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.

Infrastructure-assisted automated driving will highly rely on real-time updates received from the network about the surroundings, which demand for ultra-low latency, high-throughput and highly reliable wireless connectivity. In the future, the quality of connectivity will directly impact the driving capability of these autonomous vehicles: lower downlink quality will in fact reduce the situational awareness, forcing the vehicle to, for example, reduce speed and increase safety distance. In this context, we propose a solution which allows a vehicle to choose the route that ensures the best connectivity conditions to reach its destination. Specifically, we consider that different connected and automated driving (CAD) Modes are possible depending on the estimated downlink radio quality, and the route is selected accordingly. Experiments have been conducted through simulations in an urban environment, utilizing the SUMO mobility simulator and the Sionna RT channel modelling tool. The obtained results demonstrate that the network quality can indeed significantly influence route selection, paving the way for future research towards sixth generation (6G) where services and mobility predictions can drive network optimizations.

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.

E-bikes are recognized for their sustainable transportation benefits. However, the higher speeds associated with e-bikes pose an increased risk of potential accidents and hinder fluid riding in swarms with conventional bicycles. In this paper, we analyze the accuracy of maintaining an unknown speed, assess the associated workload, and investigate the self-reported speeds of e-bike cyclists in order to adapt the electric assistance to dynamic speed limits based on the surrounding traffic conditions. Our results from a pilot study with 15 participants show that the accuracy of maintaining a speed limit through active motor control and the associated workload are influenced by factors such as the level of electrical assistance and the perception of motor disengagement. E-bike cyclists using higher levels of electrical assistance demonstrated more accurate target speed maintenance. On average, participants consistently underestimated adapted speed limits, which were also influenced by the level of electrical support.

In the fifth generation (5G) Vehicle-to-Everything (V2X) autonomous mode, vehicles may select radio resources for their packet transmissions without the support of the base station (BS). For periodic traffic, the same resources can be accessed at consecutive reservation intervals to improve the selection procedure. However, potential collisions occurring during this period may remain undetected and persist over time. Previous studies introduced algorithms leveraging in-band full-duplex (FD) transceivers to detect and react to collisions by performing resource reselection. However, as demonstrated in this paper, in more realistic scenarios where part of the data traffic is not periodic, performing reselections after collisions, regardless of the traffic type, may not be effective and could even be counterproductive. To address this issue, this work introduces two novel alternative schemes, each with its own advantages and drawbacks, both based on FD transceivers. Simulation results demonstrate that both solutions effectively handle scenarios with mixed traffic. Without penalising the aperiodic traffic, the range of periodic traffic is improved by up to 38% over the legacy Mode 2 under heavy traffic conditions.

Tele-operated Driving (ToD) is an ambitious use case in the automotive sector that enables the remote operation of vehicles, e.g., when they are located in dangerous environments, for logistics fleet operations. ToD heavily relies on 5G connectivity able to guarantee strict latency, reliability, and bandwidth requirements, for instance through a dedicated network slice. In this paper, we propose a Digital Twin (DT)-assisted radio resource allocation scheme that manages the Radio Access Network (RAN) resources within the ToD network slice. In particular, our contributions leverage interactions between the DT of the RAN and of other ToD players to feed an algorithm for efficient radio resource allocation, based on the actual ToD service demand on the planned driving paths. Early results on a realistic map demonstrate the resource savings achieved by our proposal compared to a static resource allocation scheme.

The Third Generation Partnership Project (3GPP) recently introduced the fifth-generation (5G) new radio (NR) sidelink to enable vehicle-to-everything (V2X) communications supporting advanced safety services. Nevertheless, improvements over the previous generation still pose challenges to meet the reliability and latency requirements of V2X communications, particularly in the allocation of distributed resources, i.e., Mode 2. In Mode 2, vehicles autonomously select radio resources for their message transmissions and can maintain the selected resources for a given reservation period to efficiently handle periodic data traffic. However, potential collisions during this period may remain undetected due to half-duplex communications and unacknowledged broadcast transmissions, resulting in persistent message losses and posing a threat to road safety. This paper aims to improve the 5G NR-V2X sidelink for systems beyond 5G-Advanced by exploiting full-duplex transceivers. We propose a novel medium access control (MAC) scheme where vehicles can detect collisions while transmitting, dynamically adapt the collision detection threshold according to the measured channel load, and react to detected collisions through appropriate resource reselection and retransmission procedures. Extensive simulations conducted under various settings show that this MAC scheme brings substantial performance gains in terms of reliability and latency, compared to the current legacy Mode 2 procedure and a benchmark full-duplex scheme from the literature.

Digital Twins (DTs), which are paired to Internet of Things (IoT) devices to represent them and augment their capabilities, are gaining ground as a promising technology to enable a wide variety of applications in the sixth-generation (6G) ecosystem, ranging from autonomous driving to extended reality and metaverse. In particular, “social” IoT (SIoT) devices, which are devices capable to establish social relationships with other devices, can be coupled with their virtual counterparts, i.e., social DTS (SDTs), to improve service discovery enabled by browsing the social network of friend devices. However, the mobility of SIoT devices (e.g., smartphones, wearables, vehicular on board units, etc.) may require frequent changes in the corresponding SDT placement in the edge domain to maintain a low latency between the physical device and its digital replica. Triggering SDT relocation at the right time is a critical task, because an incorrect choice could lead to either increased delays or a waste of network resources. This work proposes a learning-powered social-aware orchestration that predicts the mobility of SIoT devices to make more judicious migration decisions and efficiently move the paired SDTs accordingly, while ensuring the minimization of both intra-twin and inter-twin communication latencies. Different machine learning (ML) and deep learning (DL) algorithms are used for SIoT device mobility prediction and compared in terms of a wide set of meaningful metrics in order to identify the model that achieves the best trade-off between prediction accuracy and inference times under different scenarios. Simulation results showcase the improvements of the proposal in terms of reduced network overhead (by up to a factor of 3) and intra-twin and inter-twin communication latency (by up to 10%) compared to a more traditional solution, which activates the relocation of the DTs at fixed time intervals following periodic optimizations.

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.

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.

Applications envisioned to enable connected and autonomous vehicles entail the exchange of data through reliable and low-latency direct communications between vehicles. The current specifications for the cellular-vehicle-to-everything (C-V2X) technology can still barely match such requirements. To tackle this issue, we investigate here the joint application to the C-V2X sidelink with autonomous resource allocation of two techniques that are being considered for the evolution of cellular systems, i.e., in-band full-duplex (FD) and non-orthogonal multiple access (NOMA). In particular, we consider FD capabilities to detect ongoing collisions while transmitting and thus resolve persistent resource allocations, which are causing repeated errors at the neighboring receivers. Regarding NOMA, we consider the use of successive-interference cancellation (SIC) for the decoding of multiple signals when different vehicles are transmitting simultaneously using the same resources, thus converting packet losses into improved spectral efficiency. The investigation is based on the latest fifth generation (5G) New Radio (NR)-V2X specifications and focuses on broadcast transmissions, which are expected to cover the majority of the exchanged traffic in V2X contexts. As shown through simulations in large-scale scenarios, the two techniques, both relying on signal processing at the receiver to cancel the (self or successive) interference, provide complementary advantages under different conditions. FD is in fact very effective to improve the delivery performance at short distances when periodic data traffic is assumed, while SIC-based NOMA improves the reception probability at medium distances both under periodic and aperiodic data traffic conditions.

The analysis of data coming from massively deployed Internet of Things (IoT) devices pave the way to a myriad of intelligent applications in several vertical domains. Federated Learning (FL) has been recently proposed as a prominent solution to train Machine Learning (ML) models directly on top of devices (FL clients) generating data, instead of moving them to centralized servers in charge of training procedures. FL provides inherent benefits, mainly in terms of privacy preservation and reduction of network congestion for datasets exchange. Despite the huge recent research efforts, it still faces challenges for a practical implementation effectively targeting low communication footprint, robustness and interoperability. To fill this gap, in this work we propose a novel comprehensive framework built upon the Message Queue Telemetry Transport (MQTT) publish/subscribe messaging protocol and the Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) semantics to facilitate FL operations and make them more suited to handle IoT devices acting as FL clients. The viability of the proposal as well as its communication efficiency compared to a literature solution are evaluated through a realistic Proof-of-Concept (PoC) under different link settings and for different datasets.

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.

Efforts are underway to finalize the fifth generation (5G) vehicle-to-everything (V2X) communication technology. Direct communication among vehicles over the sidelink interface through the autonomous selection of radio resources is among the main and more challenging capabilities of such a connectivity solution. Nonetheless the crucial enhancements conceived by the Third Generation Partnership Project (3GPP) in Release 16, the autonomous resource allocation (a.k. a. Mode 2) still suffers from collisions due to the wrong estimation of the resource occupancy status enforced by half-duplex (HD) transceivers. In this paper, we argue in favour of the usage in vehicles of in-band full-duplex (FD) transceivers, gaining momentum in the research towards sixth generation (6G) systems thanks to their simultaneous transmission and reception capability. Exploiting FD, we propose to enhance the autonomous mode by detecting collisions and triggering resource reselection procedures in a more conscious manner. Simulation results show that the conceived proposal allows sidelink V2X to achieve significant improvements in terms of reliability and timeliness when compared against the legacy Mode 2.

The increasing processing capabilities of massively deployed Internet of Things (IoT) and edge devices coupled with the rising demands for fast and autonomous decision-making pave the way for accurate Machine Learning (ML) model training in proximity to where data is collected, as proposed by the Federated Learning (FL) paradigm. The devices contributing to the local training (clients) are typically large in number and differ in terms of computing and battery capabilities, experienced link quality conditions and owned datasets. If clients are not properly selected by the entity in charge of aggregating the individual models (FL server) both the convergence speed and the accuracy of the resulting model can be affected. The interest in the literature for the optimization of the client selection policies is manifest, but less attention has been devoted to the design of the practical implementation of such policies with focus on the client discovery procedures. In order to fill this gap, an edge-based framework is proposed to improve the FL client discovery procedures by leveraging (i) the Message Queue Telemetry Transport (MQTT) protocol for FL clients-server interactions aimed to retrieve the capabilities of the potential clients and (ii) the Open Mobile Alliance (OMA) Lightweight-Machine-to-Machine (LwM2M) for the semantic description of such capabilities. A proof-of-concept is provided to assess the communication footprint incurred by the proposal.

As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.

Vehicle-to-everything (V2X) connectivity is deemed crucial to enable upcoming cooperative automated driving applications. The Cellular-V2X (C-V2X) technology supports direct interactions among vehicles over the sidelink interface. According to the autonomous mode, vehicles select sidelink radio resources for their message transmissions in a completely distributed manner, without the support of the base station. The majority of works focused so far on periodic message transmissions, for which sidelink resources can be semi-persistently reserved after a channel sensing procedure. In this paper, we take a step forward by considering both periodic and high-priority aperiodic messages, such as sporadic decentralized environmental notification messages (DENMs), competing for the same resources. The impact of some new sidelink features devised in 5G-V2X (Release 16) for the autonomous mode, such as the new radio (NR) flexible numerology, the short-term sensing, the pre-emption mechanism, are preliminarily investigated in this paper. They are coupled with a newly proposed resource reservation scheme for the repetitions of DENMs. Results showcase the improvements of our proposal in terms of higher DENM reliability achieved with little-to-none additional signaling, with a negligible impact on periodically transmitted messages.

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.

The research into wireless communication and mobile computing is called to formulate novel smart mobility solutions to improve the quality of a citizen's life in smart cities. In such a context, in this paper we elaborate on the role of technologies like multi-access edge computing (MEC), Internet of Things (IoT) messaging protocols, such as Constrained Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT), and virtualization (i.e., digital twins) in the design of a framework enabling the collection and processing of data about the mobility of commuters and public transport vehicles. Such data have the purpose to feed mobility monitoring and transport planning solutions. A Proof-of-Concept (PoC) is developed to validate the framework under realistic experimental settings. Results in terms of efficiency and effectiveness of the considered messaging protocols are reported.

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).

Current vehicular communication technologies were designed for a so-called phase 1, where cars needed to advise of their presence. Several projects, research activities and field tests have proved their effectiveness to this scope. But entering the phase 2, where awareness needs to be improved with non-connected objects and vulnerable road users, and even more with phases 3 and 4, where also coordination is foreseen, the spectrum scarcity becomes a critical issue. In this work, we provide an overview of various 5G and beyond solutions currently under investigation that will be needed to tackle the challenge. We first recall the undergoing activities at the access layer aimed to satisfy capacity and bandwidth demands. We then discuss the role that emerging networking paradigms can play to improve vehicular data dissemination, while preventing congestion and better exploiting resources. Finally, we give a look into edge computing and machine learning techniques that will be determinant to efficiently process and mine the massive amounts of sensor data.

The first set of specifications for the Cellular-Vehicle-to-Everything (C-V2X) technology has been issued by the Third Generation Partnership Project (3GPP) in Release 14 to enable the exchange of periodic cooperative awareness messages supporting basic safety applications. Enhancements towards 5G New Radio (NR) V2X are under study in Release 16 in order to support advanced vehicular use cases with stricter latency and reliability demands, and different traffic patterns (e.g., aperiodic messages, unicast delivery). In this paper, we focus on the support of aperiodic traffic like Decentralized Environmental Messages (DENMs), which are sent out asynchronously to vehicles alerting them to road hazard events such as anomalous traffic conditions, road-works or signal violation, emergency braking. DENMs can be repeatedly transmitted in a relevant area to increase message reliability during the event lifetime. Simulations have been conducted to analyze scenarios where periodic and event-triggered messages share the same radio resource pool(s) in the autonomous sidelink mode of NR V2X. The main parameters of the autonomous resource allocation mechanism affecting the DENM delivery have been investigated as well as the benefits of DENM repetitions, while also considering their impact over periodically transmitted messages, under different simulation settings.

Broadcast traffic in IEEE 802.11 vehicular networks is known to suffer from poor performance due to the lack of recovery mechanisms from packet losses, based on the rules of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. This may have a detrimental impact on cooperative vehicular safety applications that build on the reliable regular broadcasting of status messages by vehicles in a local neighborhood. Full-Duplex (FD) techniques can improve the broadcast CSMA/CA performance by letting a sending vehicle sense the channel while transmitting, thus enabling “collision detection”. The vehicle, consequently, can abort the packet prone to collision and reattempt a later transmission. In this paper, we define a stochastic geometry model that captures the collision detection capability of FD-enabled vehicles, while accurately characterizing the interference power generated by other vehicles on a road segment, and the dynamics of the backoff mechanism used for broadcast packet retransmissions. The model provides helpful insights into the FD broadcast CSMA/CA behaviour, highlighting a clear relationship between the settings for the carrier sense threshold and the collision detection threshold and the number of covered receivers on the road.

Mobility as a Service (MaaS) implies the integration of different transport services in a unique platform accessible by commuters on demand. Collection and processing of data concerning the mobility of customers is crucial to calculate trips options satisfying users' needs and preferences. In this paper, we propose to exploit Multi-access Edge Computing (MEC) facilities to more efficiently deploy MaaS solutions. Specifically, we design a MEC-based MaaS framework that is fully compliant with the Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) protocol. The OMA LwM2M server hosted in the MEC platform continuously collects data from the commuters, and uses native MEC applications to provide value-added services. The OBSERVE extension of the Constrained Application Protocol (CoAP) is used to reduce energy consumption during data collection from the OMA LwM2M clients installed in the mobile user devices. Preliminary results are collected that show the performance of the proposed MaaS framework, integrated with a mobility generator tool (i.e., SUMO) that emulates the commuter paths.

The Multi-access Edge Computing (MEC) paradigm and the Cellular Vehicle-to-Everything (C-V2X) technology prove to be good candidates to address the vehicular applications' demands of high-performing connectivity and low-latency access to computing and storage resources. MEC provides cloud-like resources at the network edge, i.e., at the Multi-access Edge (ME) host. While vehicles move around, a service application instance running on a ME host may be triggered to move to another ME host to better support the application's demands. As a side effect, this migration may undermine service continuity. In this paper, we refer to the latest available ETSI MEC and 3GPP C-V2X specifications to investigate the issue of service migration between ME hosts in the context of vehicular communication. Early experimental results provide measures of the service migration latencies when ME applications run as Docker containers.

In recent years identifier/locator splitting (ILS) has been proposed as a promising future Internet solution to the so-called “semantic overload of addresses” problem. It is currently under debate whether the ILS approach, which entails identifier-to-locator resolution procedures, could ensure scalability, session continuity, and mobility across heterogeneous multiaccess networks as demanded by Internet of Things (IoT). In this paper, it is proposed to exploit the Social IoT concept to address the above issues. More specifically, a scheme is introduced that browses the social graph of devices to find information about the locator of the intended destination. Results show that the proposed solution outperforms the alternative ILS approaches in terms of number of hops and latency incurred to accomplish the resolution procedure, at the cost of a slight increase in the storage demands to track social relationships.

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.

The Identifier/Locator Splitting (ILS) paradigm has been proposed in the future Internet research arena to address the semantic overload of IP addresses. In this paper the integration of the Social Internet of Things (SIoT) concept into ILS solutions is investigated. Indeed, SIoT has been recently argued as a promising approach to improve the performance of identifier-to-locator mapping procedures. More specifically, we describe the general approach, propose an architecture and show some preliminary performance results obtained through simulations. The proposed SIoT-enabled ILS architecture relies on distributed repositories of virtual counterparts of entities that store Friendship Tables specifically introduced to enable the mapping information to be retrieved by surfing the social graph. Achieved results in a smart campus scenario show that the proposed approach achieves better performance, in terms of number of hops, than a mapping solution based on Distributed Hash Tables (DHT).

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.

On the path to zero fatalities on the roadways, all vehicles have to periodically broadcast cooperative awareness messages (CAMs) in a timely and reliable manner, even in areas of high traffic density. The carrier senses multiple access with collision avoidance (CSMA/CA) scheme of IEEE 802.11, the de-facto standard for vehicular communications, is known to offer no reliability to broadcast packets that cannot be acknowledged, and to poorly perform at high network load due to collisions and interference. In this paper, an enhanced CSMA/CA protocol is analyzed for vehicular networks, which improves the CAM timeliness and reliability by leveraging full-duplex (FD) transceivers on board. FD devices can listen to the channel while transmitting, thus making collision detection viable. A FD vehicle can detect a CAM collision while sending, promptly abort the packet and retransmit it later. Results achieved through an analytical model under mathematically tractable assumptions, and through extensive system-level simulations in a complex urban environment, show the effectiveness of the protocol to cope with direct collisions, especially in high traffic areas, paving the way towards the realization of cooperative automated driving.

The efficient usage of multiple channels allocated for vehicular communications is deemed crucial to support the increasing spectrum demand of current and upcoming Intelligent Transportation Systems (ITS). In this context, an accurate modeling of adjacent channel interference (ACI) is needed to better understand its adverse effects on simultaneous communications on different channels. In this paper, we modeled ACI in the simulation framework Veins by strictly following the standard specifications in what concerns spectral emission masks. The model was validated in a set of experiments using off-the-shelf chipsets and a software defined radio. We then studied ACI effects at the physical and packet levels in multi-channel vehicular networks through extensive simulations. Our results clearly indicate that slightly larger delays and significantly lower reliability can be experienced when ACI is modeled. Insights are also provided about the usability of the devised ACI simulation model in future studies.

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.

Reliable and timely exchange of periodic beacons, a.k.a. cooperative awareness messages (CAMs), in the one-hop neighborhood is essential in vehicular networks to support cooperative driving applications. The problem is that broadcasted CAMs are neither acknowledged nor retransmitted in case of failure, e.g., due to channel errors or collisions, and this makes data transmission unreliable and without any delivery guarantee. In this paper, we propose in-band full-duplex communications to improve the reliability of vehicle-to-vehicle beacon exchanges. The FD capability to simultaneously transmit and receive on the same channel can be beneficially exploited by a vehicle to detect a collision while transmitting, and to promptly retransmit to recover from the packet failure. Achieved simulation results show that the proposed solution provides several improvements in terms of reliability and timeliness in CAMs delivery when compared against a naïve half-duplex broadcasting and a simple CAM repetition technique.

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.

Multiple channels have been allocated in the 5 GHz spectrum for vehicular communications in USA and in Europe. Due to the limited spectrum, simultaneous communications may occur over nearby channels and be affected by adjacent channel interference (ACI). To protect safety messages delivered on the control channel (CCH), the most likely approach is to prevent the use of adjacent channels with the consequence of spectrum resources wasting. In this paper we identify temporarily stopped vehicles (e.g., at the gas station for refuelling) as users that are potentially allowed to transmit on a channel that is adjacent to the CCH. A simulation study has been conducted to evaluate the viability of our proposal. Achieved results show that the two conflicting objectives of improving spectrum utilization and not penalizing safety messages can be successfully met, provided that the transmission power on the adjacent channel is adequately tuned and the stopped vehicle is at a sufficient distance from the vehicle that is using the CCH.

The IEEE 802.11p/WAVE family of standards has been designed to provide multi-channel communications for vehicular use. Despite the increasing interest on vehicular networking, further efforts are still required to improve realism of simulations, in order to account for the latest standards and for physical-related phenomena. In this paper, we aim at overhauling the physical and MAC layers of ns-2 to include the last recommendations on frequency allocation, the effects of channel impairments (fading, obstructions, in-channel and adjacent channel interference), an enhanced receiver design, and the WAVE multi-channel related effects.

The 802.11p standard has been recently ratified to provide Wireless Access in Vehicular Environments (WAVE). According to the WAVE alternating channel access scheme, singleradio devices must tune into a common frequency during the control channel (CCH) interval, to exchange safety and control packets, and regularly (every 50 ms) switch to one of the available service channels (SCHs) for non-safety data exchange. In order to improve non-safety data delivery performance, the extended access mode has been recently proposed by the standardization groups in order to allow vehicles to stay tuned on the SCH as long as they need before switching on the CCH. In this paper we identify some drive-thru scenarios as possible use cases for the extended access scheme, and propose some enhancements to this scheme with the aim of improving channel utilization and non-safety applications performance without penalizing the delivery of safety messages on the CCH.

Despite the massive research effort in the design of reliable techniques for dissemination of information in vehicular ad hoc networks, only a few works have investigated performances when considering the multichannel operation of the recently published IEEE 802.11p/WAVE standard. Moreover, most of the works focused on highway scenarios, basically due to the lack of scalable models simultaneously representing fading and obstructions, and providing a solution for realistic simulations in urban scenarios. In this paper, by exploiting a propagation model accounting for radio obstructions recently proposed in literature, the performance of IEEE 802.11p/WAVE is studied in an urban scenario where providers are mobile nodes. The achieved results show that the effectiveness of mobile providers is heavily affected by the urban obstructions (e.g., buildings), in terms of both coverage and service quality.

Broadcasting of data and control packets is expected to be crucial in vehicular environments. Both safety-related and non-safety applications rely on broadcasting for the exchange of data or status and advertisement messages. According to the specifications of the IEEE 802.11p/WAVE (Wireless Access in Vehicular Environments) standard, vehicles tune into a common frequency during the control channel (CCH) interval, where most of the broadcasting traffic is expected to be delivered. The rest of the time, vehicles switch to one of the available service channels (SCHs) for non-safety related data exchange. Although the WAVE channel switching could heavily affect the network performance, it has not been widely investigated in the literature. In this paper, an analytical model is designed for characterizing the losses of broadcast packets by explicitly accounting for the WAVE channel switching. Loss probabilities have been derived as a function of contention window size, number of vehicles and channel errors.

A wide variety of applications aiming to improve traffic efficiency or to provide information/entertainment to passengers in vehicles are intended to be delivered in Vehicular Ad hoc Networks in the near future. Most of these applications require vehicles to access a remote network. However, due to the high costs for deploying and maintaining an ubiquitous roadside infrastructure, just a few stationary Road-side Units (RSUs) are expected to be installed, which provide discontinuous and shortlived network connections. In this paper, the feasibility of vehicle-to-roadside communications is investigated when considering features and capabilities of the 802.11p/WAVE standard for vehicular environments. We expect that, by complementing advertisement repetitions from RSUs with smart piggybacking of some RSU parameters in the beacons transmitted by all vehicles (On board Units, OBUs), the RSU-awareness can be improved and RSU-OBU connection lifetimes can be lengthened.

A wide variety of applications aimed to improve road safety and traffic efficiency or to provide comfort/entertainment to passengers in vehicles are intended to be delivered in VANETs (Vehicular Ad hoc Networks) in the near future. Most of these applications require vehicles to access a remote network. However, due to the high costs needed for deploying and maintaining an ubiquitous roadside infrastructure, just a few stationary access points are expected to be installed, which provide partial network coverage; hence multihop communications must be enforced in order to provide remote connectivity to vehicles on the road. The upcoming IEEE 802.11p/WAVE (Wireless Access for Vehicular Environment) standard relies on a multi-channel architecture to deliver both safety and non-safety applications to vehicles, but it does not explicitly support multihop data delivery. In this paper some main design issues are investigated which deal with the provision of multihop communications in 802.11p/WAVE networks. A set of forwarding schemes compliant to the draft standard is specified and analyzed, which aim to improve data delivery and delay performances, by reducing the overhead incurred for relaying packets.

Broadcast/multicast is a service of paramount importance for wireless users, which poses several serious challenges. Many multicast applications, such as multicasting streaming or alarm signalling, require a reliable and efficient medium access control (MAC) layer. However, the current IEEE 802.11 protocol does not offer any MAC layer recovery mechanisms on unsuccessfully delivered broadcast/multicast frames. Consequently, lost frames cannot be detected, hence retransmitted, and this may deteriorate the quality of broadcast/multicast services. To address these issues, we propose a reliable multicast MAC protocol for wireless multihop networks, which aims at providing high packet delivery ratio as well as low control overhead and multicast delivery delay.

Mobile devices provided with cellular and short-range radio network interface cards offer the user potentials for new communication paradigms based on a cooperative behavior. The idea is that devices in close proximity exchange, through available short-range links, complementary shares of information downloaded from the cellular connections. Results in the literature have proven equal benefits for all partners in terms of energy consumption, achieved data rate, and transfer time, under homogeneous conditions. Notwithstanding, there could be heterogeneous cooperative scenarios in which gains are not perceived, unless novel smart policies for the content splitting among cooperating entities are implemented. In this paper we design a novel cooperation strategy based on a multi-criteria optimization algorithm for the bargaining of the content fraction each cooperating unit shall download according to the predicted cellular throughput. It will be shown that it is able to always match the expectations of the cooperating partners.

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