Knowledge Graph (KG) processing faces critical infrastructure challenges in selecting optimal NoSQL database paradigms, as traditional performance evaluations rely on static benchmarks that fail to capture the complexity of real-world KG workloads. Although the big data field offers numerous comparative studies, in the KG context DBMS selection remains predominantly ad-hoc, leaving practitioners without systematic guidance for matching storage technologies to specific KG characteristics and query requirements. This paper presents a KG-specific benchmarking framework that employs connectivity density, scale, and introduces a graph-centric metric, namely Semantic Richness (SR), within a four-tier query methodology to reveal performance crossover points across Document-Oriented, Graph, and Multi-Model DBMSs. We conduct an empirical evaluation on the FAERS adverse event KG at three scales, comparing paradigms from simple filtering to deep traversal, and provide metric-driven, evidence-based guidelines for aligning NoSQL paradigm selection with graph size, connectivity, and semantic richness.

Federated Learning (FL) aims to train artificial intelligence models without the need to share private raw data, thereby preserving privacy and security. Typically, it is assumed that all participating FL clients will act honestly to develop an accurate model. However, some clients may behave deceptively, manipulating their data to bias the model’s predictions and also degrade its generalization ability. This paper addresses the issue of fairness in FL from the perspective of client truthfulness. We introduce Federated Objective (FedObj), a novel aggregation method designed to minimize the impact of malicious clients and thereby improve the overall model’s robustness to such behavior. Our results show that FedObj achieves state-of-the-art performance in standard scenarios and outperforms conventional strategies when deceptive clients are involved. FedObj is a valuable approach for the collaborative development of trustworthy and fair AI systems, as it is significantly resilient to the misleading practices of malicious FL clients.

EDGEmergency is a platform designed for disaster management that can dynamically leverage the edge infrastructure potentially already present within the emergency perimeter. Edge devices, from IoT to smartphones, possess an increasingly significant computational capacity that can be exploited, by changing their behavior in real-time and creating a pervasive local environment, capable of adapting perfectly to the specific context of reference. EDGEmergency, in fact, allows the creation of a unified computation environment leveraging the Cloud-Edge-Client Continuum concept, through which a computation cluster with zero configurations is created on-the-fly. The platform thus allows the deployment of distributed microservices on existing edge devices, installed by default for other purposes, through a modular and incremental logic that has the role of adapting best to the needs of the individual emergency, through advanced tools for analysis and monitoring, using artificial intelligence.

Nowadays, thanks to the ever-increasing hardware capacity of Edge computing, the achievement of Ubiquitous Computing is no longer a utopia, even though it presents still several challenges. In this paper, we introduce the concept of the Cloud-Edge-Client Continuum, by extending the well-known Cloud-Edge Continuum paradigm with the addition of Clients as deployment nodes. Specifically, we propose both a system architecture and a piece of middleware that allows a web browser to be used seamlessly as a deployment Client node, introducing the concept of a Virtual Point of Deployment (VPod). Our solution allows to: a) leverage the computational capacity of a huge number of ready-to-use devices that do not require the installation of any dependencies; b) optimize the use of resources with clear benefits for end users, who can take advantage of their computing capacity to process sensitive data; c) reduce infrastructure costs. In addition, our proposal opens toward a multitude of scenarios, as the logical division that exists in the common client-server architecture is overcome, enabling the creation of a Cloud-Edge-Client Continuum environment.

The use of the Federated Learning paradigm could be disruptive in robotics, where data are naturally distributed among teams of agents and centralizing them would increase latency and break privacy. Unfortunately there are a lack of robot oriented framework for federated learning that use state of the art machine learning libraries. ROS2 (Robot Operating Systems) is a standard de-facto in robotics for building up teams of robots in a multi-node fully distributed manner. In this paper we presents the integration of ROS2 with PyTorch allowing an easy training of a global machine learning model starting from a set of local datasets. We present the architecture, the used methodology and finally we discuss the experimentation results over a well-known public dataset.

Internet of Things (IoT) became more and more popular because of the raise of ubiquitous internet connected devices. In this regard, IoT nodes are often organized in wireless sensor networks to facilitate communication and perform a coral computation. Such a network is often employed in urban or rural areas, i.e., for traffic, fires, and floods monitoring. Nodes are, therefore, deployed in remote areas, preventing the possibility to frequently access them, i.e., for firmware update. In this context, over-the-air (OTA) firmware update is used to remotely change the behavior of one or more nodes. In this paper, we firstly build a wireless mesh network with microcontrollers (i.e., ESP32) and, therefore, propose a secure filtered O TA firmware update involving firmware freshness (i.e., quarantine when firmware is not up-to-date), key pairing, and digital signature for data integrity and non-repudiation. The system is evaluated in terms of deactivation time $(s)$, energy consumption $(kWh)$, and greenhouse gases $(\mathrm{kgCO}_{2}\mathrm{e})$, highlighting good results in terms of scalability for grouped updates.

Nowadays, the combination between Cloud computing and Internet of Things (IoT) is pursuing new levels of efficiency in delivering services, representing a tempting business opportunity for IT operators of increasing their revenues. However, security is considered as one of the major factors that slows down the rapid and large scale adoption and deployment of both IoT and Cloud computing. In this paper, considering such an IoT Cloud scenario, we present an architectural model and several use cases that allow different types of users to access IoT devices.

This paper proposes a methodological approach for the performance analysis of Web-based searching applications on the Internet. It specifically investigates the behavior of the client/server (C/S), remote-evaluation (REV) and mobile-agent (MA) communication paradigms and describes how Petri-net models can be developed to derive performance indices which can help the designer to improve the efficiency of his distributed applications. Our purpose is that of identifying a set of models that can help to understand the environmental situations in which such paradigms should be preferred or combined in order to optimize the performances of a distributed system. In particular, we propose a modeling technique applied to an information retrieval application on the World Wide Web. An analytical evaluation through the solution of nonMarkovian Petri-net models is provided, which allows us to identify the main parameters, as well as the way they interact, to be taken into consideration when distributed applications are to be designed. An experimental environment is also studied in order to obtain real measurements used to validate the analytical models.