Publications

With rapid advances in containerization techniques, the serverless computing model is becoming a valid candidate execution model in edge networking, similar to the widely used cloud model for applications that are stateless, single purpose and event-driven, and in particular for delay-sensitive applications. One of the cloud serverless processes, i.e., the auto-scaling mechanism, cannot be however directly applied at the edge, due to the distributed nature of edge nodes, the difficulty of optimal resource allocation, and the delay sensitivity of workloads. We propose a solution to the auto-scaling problem by applying reinforcement learning (RL) approach to solving problem of efficient scaling and resource allocation of serverless functions in edge networks. We compare RL and Deep RL algorithms with empirical, monitoring-based heuristics, considering delay-sensitive applications. The simulation results shows that RL al-gorithm outperforms the standard, monitoring-based algorithms in terms of total delay of function requests, while achieving an improvement in delay performance by up to 50%.

In this work, we explore the integration of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector. More specifically, we present the main FL principles, the proposed workflow and then, we evaluate and compare various FL algorithms in three maritime use cases, i.e. regression to predict the naval propulsion gas turbine (GT) measures, classification to predict the ship engine condition, and time-series regression to predict ship fuel consumption. The efficiency of the proposed FL-based PdM highlights its ability to improve maintenance decision-making, reduce downtime in the shipping industry, and enhance the operational efficiency of shipping fleets. The findings of this study support the advancement of PdM methodologies in Shipping 4.0, providing valuable insights for maritime stakeholders to adopt FL, as a viable and privacy-preserving solution, facilitating model sharing in the shipping industry and fostering collaboration opportunities among them.

The main purpose of ETSI multi-access edge computing (MEC) is to improve latency and bandwidth consumption by keeping local traffic local while providing computing resources near the end-user. Despite its clear benefits, the next-generation of hyper-distributed applications (e.g., edge robotics, augmented environments, or smart agriculture) will exacerbate latency and bandwidth requirements, posing significant challenges to today’s MEC deployments.

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place.

6G targets a broad and ambitious range of networking scenarios with stringent and diverse requirements. Such challenging demands require a multitude of computational and communication resources and means for their efficient and coordinated management in an end-to-end fashion across various domains. Conventional approaches cannot handle the complexity, dynamicity, and end-to-end scope of the problem, and solutions based on artificial intelligence (AI) become necessary.

The goal of the study presented in this paper is to evaluate the performance of a proposed Reinforcement Learning (RL) power allocation algorithm. The algorithm follows a demand-driven power adjustment approach aiming at maximizing the number of users inside a coverage area that experience the requested throughput to accommodate their needs. In this context, different Quality of Service (QoS) classes, corresponding to different throughput demands, have been taken into account in various simulation scenarios.

Maritime activities are vital for economic growth, being further accelerated by various emerging maritime Internet of Things (IoT) use cases, including smart ports, autonomous navigation, and ocean monitoring systems. However, broadband, low-delay, and reliable wireless connectivity to the ever-increasing number of vessels, buoys, platforms and sensors in maritime communication networks (MCNs) has not yet been achieved. Towards this end, the integration of unmanned aerial vehicles (UAVs) in MCNs provides an aerial dimension to current deployments, relying on shore-based base stations (BSs) with limited coverage and satellite links with high latency.

The production of quality printing products requires a highly complex and uncertain process, which leads to the unavoidable generation of printing defects. This common phenomenon has severe impacts on many levels for Offset Printing manufacturers, ranging from a direct economic loss to the environmental impact of wasted resources. Therefore, the accurate estimation of the amount of paper waste expected during each press run, will minimize the paper consumption while promoting environmentally sustainable principles.

Smart homes, powered mostly by Internet of Things (IoT) devices, have become very popular nowadays due to their ability to provide a holistic approach towards effective energy management. This is made feasible via the deployment of multiple sensors, which enables predicting energy consumption via machine learning approaches. In this work, we propose FedTime, a novel federated learning approach for predicting smart home consumption which takes into consideration the age of the time series datasets of each client.

SWForum.eu Way Forward Workshop: Future Challenges in Software Engineering, held on 27 June 2023 at the Politecnico di Milano (POLIMI), in the Dipartamento di Elettronica, Informazione e Bioingegneria (DEIB), featured a dynamic agenda that encompassed a comprehensive range of topics and discussions.

In the era of digital transformation the increasing vulnerability of infrastructure and applications is often tied to the lack of technical capability and the improved intelligence of the attackers. In this paper, we discuss the complementarity between static security monitoring of rule matching and an application of self-supervised machine-learning to cybersecurity. Moreover, we analyse the context and challenges of supply chain resilience and smart logistics. Furthermore, we put this interplay between the two complementary methods in the context of a self-learning and self-healing approach.

Baldoni, Gabriele, Jose Quevedo, Carlos Guimaraes, Antonio de la Oliva, and Angelo Corsaro. “Data-Centric Service-Based Architecture for Edge-Native 6G Network.” IEEE Communications Magazine, 2023, 1–7. https://doi.org/10.1109/MCOM.001.2300178

Baldoni, Gabriele, Rafael Teixeira, Carlos Guimarães, Mário Antunes, Diogo Gomes, and Angelo Corsaro. “A Dataflow-Oriented Approach for Machine-Learning-Powered Internet of Things Applications.” Electronics 12, no. 18 (January 2023): 3940. https://doi.org/10.3390/electronics12183940

Bensalem, Mounir, Francisco Carpio, and Admela Jukan. “Towards Optimal Serverless Function Scaling in Edge Computing Network.” arXiv, May 23, 2023. https://doi.org/10.48550/arXiv.2305.13896

Corsaro, Angelo, Luca Cominardi, Olivier Hecart, Gabriele Baldoni, Julien Enoch, Pierre Avital, Julien Loudet, Carlos Guimarães, Michael Ilyin, and Dmitrii Bannov. “Zenoh: Unifying Communication, Storage and Computation from the Cloud to the Microcontroller” DSD 2023 (September 8, 2023)

Dizdarević, Jasenka, David Blazevic, Marla Grunewald, and Admela Jukan. “An Edge/Cloud Continuum with Wearable Kinetic Energy Harvesting IoT Devices in Remote Areas.” In 2024 IEEE International Conference on Communications (ICC), 2024. https://icc2024.ieee-icc.org/program/day-1

Dizdarević, Jasenka, Marc Michalke, and Admela Jukan. “Benchmarking Performance of Various MQTT Broker Implementations in a Compute Continuum.” In 2024 IEEE/ACM 24rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, n.d

Esfandiyar, Iman, and Łukasz Łowiński. “New Challenges in the Implementation of Digital Projects in Agriculture.” In XXIX Scientific Conference “Scientific, Technical and Organizational in Agriculture", 2023

Esfandiyar, Iman, and Lukasz Lowinski. “Potential of Implementing Operational Metasystems in Agriculture Using the ICOS Project as an Example.” In 24th Scientific Conference ROL-EKO “Organic Farming, Design, Research, Operation, Safety and Ergonomics of Agricultural, Forestry and Food Machinery,” 2023

Hortelano, Diego, Ignacio de Miguel, Ramón J. Durán Barroso, Juan Carlos Aguado, Noemí Merayo, Lidia Ruiz, Adrian Asensio, et al. “A Comprehensive Survey on Reinforcement-Learning-Based Computation Offloading Techniques in Edge Computing Systems.” Journal of Network and Computer Applications 216 (July 1, 2023): 103669. https://doi.org/10.1016/j.jnca.2023.103669

Hussain, A., F. Aguiló-Gost, E. Simó-Mezquita, E. Marín-Tordera, and X. Massip. “An NIDS for Known and Zero-Day Anomalies.” In 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN), 1–7, 2023. https://doi.org/10.1109/DRCN57075.2023.10108319

Lordan, Francesc, Gabriel Puigdemunt, Pere Vergés, Javier Conejero, Jorge Ejarque, and Rosa M. Badia. “Hierarchical Management of Extreme-Scale Task-Based Applications.” In Euro-Par 2023: Parallel Processing, edited by José Cano, Marios D. Dikaiakos, George A. Papadopoulos, Miquel Pericàs, and Rizos Sakellariou, 111–24. Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-39698-4_8

Plociennik, Marcin. “Development of the EDGE-CLOUD Solutions across Domain.” LIP Indico (Indico), September 27, 2023. https://indico.lip.pt/event/1543/contributions/5186/

Seyghaly, Rasool, Jordi Garcia, Xavi Masip-Bruin, and Mohammad Mahmoodi Varnamkhasti. “Enhanced Smart Advertising through Federated Learning.” In 2023 International Wireless Communications and Mobile Computing (IWCMC), 675–80, 2023. https://doi.org/10.1109/IWCMC58020.2023.10183266