Ph.D. in Ingegneria Informatica E Dei Sistemi , 38th cycle (2022-2025)
Ph.D. obtained in 2026
Dissertation:
Innovative Solutions for Industrial IoT (Abstract)
Tutors:
Massimo Violante Gianpaolo Macario
Research presentation:
PosterProfile
Research topic
Innovative Solutions for Industrial IoT
Keywords
Biography
Pietro D'Agostino is a PhD student in Computer and Control Engineering, currently part of the CAD (Computer-Aided Design) group. He earned his MSc in Mechatronic Engineering in 2022, following a Bachelor’s degree in Electronic Engineering in 2020. His research focuses on developing innovative solutions to address evolving market demands, utilizing a unique combination of fog computing and artificial intelligence.
At the core of the research is a novel fog computing architecture designed for real-time data collection and processing, which enables efficient, low-latency responses by managing data closer to its source. The architecture also incorporates a sandboxing mechanism to host third-party functionalities, allowing greater adaptability and scalability. A key aspect of his project involves creating predictive maintenance capabilities powered by AI algorithms, which anticipate potential failures to improve reliability and reduce operational downtime. His work presents a forward-thinking, flexible approach to data management and maintenance, setting a new standard for efficiency and reliability in the field.
Teaching
Teachings
Master of Science
- Model-based software design. A.A. 2023/24, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso
- Model-based software design. A.A. 2024/25, MECHATRONIC ENGINEERING (INGEGNERIA MECCATRONICA). Collaboratore del corso
Publications
Works published during the Ph.D. View all publications in Porto@Iris
- D'Agostino, Pietro (2026)
Innovative Solutions for Industrial IoT. relatore: VIOLANTE, MASSIMO; MACARIO, GIANPAOLO; , 38. XXXVIII Ciclo, P.: 152
Doctoral Thesis - D'Agostino, Pietro; Violante, Massimo; Macario, Gianpaolo (2025)
A Scalable Fog Computing Solution for Industrial Predictive Maintenance and Customization. In: ELECTRONICS, vol. 14. ISSN 2079-9292
Contributo su Rivista - D'Agostino, Pietro; Violante, Massimo; Macario, Gianpaolo (2024)
Optimizing LSTM-based temperature prediction algorithm for embedded system deployment. In: 29TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, Padova (IT), September 10-13, 2024, pp. 01-07
Contributo in Atti di Convegno (Proceeding) - D'Agostino, Pietro; Violante, Massimo; Macario, Gianpaolo (2023)
A user-extensible solution for deploying fog computing in industrial applications. In: International Symposium on Industrial Electronics (ISIE) 2023, Helsinki- (FIN), 19-21 June 2023, pp. 1-6. ISBN: 979-8-3503-9971-4
Contributo in Atti di Convegno (Proceeding) - D'Agostino, Pietro; Violante, Massimo; Macario, Gianpaolo (2023)
An embedded low-cost solution for a fog computing device on the Internet of Things. In: The Eighth IEEE International Conference on Fog and Mobile Edge Computing, Tartu (Estonia), September 18-20, 2023, pp. 284-291. ISBN: 979-8-3503-1697-1
Contributo in Atti di Convegno (Proceeding) - Sini, Jacopo; Pugliese, Luigi; D'Agostino, Pietro; Violante, Massimo; Groppo, Riccardo (2023)
A Novel Real-Time Redundant System For Aiding Drivers To Increase Their Attention Level. In: IEEE Smart World Congress 2023, Portsmouth (UK), 28-31 August 2023, pp. 898-903. ISBN: 979-8-3503-1980-4
Contributo in Atti di Convegno (Proceeding)