Publications
All our publications can be found in 
Beyond Multi-Access Edge Computing: Essentials to Realize a Mobile, Constrained Edge
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.
A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
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.
Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G
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.
Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach
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.
Uplink NOMA for UAV-Aided Maritime Internet-of-Things
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.
A Stacking Ensemble Learning Model for Waste Prediction in Offset Printing
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.
Data Aging Matters: Federated Learning-Based Consumption Prediction in Smart Homes via Age-Based Model Weighting
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.
Presentation Open Access SWForum.eu The Way Forward: Workshop on Future Challenges in Software Engineering
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.
Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs
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.
Scaling Serverless Functions in Edge Networks: A Reinforcement Learning Approach
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%.
Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases
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.
Towards Optimal Serverless Function Scaling in Edge Computing Network
Serverless computing has emerged as a new execution model which gained a lot of attention in cloud computing thanks to the latest advances in containerization technologies. Recently, serverless has been adopted at the edge, where it can help overcome heterogeneity issues, constrained nature and dynamicity of edge devices. Due to the distributed nature of edge devices, however, the scaling of serverless functions presents a major challenge. We address this challenge by studying the optimality of serverless function scaling. To this end, we propose Semi-Markov Decision Process-based (SMDP) theoretical model, which yields optimal solutions by solving the serverless function scaling problem as a decision making problem. We compare the SMDP solution with practical, monitoring-based heuristics. We show that SMDP can be effectively used in edge computing networks, and in combination with monitoring-based approaches also in real-world implementations.
A Dataflow-Oriented Approach for Machine-Learning-Powered Internet of Things Applications
The rise of the Internet of Things (IoT) has led to an exponential increase in data generated by connected devices. Machine Learning (ML) has emerged as a powerful tool to analyze these data and enable intelligent IoT applications. However, developing and managing ML applications in the decentralized Cloud-to-Things continuum is extremely complex. This paper proposes Zenoh-Flow, a dataflow programming framework that supports the implementation of End-to-End (E2E) ML pipelines in a fully decentralized manner and abstracted from communication aspects. Thus, it simplifies the development and upgrade process of the next-generation ML-powered applications in the IoT domain. The proposed framework was demonstrated using a real-world use case, and the results showcased a significant improvement in overall performance and network usage compared to the original implementation. Additionally, other of its inherent benefits are a significant step towards developing efficient and scalable ML applications in the decentralized IoT ecosystem.
Zenoh-Unifying communication, storage, and computation from the cloud to the microcontroller
An increasing number of systems span from the data-center down to the micro-controller and need to smoothly operate across this continuum composed by extremely heterogeneous network technologies and computing platforms. Building these systems is quite challenging due to limitations of existing technological stacks. This paper introduces Zenoh a Pub/Sub- /Query protocol that unifies data at rest, data in motion and computations. Zenoh has been designed ground-up to address the needs of the cloud to micro-controller continuum. It has a minimal wire overhead of 5 bytes, it runs and perform on constrained as well as on high end networks and hardware.
New challenges in the implementation of digital projects in agriculture
Presentation about new challenges in the implementation of digital projects in agriculture.
Potential of Implementing Operational Metasystems in Agriculture Using the ICOS Project as an Example
Presentation about the potential of implementing operational metasystems in agriculture using the ICOS project as an example.
Hierarchical Management of extreme-scale task-based applications
The scale and heterogeneity of exascale systems increment the complexity of programming applications exploiting them. Task-based approaches with support for nested tasks are a good-fitting model for them because of the flexibility lying in the task concept. Resembling the hierarchical organization of the hardware, this paper proposes establishing a hierarchy in the application workflow for mapping coarse-grain tasks to the broader hardware components and finer-grain tasks to the lowest levels of the resource hierarchy to benefit from lower-latency and higher-bandwidth communications and exploiting locality. Building on a proposed mechanism to encapsulate within the task the management of its finer-grain parallelism, the paper presents a hierarchical peer-to-peer engine orchestrating the execution of workflow hierarchies with fully-decentralized management. The tests conducted on the MareNostrum 4 supercomputer using a prototype implementation prove the validity of the proposal supporting the execution of up to 707,653 tasks using 2,400 cores and achieving speedups of up to 106 times faster than executions of a single workflow and centralized management.
Enhanced Smart Advertising through Federated Learning
Smart advertising is growing in popularity and affecting businesses. Smart advertising is a more friendly, interactive, personalised, and creative method of promoting a product, and attempts to delight clients. AROUND is a social networking service that emphasizes smart advertising through an effective recommender system. The system considers user profiles, history, social network connections, mood, and IoT-supported positioning to select the most relevant ads using machine learning technology. Although the current deployment of the AROUND system is based on the cloud, an edge-based architecture provides relevant improvement in terms of system response time. In this paper we extend the edge-based strategy to leverage the potential of federated learning on multiple distributed edge servers. We show that federated learning can take advantage of the distributed nature of the system, and leverage the specificities of local features. In fact, in this research, we propose a novel federated learning solution to provide smart advertising as a classification problem which uses ensemble methods and logistic regression as internal (local) models and meta-heuristic algorithms for federated learning aggregation. As part of the experiments, we prove this technology on a real data set with more than one million registers, and show the efficiency in terms of enhanced accuracy and improved training and response speed.
Development of the EDGE-CLOUD solutions across domain
Edge-to-Cloud is expected to provide the means for workloads execution and data processing both in the Edge and the Cloud. Within this presentation we present different efforts addressing the challenges towards achieving the next generation of continuum management. We will explain this presenting efforts of two projects illuMINEation and ICOS. ICOS is proposing a high-level meta operating system (metaOS) to realize the continuum. The use case that is targeting to exploit the technologies is related to agriculture and robotics - the Agriculture Operational Robotic Platform (AORP) is an agro robot that can execute different tasks and missions, like sowing and tending crops, removing weeds, monitoring crop development, and identifying threats. The platform moves autonomously through the field, performing the assigned missions. The robotic platform consists of control and driving modules. In addition, it is equipped with interchangeable tools - a seeder and a sprayer. The AORP is equipped with cameras, sensors and Edge computational devices that can be connected to the Cloud directly, via the transport platform, or via farm connectivity.
A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems
This paper presents a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in some scenarios, particularly on those related to the computation offloading problem in edge systems.
An NIDS for Known and Zero-Day Anomalies
Rapid development in the network infrastructure has resulted in sophisticated attacks which are hard to detect using typical network intrusion detection systems (NIDS). There is a strong need for efficient NIDS to detect these known attacks along with ever-emerging zero-day exploits. Existing NIDS are more focused on detecting known attacks using supervised machine learning approaches, achieving better performance for known attacks but poor detection of unknown attacks. Many NIDS have utilized the unsupervised approach, which results in better detection of unknown anomalies. In this paper, we proposed a Hybrid NIDS based on Semisupervised One-Class Support Vector Machine (OC-SVM) and Supervised Random Forest (RF) algorithms. This detection system has several stages. The First stage is based on OC-SVM, which filters benign and malicious traffic. The next stages use many parallel supervised models and an additional OC-SVM model to separate known and unknown attacks from malicious traffic. The previous process is done so that known attacks are classified by their type, and unknown attacks are detected. The proposed NIDS is tested on the standard public dataset CSE-CIC-IDS-2018. The evaluation results show that the system achieves a high accuracy, 99.45%, for detecting known attacks. Our proposed NIDS achieves an accuracy of 93.99% for unknown or zero-day attacks. The overall accuracy of the proposed NIDS is 95.95%. The system significantly improves the detection of known and unknown anomalies using a hybrid approach.
Security in DevSecOps: Applying Tools and Machine Learning to Verification and Monitoring Steps
Security represents one of the crucial concerns when it comes to DevOps methodology-empowered software development and service delivery process. Considering the adoption of Infrastructure as Code (IaC), even minor flaws could potentially cause fatal consequences, especially in sensitive domains such as healthcare and maritime applications. However, most of the existing solutions tackle either Static Application Security Testing (SAST) or run-time behavior analysis distinctly. In this paper, we propose a) IaC Scan Runner, an open-source solution developed in Python for inspecting a variety of state-of-the-art IaC languages in application design time and b) the run time anomaly detection tool called LOMOS. Both tools work in synergy and provide a valuable contribution to a DevSecOps tool set. The proposed approach is demonstrated and their results will be demonstrated on various case studies showcasing the capabilities of static analysis tool IaC Scan Runner combined with LOMOS – log analysis artificial intelligence-enabled framework.
This project has received funding from the European Union’s HORIZON research and innovation programme under grant agreement No 101070177.