Digital Twin Networks (DTNs) are an emerging paradigm where DTs collaborate to share knowledge and deliver intelligent services. To meet the need for low-latency and localized processing, DTs can be deployed at the network edge. However, effective mechanisms for inter-twin communication and service provisioning remain largely unexplored. In this paper, we address these challenges by leveraging a name-based communication paradigm, namely Information Centric Networking (ICN), enhanced with semantic-awareness. To overcome the limitations of exact name matching, we integrate deep learning models into the ICN forwarding fabric to compute semantic similarity. This enables the discovery of relevant cached service results or routes to DT service providers, even under naming heterogeneity. A preliminary evaluation shows improved service provisioning success and reduced latency compared to standard ICN delivery.

Speech Emotion Recognition (SER) has achieved significant progress in widely studied languages such as English, driven by the availability of large-scale benchmark datasets. However, research on Italian remains limited due to the scarcity of recent and complementary emotional speech corpora. This paper investigates a feature-based transfer learning approach for Italian SER using audio models pre-trained on AudioSet. Experiments are conducted on two recent Italian corpora with complementary characteristics: AI4SER, recorded in controlled laboratory conditions, and Emozionalmente, developed through crowdsourcing and characterized by higher acoustic variability. A three-scenario evaluation protocol—within-corpus, cross-corpus, and joint training—is adopted to assess robustness to domain shift. Results indicate that embeddings extracted from deeper and higher-capacity models consistently improve performance and robustness. However, significant degradation is observed in cross-corpus settings, highlighting the strong impact of dataset-specific characteristics. Joint training partially mitigates this effect but does not fully eliminate the generalization gap. These findings provide insights into transfer learning effectiveness for Italian SER under heterogeneous recording conditions.

The explosive growth of mobile data traffic demands scalable, resilient, and energy-efficient solutions for content dissemination, particularly at the network edge. In this context, 5G networks can rely on Outband Device-to-Device (O-D2D) communications, using technologies such as Wi-Fi Direct, to offload infrastructure, enhance coverage at the cell edge, and enable proximity-based services. However, O-D2D communications face critical challenges related to peer discovery, reliable data retrieval over lossy wireless links, and energy efficiency constraints. In this paper, we propose a novel framework that integrates Named Data Networking (NDN) and Chinese Remainder Theorem (CRT)-based fragmentation to support autonomous O-D2D content delivery. By leveraging name-based forwarding and in-network caching, NDN facilitates distributed content retrieval, making it inherently suited for dynamic and infrastructure-less D2D environments. CRT-based fragmentation, on the other hand, enhances reliability and energy efficiency by allowing data reconstruction from partial fragment sets, mitigating the effects of packet losses typical of wireless links. We implement the proposed architecture in ndnSIM v2.9, extending the NDN forwarding plane to support CRT-based multi-source data retrieval. Simulation results demonstrate that our solution significantly improves content delivery and energy efficiency compared to existing NDN retrieval schemes, particularly under lossy wireless conditions.

Federated Learning (FL) is gaining momentum as a prominent solution to enable on-device training while preserving data privacy. In a typical FL architecture, the function of aggregating model updates from thousands of client devices, each locally training the shared model on its own data, is placed at a dedicated node within the edge domain. Such a placement decision remains static throughout the entire training process. However, the time-varying, heterogeneous, and limited availability of computing capabilities in edge environments, coupled with fluctuating populations of participating clients across training rounds, calls for dynamic per-round placement of the aggregation function. To further optimize the usage of heterogeneous resources while accounting for FL aggregation tasks, which may require different scales of computing capabilities, a fine-grained allocation of computing resources is needed across edge nodes. The objective of this work is to jointly optimize the FL aggregation function placement at each training round and the allocated computing resources in order to minimize the overall per-round training time while not exceeding the computing capabilities of each edge node. An optimization problem is formulated, and an efficient and effective heuristic algorithm is proposed, based on local search techniques. Experimental results demonstrate that the proposed solution significantly outperforms benchmark approaches, achieving a reduction in per-round training time and bandwidth consumption. Under the considered settings, the proposal yields up to a 27% gain in training time compared to methods that optimize aggregation placement only, without performing fine-grained resource allocation.

In-Network Computing (INC) is emerging as a key enabler of Sixth-Generation (6 G) systems, allowing programmable network nodes to provide not only connectivity, but also storage and processing across the cloud-to-edge continuum. Machine learning (ML) tasks, particularly Deep Neural Network (DNN) inference, stand to benefit significantly from this shift. Under the Split Inference (SI) paradigm, different layers of a DNN can be distributed across multiple in-network nodes that cooperate with the end-device requesting inference. In this work, we explore the potential of Named Data Networking (NDN) as an enabler for in-network SI. We demonstrate how NDN’s native features, such as in-network caching and routing-by name, can reduce inference delays and improve robustness under lossy edge connectivity, compared to traditional host-centric networking. Simulation results validate the effectiveness of NDN-based innetwork SI, highlighting its potential to enable resilient and efficient ML services in future 6 G environments.

Split inference (SI) has been devised as a valuable solution to enable the execution of computation-heavy deep neural network (DNN) inference models on resource-constrained edge devices. The different layers of a DNN model are distributed to one or several nodes (mainly edge/cloud servers) cooperating with the end-device requesting the inference. Boosted by the sixth generation (6 G) trends, programmable network nodes equipped with computing, caching and intelligence capabilities can be involved in such a cooperative task. In this work, we propose Named Data Networking (NDN) as a key enabler of in-network SI. Its connectionless communication model coupled with routing-by-name and native in-network caching capabilities can facilitate dynamic splitting operations on nodes throughout the cloud-to-things continuum. We show how NDN design principles and communication primitives can be leveraged to support in-network SI. Then, preliminary results are reported to showcase the benefits of the conceived proposal.

The rapid proliferation of connected devices in Internet of Things (IoT) ecosystems has created significant challenges for efficient service discovery, with traditional approaches suffering from scalability limitations and excessive resource consumption. This paper presents a novel utility-driven service discovery model for Social Digital Twins (SDTs) environments that optimises resource utilization while maintaining discovery effectiveness. Our proposed model introduces a dynamic utility function that evaluates the contribution of each social relationship among digital twins to the discovery process, enabling nodes to make intelligent forwarding decisions with only local knowledge. By balancing exploitation of high-utility paths with exploration of potentially valuable alternatives, the system adapts to network conditions and service distribution patterns. Extensive simulations on a large-scale SDT dataset demonstrate that our service-specific utility approach achieves 41.7% efficiency, outperforming flooding-based techniques (2.54% efficiency) by a factor of 16.4 while maintaining high service discovery rates. The model shows particular effectiveness in dynamic environments where service availability fluctuates, making it suitable for next-generation IoT deployments where scalability and resource efficiency are critical concerns.

Federated Learning (FL) is gaining momentum as a promising solution to enable the efficient and privacy-preserving distributed training of Machine Learning (ML) models. Unlike centralized ML solutions, only the ML model and its updates are transferred between the clients and the aggregator server, eliminating the need to share large datasets. Notwithstanding, poor connectivity conditions experienced over the path that interconnects the FL clients and the aggregator server, either due to (wireless) channel losses or congestion, may deteriorate the training convergence. Several methods have been devised to reduce the training duration, primarily by minimizing data transfer through the design of ML algorithms at the application level. However, these solutions still exhibit unsettled issues, as they may only reduce the communication footprint but do not improve the communication process as a whole. Differently, in this work, our aim is to improve FL data exchange from a networking perspective by promoting Information Centric Networking (ICN) approaches rather than host-centric TCP/IP-based solutions. To this aim, we analyse the impact that host-centric transport protocols as well as ICN approaches have on the FL performance, in terms of duration of the model training and exchanged data (model and updates) load, under different channel loss settings. We show that ICN-based FL solutions significantly reduce the network data load and decrease the duration of the training round by up to an order of magnitude for high channel loss rates.

Federated Learning (FL) enables multiple devices (clients) training a shared machine learning (ML) model on local datasets and then sending the updated models to a central server, whose task is aggregating the locally-computed updates and sharing the learned global model again with the clients in an iterative process. The population of clients may change at each round, whereas the node executing the aggregation function is typically placed at an edge domain and remains static until the end of the overall FL training process. Indeed, the computing capabilities of the edge node hosting the aggregation function and the distance (latency) of such a node from the selected clients can highly affect the convergence rate of the FL training procedure. Moreover, the heterogeneous time-varying capabilities of edge nodes, coupled with the dynamic client population selected at each round, call for the optimal dynamic placement of the aggregation function across the available nodes in an edge domain. In this work, we formulate an optimization problem for the placement of the FL aggregation function, which aims to select at each round the edge node able to minimize the overall per-round training time, encompassing the aggregation time, the local training time at the clients and the time for exchanging the global model and the model updates. A time-efficient greedy heuristics is proposed, which is shown to well approximate the optimal solution and outperform the considered benchmark solutions.

Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) modules alongside cutting-edge communication and networking architectures, DTs will be able to develop cognitive and social skills and build relationships with each other, thus facilitating the sharing of services and experience. However, in current implementations, DTs typically engage with their physical counterpart only, for predictive main-tenance and optimization, while protocols for inter-twin commu-nications and service discovery are still unexplored. In this paper, we focus on service discovery and provisioning in DT networks hosted at the network edge. In our design, the Social Internet of Things (SIoT) notion is applied to build social networks among DTs and, in parallel, name-based primitives, according to the Information Centric Networking (ICN) paradigm, are consid-ered to support inter-twin interactions. Two distributed name-based service discovery mechanisms are envisioned: a social-driven scheme, leveraging friendship and similarities among DTs' names, and a network-driven scheme, leveraging the ICN forwarding fabric only. A performance evaluation shows the benefits of the conceived solution in terms of reduced discovery latency compared to legacy centralized approaches.

Edge computing is aimed to support compute-intensive data-hungry interactive applications which can hardly run on resource-constrained consumer devices and may suffer from running in the cloud due to the long data transfer delay. The edge network nodes' heterogeneous and limited (compared to the cloud) capabilities make the computing task placement a challenge. In this paper, we propose a novelin-networktask placement strategy aimed at minimizing the edge network resources usage. The proposal specifically accounts fortime-limited reusablecomputing tasks, i.e., tasks whose output can be cached to serve requests from different consumers for a certain time. Caching such results, during their time validity, achieves the twofold benefit of reducing the service provisioning time and improving the edge resource utilization, by avoiding redundant computations and data exchange. The devised strategy is implemented as a network application of a Software-defined Networking Controller in charge of overseeing the edge domain. We formulate the optimal task placement through an integer linear programming problem, and we define an efficient heuristic algorithm that well approximates the solution achieved through a standard optimal solver. Achieved results show that the proposal successfully meets the targeted objectives in a wide variety of simulated scenarios, by outperforming benchmark solutions.

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection.

The increasing volume of Internet traffic is pushing the Internet Service Providers to deploy distributed caching services at the network edge, close to the end users, in order to speed up the data retrieval and reduce the bandwidth demands. In parallel, centralized paradigms like Software Defined Networking (SDN) are considered to optimize network management while supporting a variety of network applications like routing, load balancing and caching. In this paper, we extend the SDN control plane to support a novel content caching strategy. We consider a softwarised edge network domain where SDN nodes, augmented with storage capabilities, cache incoming data with the twofold target of limiting the retrieval delay and the inter-domain traffic. The caching decision is taken in a centralized mode by the SDN Controller, according to a newly defined content-driven closeness centrality metric, which identifies the importance of the SDN nodes as cachers based on their proximity to the majority of the clients requesting the most popular contents. Simulation results show the superiority of the solution in terms of higher cache hits and reduced latency, when compared against benchmark caching strategies.

The Internet of Vehicles (IoV) can deliver services for intelligent transportation systems. However, it typically relies on the exchange of sensitive information, including passwords and personal data, which makes it vulnerable to many security risks, such as phishing attacks, which have largely increased in the last decade. Cryptography-based security measures look as a form of protection to preserve sensitive information, but they can be bypassed by inside attackers and, in addition, they increase the burden on the network. To tackle the aforementioned issues, in this paper, we present an approach to control phishing attacks in the IoV environment, based on Machine Learning (ML) and Deep Learning (DL) techniques.

With the outbreak of COVID-19, and its terrible and fast spread among communities, contact tracing methods have become crucial to protect people. However, conventional mechanisms, for instance based on the manual search of close contacts, lack of high efficiency due to the high time consumption. The research community is therefore exploring new ways to track contagious diseases by exploiting modern communications paradigms and technologies. In this paper, we propose a new contact tracing method based on Online Social Network (OSN) platforms. In our design, contact detection occurs in real-time by means of traditional proximity approaches. Then, a likely future contact forecast is notified through OSN communities.

Federated learning (FL) is gaining momentum as a prominent solution to perform training procedures without the need to move sensitive end-user data to a centralized third party server. In FL, models are locally trained at distributed end-devices, acting as clients, and only model updates are transferred from the clients to the aggregator, which is in charge of global model aggregation. Although FL can ensure better privacy preservation than centralized machine learning (ML), it exhibits still some concerns. First, clients need to be properly discovered and selected to ensure that highly accurate models are built. Second, huge models may still require to be exchanged from the aggregator to all the selected clients, incurring a not negligible network footprint. To tackle such issues, in this paper, we propose a framework built upon in-network caching, multicast and name based data delivery, natively provided by the Named Data Networking (NDN) paradigm, in order to support client discovery and aggregator-clients data exchange. Benefits of the proposal are showcased when compared to a conventional application-layer solution.

With more than 75 billions of objects connected by 2025, Internet of Things (IoT) is the catalyst for the digital revolution, contributing to the generation of big amounts of (transient) data, which calls into question the storage and processing performance of the conventional cloud. Moving storage resources at the edge can reduce the data retrieval latency and save core network resources, albeit the actual performance depends on the selected caching policy. Existing edge caching strategies mainly account for the content popularity as crucial decision metric and do not consider the transient feature of IoT data. In this article, we design a caching orchestration mechanism, deployed as a network application on top of a software-defined networking Controller in charge of the edge infrastructure, which accounts for the nodes’ storage capabilities, the network links’ available bandwidth, and the IoT data lifetime and popularity. The policy decideswhich IoT contentshave to be cached andin which nodeof a distributed edge deployment with limited storage resources, with the ultimate aim of minimizing the data retrieval latency. We formulate the optimal content placement through an Integer Linear Programming (ILP) problem and propose a heuristic algorithm to solve it. Results show that the proposal outperforms the considered benchmark solutions in terms of latency and cache hit probability, under all the considered simulation settings.

Software Defined Networking (SDN) and Named Data Networking (NDN) have been recently advocated as complementary paradigms to improve content distribution in the next-generation Internet. On the one hand, SDN offers a centralized control plane that can optimize routing decisions; on the other, the distinctive features at the NDN data plane, such as name-based delivery, in-network caching, and stateful forwarding, simplify data dissemination. In the integrated design, when a request cannot be handled locally at the NDN data plane in the Forwarding Information Base (FIB), the SDN Controller is contacted to inject the forwarding rule. Decisions such as which rules need to be stored in the node and for how long deeply affect the packet forwarding performance. This paper debates about the issues related to forwarding rules in the FIBs of SDN-controlled NDN nodes, by specifically accounting for their name-based nature, representing a key novelty compared to legacy SDN implementations. Quantitative results are reported to showcase the impact of crucial parameters, like the content popularity, the content requests rate, the table size, on the FIB performance in terms of valuable metrics (e.g., hit ratio, rejected requests, incurred signaling with the Controller).

The latest advances in mobile devices and the widespread diffusion of networked objects are driving the evolution of traditional Mobile Cloud Computing (MCC) systems toward a new framework where storage, computing, sensing, and other device capabilities are offered as a service at the network edge. This visionary scenario, encompassing heterogeneous resources generated, shared, and consumed everywhere in the network, requires innovative architectural and protocol design. In this context, can the approaches recently formulated in the Future Internet research arena (e.g., middleware-based virtualization, Information Centric Networking, and Software-Defined Networking/Network Function Virtualization) support the evolution of mobile cloud systems? This paper provides an affirmative answer by proposing Future-MCC, a novel architecture that capitalizes on such promising approaches and re-thinks (when needed) their philosophy to better fit the evolution of MCC systems. The performance of Future-MCC has been investigated in a representative heterogeneous and dynamic Smart City scenario. Computer simulation results clearly demonstrate that the proposed solution ensures (i) a reduction of the bandwidth requirements spanning from 66 to 91 percent and (ii) an average energy saving equal to 99 percent with respect to a conventional cloud computing platform.

Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements in virtualization technologies, edge network devices are rapidly evolving from simple forwarders to softwarized infrastructures augmented with computing and storage capabilities. Such a trend blurs the distinction between IT and telco domains and paves the way for the integrated management of network and computing resources. In this paper, we focus on the interplay of the Software Defined Networking (SDN) and Named Data Networking (NDN) paradigms as key drivers for the orchestration of computing services in softwarized edge infrastructures. We propose a new framework that brings out the best of the SDN centralized intelligence to take “smart” decisions and inject rules for service allocation (e.g., retrieve input data, execute a function over them), and the best of the adaptive NDN forwarding plane and its native in-network caching to request and deliver services by name. Within the framework, we devise a service allocation strategy that aims at selecting as an executor, among the potential candidates, the one which is able to guarantee the shortest service provisioning delay for each service request, while accounting for the network topology, the links status, and the available computing resources in the edge nodes. Performance evaluations testify to the superiority of our proposal against benchmarking solutions from the literature under different operation settings.

Edge computing is a key paradigm to offload the core network and effectively process massive Internet of Things (IoT) raw data without sending them to the cloud. This paradigm normally relies on a set of purpose-built and pre-planned servers, which host storage and processing resources to provide IoT services close to the data sources, thus saving core network resources and offloading the remote cloud infrastructure. In this paper, we propose to turn the network edge into a dynamic, distributed computing environment that supports the provisioning of IoT services, by exploiting the recent evolution of named data networking (NDN), supporting both name-based data retrieval and computation. Specific name structure and novel NDN forwarding mechanisms are designed; a distributed strategy is also engineered to select the service executor among edge nodes, with the objectives to: 1) limit the raw IoT data traffic crossing the network and 2) allocate the service execution according to the nodes' available processing resources. Numerical analysis shows that the performance of the proposed framework approaches the one of the optimal solution of a formulated integer linear programming problem. System-level ndnSIM simulations confirm that the proposal also outperforms the considered state-of-the-art benchmark solutions in terms of service provisioning time.

Named Data Networking (NDN) has been recently extended to enable the discovery and provisioning by name of in-network cloud-like services such as processing and data storage. Such a feature is particularly helpful in distributed edge environments, where mobile end devices can be involved in service offering. In such a context, a challenging issue is to motivate a mobile device to behave as a provider and share its resources (e.g., CPU, memory) in order to assist other end devices (consumers), in wireless proximity, asking for a given service. In this paper, we propose a solution revolving around two concepts: enhanced NDN primitives and a social-driven stimulus for mobile devices to volunteer as service providers. A bio-inspired response function is used by a potential provider's device to rate both its available resources and the social ties with the current consumer's device, established according to the Social Internet of Things (SIoT) paradigm. An early evaluation showcases to which extent the conceived solution allows a consumer to find a nearby provider available to offer its services, under different social neighbourhood settings.

Name-based communication and in-network caching make Named Data Networking (NDN) a promising solution for content dissemination in Vehicular Ad hoc Networks (VANETs). So far, different NDN packet forwarding mechanisms have been proposed, but none of them have considered the idea of a prioritized traffic treatment based on vehicular content type. This is instead the focus of this paper, since priority-based content dissemination is a rather crucial objective in vehicular environments in order to meet the requirements of heterogeneous applications. Based on the NDN hierarchical namespace, we propose specific “name-prefixes” that identify globally understood priorities for vehicular data traffic. A prefix-based prioritized technique is then implemented on top of basic NDN forwarding algorithms. Simulation results show that the proposed enhancement succeeds in achieving differentiated traffic treatment and in reducing the latency of both high and low priority data.

The Information-Centric Networking (ICN) paradigm for the future Internet is fundamentally different from the classic host-centric Internet Protocol (IP). By leveraging unique, persistent and location-independent content names, ICN provides native multicast support, content-based security, in-network caching, and easy data access, which can be especially useful in the Internet of Things (IoT). In this paper, the attention is on the design of an ICN framework tailored to the smart home domain, considered as a major representative of IoT scenarios. The proposed solution encompasses the definition of a flexible and expressive naming scheme that supports data/command exchanges and configuration/ management operations, and also fits the common service models in the smart home domain (i.e., push, pull, multi-party). Use cases are provided to shed light on the system behaviour and preliminarily assess its potential and performance.

Named Data Networking (NDN) is a new architecture for the Future Internet that supports efficient data delivery by directly using content names instead of IP addresses. This content-centric approach can be especially useful for IoT applications, where everyday objects embedding wirelessly connected constrained devices should be accessed, by both other machines and users. In such a context, we focus on the smart home domain and propose a framework called eDomus that (i) leverages functionalities by a popular social network, i.e., Facebook, to allow a user to remotely interact with the home network and (ii) is augmented with NDN concepts to properly monitor the domestic environment. In this paper, we present our framework and our preliminary prototype, by also providing some hints on future work.

Content-Centric Networking (CCN) provides a complete communication framework for data retrieval and dissemination. It leverages innovative naming, security schemes and novel routing strategies, augmented with caching at intermediate nodes. Content requests are forwarded towards the source(s) by direct use of content names (instead of IP addresses), matching the layout of the Wireless Sensor Network. Despite its potential CCN can not be directly applied to wireless environments, specifically resource-constrained sensor nodes. In this paper, a two-tier CCN architecture is proposed to manage the heterogeneity of involved devices (remote server, sink, sensor nodes). CCN is enhanced with some changes to the forwarding strategies to improve data collection.

Content-centric is an emerging networking paradigm conceived for the future Internet. Data retrieval and distribution are based on content names instead of host addresses. In this paper we propose a content-centric architecture for energy-efficient multihop communications in a wireless mesh network and compare its performances against a legacy IP-based approach.

In this paper, a delay tolerant approach for traffic management in LTE over satellite link is presented. Due to the long propagation delays and frequent disruptions, mobile satellite communications pose many challenges, especially to the transport layer when the TCP protocol is used. We design and evaluate the performance of a transport protocol solution based on the DTN architecture in the satellite-LTE system. Our proposal, named Tiny Bundle Layer, acts as an overlay on top of different versions of TCP in order to overcome the satellite link impairments.

In this paper we present a novel multi-criteria network selection algorithm for always best connected service provisioning. It relies on a suitably defined cost function, which at the same time takes into account metrics reflecting both objective, i.e. network related, and subjective, i.e. user preference related, conditions. Point of strength of our proposal is the implementation of the selection algorithm at a middleware layer; this hiding both network cost computation and 4G scenario complexity from user and application layers. The good performance observed is mainly due to the possibility of associating a weight to each cost parameter that is dynamically adapted to user preferences and profile not only on a per-session basis but also within the same session