, and algorithms to compute the best fuel station and the best time to fill. The modularity of PIENO allows it to adapt to different OEMs by modifying the intra-network interface to properly collect the fuel level, as well as to adapt to different markets and countries, retrieving the station’s locations and fuel prices by modifying the inter-network interface.