To support multiple AI-based applications, mobile systems need to collaboratively execute DNN architectures on heterogeneous AI accelerators. At the same time, the increasing DNN complexity and high degree of diversity in workloads on multichip module (MCM) accelerators are pushing AI processing off mobile nodes onto the edge. This has made computationally intensive, edge-based solutions the dominant approach for the deployment of modern neural networks. However, the rigid structure of fully-executed DNNs fails to align with the modular nature of MCM architectures, limiting their potential for efficient execution. In this paper, we introduce CLEAR, a novel optimization framework based on geometric programming that leverages both transformer-based and more canonical DNNs with early exits. CLEAR enables fast, coordinated decisionmaking across DNN design, workload distribution, and resource allocation, with the overarching goal of minimizing inference energy consumption. To our knowledge, this is the first work to integrate dynamic DNN optimization with decisions at both the communication infrastructure and hardware accelerator levels. We evaluate CLEAR using real-world wireless measurements and dynamic DNNs applied to computer vision inference tasks. Our results demonstrate that CLEAR achieves near-optimal performance and reduces energy consumption and resource usage by over 80% and 70%, respectively, compared to its benchmark.

In classification tasks, it is critical to accurately distinguish between specific classes, as misclassifications can undermine system reliability and user trust. In this paper, we study how client selection in both centralized and federated learning environments affects the performance of classification models trained on heterogeneous data. When training datasets across clients are statistically diverse, careful client selection becomes crucial to improve the ability of the model to discriminate between classes, while preserving privacy. In particular, we introduce a novel metric based on conformal prediction outcomes – the conformal correlation matrix – which captures the likelihood of class pairs co-occurring within conformal prediction sets. Unlike the traditional confusion matrix, which quantifies actual misclassifications, our metric characterizes potential ambiguities between classes, thus offering a complementary perspective on model performance and uncertainty. Through a series of examples, we demonstrate how our proposed metric can guide informed client selection and enhance model performance in both centralized and federated training settings. Our results highlight the potential of conformal-based metrics to improve classification reliability while safeguarding sensitive information about individual client data.

The diffusion of GPS-equipped devices has resulted in the generation of vast amounts of spatio-temporal data. This data represents a fundamental resource to conduct analysis on transportation networks. It is therefore of great interest to identify models capable of distinguishing and classifying trajectories to facilitate decision-making processes, congestion prediction and emissions monitoring. However, many existing algorithms necessitate a complex feature engineering process and domain knowledge. In this context, this work proposes a neural network-based approach, which eliminates the need for complicated hand-crafted features, using Gramian angular fields and leveraging possibly pre-trained convolutional neural networks. Therefore, we combine these tools to tackle the challenge of multiclass trajectory classification. We demonstrate the effectiveness of our method on an imbalanced dataset simulated with SUMO by classifying different means of transportation–private car, taxi, bus, pedestrian, motorcycle, bicycle–achieving good results in terms of accuracy and F1 score.

Machine learning (ML) can be often distributed, owing to the need to harness more resources and/or to preserve privacy. Accordingly, distributed learning has received significant attention from the literature; however, most works focus on the expected learning quality (e.g., loss) attained and do not consider the distribution thereof. It follows that ML models are not dependable, and may fall short of the required performance in many real-world cases. In this work, we tackle this challenge and propose DepL, a framework attaining dependable learning orchestration. DepL efficiently makes joint, near-optimal decisions concerning (i) which data to use for learning, (ii) the ML models to use – chosen within a set of full-size models and compressed versions thereof – and when to switch from one model to another, and (iii) the clusters of physical nodes to use for the learning. DepL improves over previous works by guaranteeing that the learning quality target (e.g., a minimum loss) is achieved with a target probability, while minimizing the learning (e.g., energy) cost. DepL has provably low polynomial computational complexity and a constant competitive ratio. Further, experimental results using the CIFAR-10 and GTSRB datasets show that it consistently matches the optimum and outperforms state-of-theart approaches (30% faster learning and 40–80% lower cost).

In many cases, federated learning (FL) has to take place in communication constrained scenarios, where we must select a small number of learning nodes to reduce bandwidth consumption. Furthermore, such nodes may also have computational constraints, i.e., they can store small datasets and process and perform as little data processing as possible. In this context, it is of paramount importance to make node selection decisions before the learning process begins, and without labeling information. We tackle this daunting task through a two-pronged approach, where we (i) introduce a new metric called loneliness, defined on unlabeled datasets, and (ii) propose a novel algorithm called Goldilocks to make node selection decisions and identify the data to be labeled. Through both a theoretical and an experimental analysis, we show that loneliness is strongly linked with learning performance (i.e., test accuracy). Furthermore, our performance evaluation, including three state-of-the-art datasets and a comparison against centralized learning, demonstrates that Goldilocks outperforms approaches based upon a balanced label distribution by providing over $70 \%$ accuracy improvement, in spite of being efficient to compute and not using labeling information.

In5G and beyond (5GB) networks, the notion of cell tends to blur, as a set of points-of-access (PoAs) using different technologies often cover overlapping areas. In this context, highquality decisions are needed about (i) which PoA to use when serving an end user and (ii) how to manage PoAs, e.g., how to set their power levels. To address this challenge, we present Cluster-then-Match (CtM), an efficient algorithm making joint decisions about user assignment and PoA management. Following the human-centric networking paradigm, such decisions account not only for the performance of the network, but also for the level of electromagnetic field exposure to which human bodies incur and energy consumption. Our performance evaluation shows how CtM can match the performance of state-of-the-art network management schemes, while reducing electromagnetic emissions and energy consumption by over 80%.

Theexisting work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its average value. This leads to a poor dependability of the resulting ML models, whose performance may be much worse than expected. We fill this gap by proposing DepL, a framework for dependable learning orchestration, able to make high-quality, efficient decisions on (i) the data to leverage for learning, (ii) the models to use and when to switch among them, and (iii) the clusters of nodes, and the resources thereof, to exploit. For concreteness, we consider as possible available models a full DNN and its compressed versions. Unlike previous studies, DepL guarantees that a target learning quality is reached with a target probability, while keeping the training cost at a minimum. We prove that DepL has constant competitive ratio and polynomial complexity, and show that it outperforms the state-of-the-art by over 27% and closely matches the optimum.

In next-generation networks, cells will be replaced by a collection of points-of-access (PoAs), with overlapping coverage areas and/or different technologies. Along with a promise for greater performance and flexibility, this creates further pressure on network management algorithms, which must make joint decisions on (i) PoA-to-user association and (ii) PoA management. We solve this challenging problem through an efficient and effective solution concept called Cluster-then-Match (CtM). While state-of-the-art approaches tend to focus on performance-related metrics, e.g., network throughput, CtM makes human-centric decisions, where pure network performance is balanced against energy consumption and electromagnetic field exposure. Importantly, such human-centric metrics concern all humans in the network area — including those who are not network users. Through our performance evaluation, which leverages detailed models for EMF exposure estimation and standard-specified signal propagation models, we show that CtM outperforms state-of-the-art network management schemes that solely focus on network performance, including those utilizing machine learning, reducing energy consumption by over 80% in indoor scenarios, and over 36% in outdoor ones.

Next-generation mobile systems will support multiple AI-based applications, each leveraging heterogeneous sensors and data sources through deep neural network (DNN) architectures collaboratively executed within the network. In this context, to minimize the cost of the AI inference task subject to requirements on latency, quality, and – crucially –reliabilityof the inference process, it is vital to optimize (i) the set of sensors/data sources and (ii) the DNN architecture, (iii) the network nodes executing sections of the DNN, and (iv) the resources to use. To achieve these goals, we leverage dynamic gated neural networks with branches, and propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), based upon quantile-Constrained policy optimization. QIC makes joint, high-quality, swift decisions on all the above aspects of the system, with the aim to minimize inference energy cost. We remark that this is the first contribution connecting gated dynamic DNNs with infrastructure-level decision making. We evaluate QIC using a dynamic gated DNN with stems and branches for optimal sensor fusion and inference, trained on the RADIATE dataset offering Radar, LiDAR, and Camera data, and real-world wireless measurements. Our results confirm that QIC closely matches the optimum and outperforms existing approaches in reducing energy consumption (compute, communication, and total) and application requirements failure by over 70%.

An increasing number of mobile applications leverage deep neural networks (DNN) as an essential component to adapt to the operational context at hand and provide users with an enhanced experience. It is thus of paramount importance that network systems support the execution of DNN inference tasks in an efficient and sustainable way. Matching the diverse resources available at the mobile-edge-cloud network tiers with the applications requirements and the complexity of their, while minimizing energy consumption, is however challenging. A possible approach to the problem consists in exploiting the emerging concept of dynamic DNNs, characterized by multi-branched architectures with early exits enabling sample-based adaptation of the model depth. We leverage this concept and address the problem of deploying portions of DNNs with early exits across the mobile-edge-cloud system and allocating therein the necessary network, computing, and memory resources. We do so by developing a 3-stage graph-modeling method that allows us to represent the characteristics of the system and the applications as well as the possible options for splitting the DNN over the multi-tier network nodes. Our solution, called Feasible Inference Graph (FIN), can determine the DNN split, deployment, and resource allocation that minimizes the inference energy consumption while satisfying the nodes’ constraints and the requirements of multiple, co-existing applications. FIN closely matches the optimum and leads to over 89% energy savings with respect to state-of-the-art alternatives.

The increasing pervasiveness of intelligent mobile applications requires to exploit the full range of resources offered by the mobile-edge-cloud network for the execution of inference tasks. However, due to the heterogeneity of such multi-tiered networks, it is essential to make the applications’ demand amenable to the available resources while minimizing energy consumption. Modern dynamic deep neural networks (DNN) achieve this goal by designing multi-branched architectures where early exits enable sample-based adaptation of the model depth. In this paper, we tackle the problem of allocating sections of DNNs with early exits to the nodes of the mobile-edge-cloud system. By envisioning a 3-stage graph-modeling approach, we represent the possible options for splitting the DNN and deploying the DNN blocks on the multi-tiered network, embedding both the system constraints and the application requirements in a convenient and efficient way. Our framework – named Feasible Inference Graph (FIN) – can identify the solution that minimizes the overall inference energy consumption while enabling distributed inference over the multi-tiered network with the target quality and latency. Our results, obtained for DNNs with different levels of complexity, show that FIN matches the optimum and yields over 65% energy savings relative to a state-of-the-art technique for cost minimization.

Decentralized learning scenarios offer the opportunity of a flexible cooperation between learning nodes; in other words, each node may cooperate with an arbitrary subset of its peers. In such scenarios, we tackle the problem of choosing the nodes that cooperate towards the training of a machine learning model, hence, tweaking the cooperation graph connecting the nodes themselves. We propose and evaluate a data-driven approach to the problem, by proposing three metrics to choose the edges to activate in the cooperation graph, and an efficient iterative algorithm exploiting them. Through our performance evaluation, which leverages state-of-the-art datasets and neural network architectures, we find that privacy-preserving metrics accounting for the difference between local datasets are very effective in identifying the best edges to activate to improve the efficiency of model training without hurting performance.

Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirements on latency, quality, and - crucially - reliability of the inference process, it is vital to optimize (i) the set of sensors/data sources and (ii) the DNN architecture, (iii) the network nodes executing sections of the DNN, and (iv) the resources to use. To this end, we leverage dynamic gated neural networks with branches, and propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), based upon quantile-Constrained policy optimization. QIC makes joint, high-quality, swift decisions on all the above aspects of the system, with the aim to minimize inference energy cost. We remark that this is the first contribution connecting gated dynamic DNNs with infrastructure-level decision making. We evaluate QIC using a dynamic gated DNN with stems and branches for optimal sensor fusion and inference, trained on the RADIATE dataset offering Radar, LiDAR, and Camera data, and real-world wireless measurements. Our results confirm that QIC matches the optimum and outperforms its alternatives by over 80%.

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communication security and privacy. In this work, we focus on the latter aspect and introduce a strategy to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users that are facilitated by the presence of IRSs. Specifically, we envision a transmission scheme that cycles across a number of IRS-to-user assignments, and we select them in a near-optimal fashion, thus guaranteeing both a high data rate and a good secrecy rate. Unlike most of the existing works addressing passive eavesdropping, the strategy we envision has low complexity and is suitable for scenarios where nodes are equipped with a limited number of antennas. Through our performance evaluation, we highlight the trade-off between the legitimate users’ data rate and secrecy rate, and how the system parameters affect such a trade-off.

Model compression is a fundamental tool to execute machine learning (ML) tasks on the diverse set of devices populating current-and next-generation networks, thereby exploiting their resources and data. At the same time, how much and when to compress ML models are very complex decisions, as they have to jointly account for such aspects as the model being used, the resources (e.g., computational) and local datasets available at each node, as well as network latencies. In this work, we address the multi-dimensional problem of adapting the model compression, data selection, and node allocation decisions to each other: our objective is to perform the DNN training at the minimum energy cost, subject to learning quality and time constraints. To this end, we propose an algorithmic framework called PACT, combining a time-expanded graph representation of the training process, a dynamic programming solution strategy, and a data-driven approach to the estimation of the loss evolution. We prove that PACT’s complexity is polynomial, and its decisions can get arbitrarily close to the optimum. Through our numerical evaluation, we further show how PACT can consistently outperform state-of-the-art alternatives and closely matches the optimal energy consumption.

Deploying services efficiently while satisfying their quality requirements is a major challenge in network slicing. Effective solutions place instances of the services' virtual network functions (VNFs) at different locations of the cellular infrastructure and manage such instances by scaling them as needed. In this work, we address the above problem and the very relevant aspect of sub-slice reuse among different services. Further, unlike prior art, we account for the services' finite lifetime and time-varying traffic load. We identify two major sources of inefficiency in service management: (i) the overspending of computing resources due to traffic of multiple services with different latency requirements being processed by the same virtual machine (VM), and (ii) the poor packing of traffic processing requests in the same VM, leading to opening more VMs than necessary. To cope with the above issues, we devise an algorithm, called REShare, that can dynamically adapt to the system's operational conditions and find an optimal trade-off between the aforementioned opposite requirements. We prove that REShare has low algorithmic complexity and is asymptotic 2-competitive under a non-decreasing load. Numerical results, leveraging real-world scenarios, show that our solution outperforms alternatives, swiftly adapting to time-varying conditions and reducing service cost by over 25%.

Assisted driving for connected cars is one of the main applications that 5G-and-beyond networks shall support. In this work, we propose an assisted driving system leveraging the synergy between connected vehicles and the edge of the network infrastructure, in order to envision global traffic policies that can effectively drive local decisions. Local decisions concern individual vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when; global decisions, instead, involve whole traffic flows. Such decisions are made at different time scales by different entities, which are integrated within an edge-based architecture and can share information. In particular, we leverage a queuing-based model and formulate an optimization problem to make global decisions on traffic flows. To cope with the problem complexity, we then develop an iterative, linear-time complexity algorithm called Bottleneck Hunting (BH). We show the performance of our solution using a realistic simulation framework, integrating a Python engine with ns-3 and SUMO, and considering two relevant services, namely, lane change assistance and navigation, in a real-world scenario. Results demonstrate that our solution leads to a reduction of the vehicles’ travel times by 66 percent in the case of lane change assistance and by 20 percent for navigation, compared to traditional, local-coordination approaches.

In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available. Such nodes can cooperate to perform a distributed learning task, aided by a learning controller (often located at the network edge). The controller is required to make decisions concerning (i) data selection, i.e., which data sources to use; (ii) model selection, i.e., which machine learning model to adopt, and (iii) matching between the layers of the model and the available physical nodes. All these decisions influence each other, to a significant extent and often in counter-intuitive ways. In this paper, we formulate a problem addressing all of the above aspects and present a solution concept called RightTrain, aiming at making the aforementioned decisions in a joint manner, minimizing energy consumption subject to learning quality and latency constraints. RightTrain leverages an expanded-graph representation of the system and a delay-aware Steiner tree to obtain a provably near-optimal solution while keeping the time complexity low. Specifically, it runs in polynomial time and its decisions exhibit a competitive ratio of$2(1+\epsilon)$, outperforming state-of-the-art solutions by over 50%. Our approach is also validated through a real-world implementation.

The traditional approach to distributed machine learning is to adapt learning algorithms to the network, e.g., reducing updates to curb overhead. Networks based on intelligent edge, instead, make it possible to follow the opposite approach, i.e., to define the logical network topology around the learning task to perform, so as to meet the desired learning performance. In this paper, we propose a system model that captures such aspects in the context of supervised machine learning, accounting for both learning nodes (that perform computations) and information nodes (that provide data). We then formulate the problem of selecting (i) which learning and information nodes should cooperate to complete the learning task, and (ii) the number of epochs to run, in order to minimize the learning cost while meeting the target prediction error and execution time. After proving important properties of the above problem, we devise an algorithm, named DoubleClimb, that can find a$1+1/| \mathcal {I}|$-competitive solution (with$\mathcal {I}$being the set of information nodes), with cubic worst-case complexity. Our performance evaluation, leveraging a real-world network topology and considering both classification and regression tasks, also shows that DoubleClimb closely matches the optimum, outperforming state-of-the-art alternatives.

In cell-free wireless networks, multiple connectivity options and technologies are available to serve each user. Traditionally, such options are ranked and selected solely based on the network performance they yield; however, additional information such as electromagnetic field (EMF) exposure could be considered. In this work, we explore the trade-offs between network performance and EMF exposure in a typical indoor scenario, finding that it is possible to significantly reduce the latter with a minor impact on the former. We further find that surrogate models represent an efficient and effective tool to model the network behavior.

Thanks to network slicing, mobile networks can now support multiple and diverse services, each requiring different key performance indicators (KPIs). In this new scenario, it is critical to allocate network and computing resources efficiently and in such a way that all KPIs targeted by a service are met. Accounting for all sorts of KPIs (e.g., availability and reliability, besides the more traditional throughput and latency) is an aspect that has been scarcely addressed so far and that requires tailored models and solution strategies. We address this issue by proposing a novel methodology and resource orchestration scheme, named OKpi, which provides high-quality decisions on VNF (Virtual Network Function) placement and data routing, including the selection of radio points of attachment. Importantly, OKpi has polynomial computational complexity and accounts forallKPIs required by each service, and for any resource available from the fog to the cloud. We prove several properties of OKpi and demonstrate that it performs very closely to the optimum under real-world scenarios. We also implement OKpi in a testbed supporting a robot-based, smart factory service, and we present some field tests that further confirm the ability of OKpi to make high-quality decisions.

Millimeter-wave communication is widely seen as a promising option to increase the capacity of vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Due to harsh propagation conditions, mmWave systems resort to narrow beams to serve their users, and such beams need to be configured according to traffic demand and its spatial distribution, as well as interference. In this work, we address the beam management problem, considering an urban vehicular network composed of gNBs. We first build an accurate, yet tractable, system model and formulate an optimization problem aiming at maximizing the total network data rate while accounting for the stochastic nature of the network scenario. Then we develop a graph-based model capturing the main system characteristics and use it to develop a belief propagation algorithmic framework, called CRAB, that has low complexity and, hence, can effectively cope with large-scale scenarios. We assess the performance of our approach under real-world settings and show that, in comparison to state-of-the-art alternatives, CRAB provides on average a 50% improvement in the amount of data transferred by the single gNBs and up to 30% better user coverage.

We consider the optimization of a smart radio environment where meta-surfaces are employed to improve the performance of multiuser wireless networks working at sub-THz frequencies. Motivated by the extreme sparsity of the THz channel we propose to model each meta-surface as an electronically steerable reflector, by using only two parameters, regardless of its size. This assumption, although suboptimal in a general multiuser setup, allows for a significant complexity reduction when optimizing the environment and, despite its simplicity, is able to provide high communication rates. We derive a set of asymptotic results providing insight on the system behavior when both the number of antennas at the transmitter and the meta-surfaces area grow large. For the optimization we propose an algorithm based on the Newton-Raphson method and a simpler, yet effective, heuristic approach based on a map associating meta-surfaces and users. Through numerical results we provide insights on the system behavior and we assess the performance limits of the network in terms of supported users and spatial density of the meta-surfaces.

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communications security and privacy. In this work, we focus on this aspect and envision a strategy to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users. Unlike most of the existing works addressing passive eavesdropping, the strategy we consider has low complexity and is suitable for scenarios where nodes are equipped with a limited number of antennas. Through our performance evaluation, we highlight the trade-off between the legitimate users’ data rate and secrecy rate, and how the system parameters affect such a trade-off.

One of the main use cases for advanced cellular networks is represented by massive Internet-of-things (MIoT), i.e., an enormous number of IoT devices that transmit data toward the cellular network infrastructure. To make cellular MIoT a reality, data transfer and control procedures specifically designed for the support of IoT are needed. For this reason, 3GPP has introduced the Control Plane Cellular IoT optimization, which foresees a simplified bearer instantiation, with the Mobility Management Entity (MME) handling both control and data traffic. The performance of the MME has therefore become critical, and properly scaling its computational capability can determine the ability of the whole network to tackle MIoT effectively. In particular, considering virtualized networks and the need for an efficient allocation of computing resources, it is paramount to characterize the MME performance as the MIoT traffic load changes. We address this need by presenting compact, closed-form expressions linking the number of IoT sources with the rate at which bearers are requested, and such a rate with the delay incurred by the IoT data. We show that our analysis, supported by testbed experiments and verified through large-scale simulations, represents a valuable tool to make effective scaling decisions in virtualized cellular core networks.

Automotive services for connected vehicles are one of the main fields of application for new-generation mobile networks as well as for the edge computing paradigm. In this paper, we investigate a system architecture that integrates the distributed vehicular network with the network edge, with the aim to optimize the vehicle travel times. We then present a queue-based system model that permits the optimization of the vehicle flows, and we show its applicability to two relevant services, namely, lane change/merge (representative of cooperative assisted driving) and navigation. Furthermore, we introduce an efficient algorithm called Bottleneck Hunting (BH), able to formulate high-quality flow policies in linear time. We assess the performance of the proposed system architecture and of BH through a comprehensive and realistic simulation framework, combining ns-3 and SUMO. The results, derived under real-world scenarios, show that our solution provides much shorter travel times than when decisions are made by individual vehicles.

Networks can now process data as well as transporting it; it follows that they can support multiple services, each requiring different key performance indicators (KPIs). Because of the former, it is critical to efficiently allocate network and computing resources to provide the required services, and, because of the latter, such decisions must jointly consider all KPIs targeted by a service. Accounting for newly introduced KPIs (e.g., availability and reliability) requires tailored models and solution strategies, and has been conspicuously neglected by existing works, which are instead built around traditional metrics like throughput and latency. We fill this gap by presenting a novel methodology and resource allocation scheme, named OKpi, which enables high-quality selection of radio points of access as well as VNF (Virtual Network Function) placement and data routing, with polynomial computational complexity. OKpi accounts for all relevant KPIs required by each service, and for any available resource from the fog to the cloud. We prove several important properties of OKpi and evaluate its performance in two real-world scenarios, finding it to closely match the optimum.

Mmwave bands are being widely touted as a very promising option for future 5G networks, especially in enabling such networks to meet highly demanding rate requirements. Accordingly, the usage of these bands is also receiving an increasing interest in the context of 5G vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Mmwave communications, however, require the link to be established using narrow directed beams, to overcome harsh propagation conditions. The advanced antenna systems enabling this also allow for a complex beam design at the base station, where multiple beams of different widths can be set up. In this work, we focus on beam management in an urban vehicular network, using a graph-based approach to model the system characteristics and the existing constraints. In particular, unlike previous work, we formulate the beam design problem as a maximum-weight matching problem on a bipartite graph with conflicts, and then we solve it using an efficient heuristic algorithm. Our results show that our approach easily outperforms advanced methods based on clustering algorithms.

All the content consumed by mobile users, be it a web page or a live stream, undergoes some processing along the way; as an example, web pages and videos are transcoded to fit each device's screen. The recent multi-access edge computing (MEC) paradigm envisions performing such processing within the cellular network, as opposed to resorting to a cloud server on the Internet. Designing a MEC network, i.e., placing and dimensioning the computational facilities therein, requires information on how much computational power is required to produce the contents needed by the users. However, real-world demand traces only contain information on how much data is downloaded. In this paper, we demonstrate how to enrich demand traces with information about the computational power needed to process the different types of content, and we show the substantial benefit that can be obtained from using such enriched traces for the design of MEC-based networks.

Massive Internet-of-things (MIoT) represents one of the main use cases of 5G, as well as one of the most challenging ones. Accordingly, MIoT traffic is given special treatment in the core network, with the Mobility Management Entity (MME) serving both control and data traffic. In this context, it is critical to properly dimension the MME and to adapt its capability to traffic fluctuations. To this end, we present a compact, closed form linking the number of IoT sources with the rate at which bearers are requested from the MME, and such a rate delay incurred by MME operations. Our model represents a valuable tool to make effective, realtime scaling decisions in virtualized cellular core networks.

Vehicle-to-infrastructure millimeter-wave (mmWave)communication represents a potential solution to capacity shortage in mobile networks. However, effective beam alignment between senders and receivers requires knowledge of the position of vehicles, which is often impractical to obtain in real time. We propose to solve this problem by leveraging the traffic signals, e.g., semaphores, that regulate the vehicular mobility. As an example, we may coordinate beams with red semaphore lights, as they correspond to higher vehicle densities and lower speeds. In order to evaluate such intuition, we propose a mmWave communication model accounting for both the distance and the speed of vehicles being served, and use such a model to compare several beam design strategies. For increased realism, we consider as our reference scenario a large-scale, real-world vehicular trace depicting the mobility in Luxembourg. Our results show that our approach outperforms static beam design based on road topology alone, and, remarkably, it yields a performance comparable to that of solutions based on real-time mobility information.

Through their computational and forwarding capabilities, 5G networks can support multiple vertical services. Such services may include several common virtual (network)functions (VNFs), which could be shared to increase resource efficiency. In this paper, we focus on the seldom studied VNF-sharing problem, and decide (i)whether sharing a VNF instance is possible/beneficial or not, (ii)how to scale virtual machines hosting the VNFs to share, and (iii)the priorities of the different services sharing the same VNF. These decisions are made with the aim to minimize the mobile operator's costs while meeting the verticals' performance requirements. Importantly, we show that the aforementioned priorities should not be determined a priori on a per-service basis, rather they should change across VNFs since such additional flexibility allows for more efficient solutions. We then present an effective methodology called FlexShare, enabling near-optimal VNF-sharing decisions in polynomial time. Our performance evaluation, using real-world VNF graphs, confirms the effectiveness of our approach, which consistently outperforms baseline solutions using per-service priorities.

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, efficiently. To this end, we formulate a queuing-based model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out an accounting for multiple scenarios representing the real-world services, shows that MaxZ performs substantially better than the state-of-the-art alternatives and consistently close to the optimum.

5G network nodes, fronthaul and backhaul alike, will have both forwarding and computational capabilities. This makes energy-efficient network management more challenging, as decisions, such as activating or deactivating a node, impact on both the ability of the network to route traffic and the amount of processing it can perform. To this end, we formulate an optimization problem accounting for the main features of 5G nodes and the traffic they serve, allowing joint decisions about: 1) the nodes to activate; 2) the network functions they run; and 3) the traffic routing. Our optimization module is integrated within the management and orchestration framework of 5G, thus enabling swift and high-quality decisions. We test our scheme with both a real-world testbed based on OpenStack and OpenDaylight, and a large-scale emulated network whose topology and traffic come from a real-world mobile operator, finding it to consistently outperform state-of-the art alternatives and closely match the optimum.

Thanks to its computational and forwarding capabilities, the mobile network infrastructure can support several third-party (“vertical”) services, each composed of a graph of virtual (network) functions (VNFs). Importantly, one or more VNFs are often common to multiple services, thus the services deployment cost could be reduced by letting the services share the same VNF instance instead of devoting a separate instance to each service. By doing that, however, it is critical that the target KPI (key performance indicators) of all services are met. To this end, we study theVNF sharingproblem and make decisions on 1) when sharing VNFs among multiple services is possible, 2) how to adapt the virtual machines running the shared VNFs to the combined load of the assigned services, and 3) how to prioritize the services traffic within shared VNFs. All decisions aim to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay. Notably, we show that the aforementioned priorities should be managed dynamically and vary across VNFs. We then propose the FlexShare algorithm to provide near-optimal VNF-sharing and priority assignment decisions in polynomial time. We prove that FlexShare is within a constant factor from the optimum and, using real-world VNF graphs, we show that it consistently outperforms baseline solutions.

One of the most disruptive innovations in next- generation cellular networks will be the massive support of Machine Type and IoT (MTC/IoT) communications. This type of communications exhibits very different requirements from traditional cellular traffic: in MTC/IoT, the same base station may need to provide service to thousands of nodes, each of them transmitting small and infrequent data. In this context, it is critical to evaluate the impact of MTC/IoT on the Evolved Packet Core (EPC) network. We do so by quantifying analytically the signaling load on the EPC due to MTC/IoT bearer instantiation in both standard and 3GPP IoT-optimized LTE networks. Our analysis, validated via simulation, provides useful insights on the impact of the traffic load on each component of the EPC, as well as on the system design.

Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding near-optimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum.

Next-generation mobile networks will be heavily based on virtualization and their pervasiveness raises many questions regarding the energy efficiency of an architecture that requires distributed computing resources at the network edge. In this paper, we focus on the two main virtualization approaches, i.e., virtual machines and containers, which play a primary role in the provisioning of MEC-based services for mobile users. Specifically, we compare the two approaches from the viewpoint of the power consumption they are associated with - a metric significantly affecting the network provider's costs as well as the ICT environment footprint. Through a set of realworld experiments, using real-world video streaming and gaming applications, we assess not only the magnitude of the power consumption we incur, but also how it evolves as the workload increases. Our results show that containers are both more powerefficient and more scalable than virtual machines.

The future 5G transport networks are envisioned to support a variety of vertical services through network slicing and efficient orchestration over multiple administrative domains. In this paper, we propose an orchestrator architecture to support vertical services to meet their diverse resource and service requirements. We then present a system model for resource orchestration of transport networks as well as low-complexity algorithms that aim at minimizing service deployment cost and/or service latency. Importantly, the proposed model can work with any level of abstractions exposed by the underlying network or the federated domains depending on their representation of resources.

We consider the problem of energy-efficient network management in 5G systems, where backhaul and fronthaul nodes have both networking and computational capabilities. We devise an optimization model accounting for the main features of 5G backhaul and fronthaul, and jointly solve the problems of (i) node switch on/off, (ii) VNF placement, and (iii) traffic routing. We implement an optimization module within an application on top of an SDN controller and NFV orchestrator, thus enabling swift, high-quality decisions based on current network conditions. Finally, we validate and test our scheme with real-world power consumption, network topology and traffic demand, assessing its performance as well as the relative importance of the main contributions to the total power consumption of the system.

One of the key applications envisioned for C-V2I (Cellular Vehicle-to-Infrastructure) networks pertains to safety on the road. Thanks to the exchange of Cooperative Awareness Messages (CAMs), vehicles and other road users (e.g., pedestrians) can advertise their position, heading and speed and sophisticated algorithms can detect potentially dangerous situations leading to a crash. In this paper, we focus on the safety application for automotive collision avoidance at intersections, and study the effectiveness of its deployment in a C-V2I-based infrastructure. In our study, we also account for the location of the server running the application as a factor in the system design. Our simulation-based results, derived in real-world scenarios, provide indication on the reliability of algorithms for car-to-car and car-to-pedestrian collision avoidance, both when a human driver is considered and when automated vehicles (with faster reaction times) populate the streets.

In a world of open data and large-scale measurements, it is often feasible to obtain a real-world trace to fit to one's research problem. Feasible, however, does not imply simple. Taking next-generation cellular network planning as a case study, in this paper we describe a large-scale dataset, combining topology, traffic demand from call detail records, and demographic information throughout a whole country. We investigate how these aspects interact, revealing effects that are normally not captured by smaller-scale or synthetic datasets. In addition to making the resulting dataset available for download, we discuss how our experience can be generalized to other scenarios and case studies, i.e., how everyone can construct a similar dataset from publicly available information.

Vehicular users are emerging as a prime market for targeted advertisement, where advertisements (ads) are sent from network points of access to vehicles, and displayed to passengers only if they are relevant to them. In this study, we take the viewpoint of a broker managing the advertisement system, and getting paid every time a relevant ad is displayed to an interested user. The broker selects the ads to broadcast at each point of access so as to maximize its revenue. In this context, we observe that choosing the ads that best fit the users' interest could actually hurt the broker's revenue. In light of this conflict, we present Volfied, an algorithm allowing for conflict-free, near-optimal ad selection with very low computational complexity. Our performance evaluation, carried out through real-world vehicular traces, shows that Volfied increases the broker revenue by up to 70 percent with provably low computational complexity, compared to state-of-the-art alternatives.

Deployment and demand traces are a crucial tool to study today's LTE systems, as well as their evolution toward 5G. In this paper, we use a set of real-world, crowdsourced traces, coming from the WeFi and OpenSignal apps, to investigate how present-day networks are deployed, and the load they serve. Given this information, we present a way to generate synthetic deployment and demand profiles, retaining the same features of their real-world counterparts. We further discuss a methodology using traces (both real-world and synthetic) to assess (i) to which extent the current deployment is adequate to the current and future demand, and (ii) the effectiveness of the existing strategies to improve network capacity. Applying our methodology to real-world traces, we find that present-day LTE deployments consist of multiple, entangled, medium- to large-sized cells. Furthermore, although today's LTE networks are overprovisioned when compared to the present traffic demand, they will need substantial capacity improvements in order to face the load increase forecasted between now and 2020.

Containerization, often referred to as lightweight virtualization, is one of the key building blocks of next-generation networks. In this paper we consider Docker, the de facto standard containerization solution, and seek to measure the power consumption it is associated with. We perform our tests with real, off-the-shelf hardware and using several heterogeneous types of load. We find that, while CPU usage represents the main contribution to the overall consumption, other aspects need to be accounted for, both within and out of Docker.

We target the evaluation of a Superfluid 5G network from an economic point of view. The considered 5G architecture has notably features, such as flexibility, agility, portability and high performance, as shown by the H2020 SUPERFLUIDITY project. The proposed economic model, tailored to the Superfluid network architecture, allows to compute the CAPEX, the OPEX, the Net Present Value (NPV) and the Internal Rate of Return (IRR). Specifically, we apply our model to estimate the impact for the operator of migrating from a legacy 4G to a 5G network. Our preliminary results, obtained over two realistic case studies located in Bologna (Italy) and San Francisco (CA), show that the monthly subscription fee for the subscribers can be kept sufficiently low, i.e., typically around 5 [USD] per user, while allowing a profit for the operator.

We focus on today's LTE systems and use real-world, crowdsourced traces to understand (i) how present-day LTE networks are deployed and to which extent they are suited to the current traffic load; (ii) how well they will withstand the traffic demand forecasted within 2020; (iii) which techniques to improve them should be pursued and how aggressively. To this end, we use two datasets, coming from WeFi and OpenSignal and available under commercial terms. We find that today's networks are composed of tangled, medium to large-sized cells, characterized by fairly high interference. Also, current networks are typically overprovisioned, but the future traffic load will pose a significant strain on them. To accommodate the forecasted mobile traffic, our study highlights the efficacy of: (i) traffic offloading for pedestrian and stationary users, (ii) increasing the available bandwidth through, e.g., spectrum refarming, (iii) mitigating interference and improving link quality for edge users through coordinated downlink transmissions. By putting in place these actions, only a negligible amount of additional cellular infrastructure will be required. Our results come from the combination of real-world traces, experimental measurements, and ITU-recommended propagation models. Each step we take is backed by real-world facts and data.

Service providers are expected to play an increasingly central role in the mobile market and their relationship with the traditional mobile network operators (MNOs) is starting to change. The dilemma faced by over-the-top service-providers (OTTs) is now whether to enter into a service level agreement with the MNOs (in the same spirit of mobile virtual network operator agreements) or to invest in deploying their own network infrastructure to serve their demand. The purpose of this paper is to study the factors shaping the agreements between OTTs and MNOs and how these factors impact network planning decisions. To this end, we build a synthetic model of cellular network deployment that explores how traditional mobile operators and OTTs compete in deploying new infrastructure. Using our model in conjunction with real-world data, we find that service-driven networks are heavily influenced by regulatory decisions, and that cost structures and demand characteristics play non-marginal roles in the definition of service-driven networks.

The success of smartphones has encouraged over-the-top service providers to seek ways in which they can have more control over the wireless service offered to their users. Google Project Fi is an example for this type of action, where control over the wireless service is achieved by either deploying own wireless infrastructure or entering into service level agreements with mobile network operators. Following this example, we construct a game theoretic model for the interaction between mobile network operators and over-the-top service providers and assess how the spatial distribution of mobile demand impacts the outcomes of cooperation, unevenly affecting the utilities achieved by the two parties. We also show how the cost of fixed infrastructure deployed by the mobile operator, if too high, may render cooperation between the two parties ineffective.

This paper focuses on targeted advertising for vehicular users, where users receive advertisements (ads) from roadside units and the vehicle onboard system displays only ads that are relevant to the user. A broker broadcasts ads and is paid by advertisers based on the number of vehicles that displayed each ad. The problem we study is the following: given that the broker can broadcast a limited number of ads, what is the strategy for ad selection that maximizes the broker's revenue? We first identify the conflict existing between users' interests and broker's revenue as a critical feature of this scenario, which may dramatically reduce the broker's revenue. Then, given the problem complexity, we propose Volfied, an algorithm that solves this conflict, allows for near-optimal broker's revenue and has very limited computational complexity. Our results show that Volfied increases the broker's revenue by up to 70% with respect to state-of-the-art alternatives.

Online social networks (OSNs) serve today as a platform for information dissemination. At the same time, mobile devices provide ubiquitous network access through the cellular infrastructure. In this paper, we develop mechanisms for minimizing the peak load of the cellular network due to information cascades spreading on social media. First, we exploit the social ties for predicting information dissemination and we propose Proactive Seeding-a technique for minimizing the peak load of cellular networks. Much of such a load is due to information cascades spreading in social media, and we address it by proactively pushing (“seeding”) content to selected users before they actually request it. We develop a family of algorithms that take as input information primarily about: (i) cascades on the OSN, (ii) the background traffic load in the cellular network, and (iii) the local connectivity among mobiles; the algorithms then select which nodes to seed and when. We prove that Proactive Seeding is optimal when the prediction of information cascades is perfect. We perform simulations driven by traces from Twitter and cellular networks and we find that Proactive Seeding reduces the peak cellular load by 20-50 percent. Then, we exploit the fact that there is correlation between social ties and physical proximity and we combine Proactive Seeding with device-to-device communication to further reduce the peak load.

An effective way to provide popular content in LTE networks is through broadcast and multicast services (a.k.a. eMBMS). This requires to aggregate cells into areas where transmissions are synchronized in time so that each area broadcasts the same set of content items, on the same radio resources. We look at an aspect of LTE broadcasting that has been scarcely addressed so far: how to form broadcasting areas and assign content to them so that radio resources are efficiently exploited and user requests satisfied. Due to its high complexity, we solve the problem through an original clustering heuristics, named Single-Content Fusion (SCF), that initially aggregates cells into single-content areas by maximizing cell similarity in content interests. Such areas are then merged into multiple-content areas leveraging similarity in spatial coverage. The validity of our solution is shown by the excellent match with the optimum in a toy scenario and by the remarkable advantages SCF provides in large-scale, real-world scenarios, in comparison to other heuristic approaches.

The role of vehicles has been rapidly expanding to become a different kind of utility, no longer just vehicles but nodes of the future Internet. The car producers and the research community are investing considerable time and resources in the design of new protocols and applications that meet customer demand, or that foster new forms of interaction between the moving customers and the rest of the world. Among the variety of new applications and business models, the spreading of advertisements is expected to play a crucial role. Indeed, advertising is already a significant source of revenue and it is currently used over many communication channels, such as the Internet and television. In this paper, we address the targeting of advertisements in vehicular networks, where advertisements are broadcasted by Access Points and then displayed to interested users. In particular, we describe the advertisement dissemination process by means of an optimization model aiming at maximizing the number of advertisements that are displayed to users within the advertisement target area and target time period. We then solve the optimization problem on an urban area, using realistic vehicular traffic traces. Our results highlight the importance of predicting vehicles mobility and the impact of the user interest distribution on the revenue that can be obtained from the advertisement service.

Future cellular networks will be owned by multiple parties, e.g., two mobile operators, each of which controls some elements of the access and backhaul infrastructure. In this context, it is important that as much traffic as possible is processed by the same party that generates it, i.e., that the coupling between traffic and network ownership is maximized. Software-defined backhaul networks can attain this goal; however, existing management schemes ignore ownership altogether. We fill this gap by presenting an ownership- aware network management scheme, maximizing the amount of traffic that is processed by the same party it belongs to.

We consider a network of electric vehicles (EVs) and its components: vehicles, charging stations, and coalitions of stations. For such a setting, we propose a model in which individual stations, coalitions of stations, and vehicles interact in a market revolving around the energy for battery recharge. We start by separately studying 1) how autonomously operated charging stations form coalitions; 2) the price policy enacted by such coalitions; and 3) how vehicles select the charging station to use, working toward a time/price tradeoff. Our main goal is to investigate how equilibrium in such a market can be reached. We also address the issue of computational complexity, showing that, through our model, equilibria can be found in polynomial time. We evaluate our model in a realistic scenario, focusing on its ability to capture the advantages of the availability of an intelligent transportation system supporting the EV drivers. The model also mimics the anticompetitive behavior that charging stations are likely to follow, and it highlights the effect of possible countermeasures to such a behavior.

We address the resource allocation problem in an LTE-based 2-tier heterogeneous network where in-band D2D communications are supported under network control. The different communication paradigms share the same radio resources, thus they may interfere. We devise a dynamic programming approach to efficiently schedule download and upload traffic, by 1) efficiently matching communicating endpoints and 2) assigning radio resources in an interference-aware manner while accounting for the characteristics of the content to be delivered. To this end, we develop an accurate model of the system and apply approximate dynamic programming to solve it. Our solution allows us to deal with realistic large-scale scenarios. In such scenarios, we compare our approach to today's networks where eICIC techniques and proportional fairness scheduling are implemented. Results highlight that our solution increases the system throughput while greatly reducing energy consumption. We also show that D2D mode, established either in the downlink or uplink, can effectively support delivery of highly popular content without significantly harming macrocell or microcell traffic, leading to increased system capacity. Interestingly, we find that D2D mode can also be a low-cost alternative to microcells.

Resource allocation in LTE networks is known to be an NP-hard problem. In this paper, we address an even more complex scenario: an LTE-based, 2-tier heterogeneous network where D2D mode is supported under the network control. All communications (macrocell, microcell and D2D-based) share the same frequency bands, hence they may interfere. We then determine (i) the network node that should serve each user and (ii) the radio resources to be scheduled for such communication. To this end, we develop an accurate model of the system and apply approximate dynamic programming to solve it. Our algorithms allow us to deal with realistic, large-scale scenarios. In such scenarios, we compare our approach to today's networks where eICIC techniques and proportional fairness scheduling are implemented. Results highlight that our solution increases the system throughput while greatly reducing energy consumption. We also show that D2D mode can effectively support content delivery without significantly harming macrocells or microcells traffic, leading to an increased system capacity. Interestingly, we find that D2D mode can be a low-cost alternative to microcells.

Broadcasting capabilities are one of the most promising features of upcoming LTE-Advanced networks. However, the task of scheduling broadcasting sessions is far from trivial, since it affects the available resources of several contiguous cells as well as the amount of resources that can be devoted to unicast traffic. In this paper, we present a compact, convenient model for broadcasting in LTE, as well as a set of efficient algorithms to define broadcasting areas and to actually perform content scheduling. We study the performance of our algorithms in a realistic scenario, deriving interesting insights on the possible trade-offs between effectiveness and computational efficiency.

A number of vehicular networking applications require continuous knowledge of the location of vehicles and tracking of the routes they follow, including, e.g., real-time traffic monitoring, e-tolling, and liability attribution in case of accidents. Locating and tracking vehicles has however strong implications in terms of security and user privacy. On the one hand, there should be a mean for an authority to verify the correctness of positioning information announced by a vehicle, so as to identify potentially misbehaving cars. On the other, public disclosure of identity and position of drivers should be avoided, so as not to jeopardize user privacy. In this paper, we address such issues by introducing A-VIP, a secure, privacy-preserving framework for continuous tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows a location authority to verify the positions announced by vehicles, or to infer the actual ones if needed, without resorting to computationally expensive asymmetric cryptography. We assess the effectiveness of A-VIP via realistic simulation and experimental testbeds.

Bandwidth availability in the cellular backhaul is challenged by ever-increasing demand by mobile users. Vehicular users, in particular, are likely to retrieve large quantities of data, choking the cellular infrastructure along major thoroughfares and in urban areas. It is envisioned that alternative roadside network connectivity can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of vehicular networks in this task, considering that roadside units can exploit mobility prediction to decide which data they should fetch from the Internet and to schedule transmissions to vehicles. Rather than adopting a specific prediction scheme, we propose a fog-of-war model that allows us to express and account for different degrees of prediction accuracy in a simple, yet effective, manner. We show that our fog-of-war model can closely reproduce the prediction accuracy of Markovian techniques. We then provide a probabilistic graph-based representation of the system that includes the prediction information and lets us optimize content prefetching and transmission scheduling. Analytical and simulation results show that our approach to content downloading through vehicular networks can achieve a 70% offload of the cellular network.

Knowledge of the location of vehicles and tracking of the routes they follow are a requirement for a number of applications. However, public disclosure of the identity and position of drivers jeopardizes user privacy, and securing the tracking through asymmetric cryptography may have an exceedingly high computational cost. In this paper, we address all of the issues above by introducing A-VIP, a lightweight privacy-preserving framework for tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows an authority to verify the locations announced by vehicles, or to infer the actual ones if needed. We assess the effectiveness of A-VIP through testbed implementation results.

We envision a next-generation cellular network, where base stations allow Internet connectivity through different wireless interfaces (e.g., LTE and WiFi), and licensed cellular frequencies can be used also for device-to-device communications. With this scenario in mind, we develop a model that synthetically and consistently describes the diverse communications opportunities offered by the above network system. Then, we propose a fix-and-relax approach that makes the model solvable in real time. As one of its possible applications, our numerical results show how the model can be effectively used to design and analyze policies for dynamic frequency allocation.

Floating Car Data (FCD) is currently collected by moving vehicles and uploaded to Internet-based processing centers through the cellular access infrastructure. As FCD is foreseen to rapidly become a pervasive technology, the present network paradigm risks not to scale well in the future, when a vast majority of automobiles will be constantly sensing their operation as well as the external environment and transmitting such information towards the Internet. In order to relieve the cellular network from the additional load that widespread FCD can induce, we study a local gathering and fusion paradigm, based on vehicle-to-vehicle (V2V) communication. We show how this approach can lead to significant gain, especially when and where the cellular network is stressed the most. Moreover, we propose several distributed schemes to FCD offloading based on the principle above that, despite their simplicity, are extremely efficient and can reduce the FCD capacity demand at the access network by up to 95%.

We consider a system where users aboard communication-enabled vehicles are interested in downloading different contents from Internet-based servers. This scenario captures many of the infotainment services that vehicular communication is envisioned to enable, including news reporting, navigation maps, and software updating, or multimedia file downloading. In this paper, we outline the performance limits of such a vehicular content downloading system by modeling the downloading process as an optimization problem, and maximizing the overall system throughput. Our approach allows us to investigate the impact of different factors, such as the roadside infrastructure deployment, the vehicle-to-vehicle relaying, and the penetration rate of the communication technology, even in presence of large instances of the problem. Results highlight the existence of two operational regimes at different penetration rates and the importance of an efficient, yet 2-hop constrained, vehicle-to-vehicle relaying.

ABSTRACT We address the problem of content discovery and provision in vehicular networks with infrastructure, when a publish/subscribe paradigm is applied. In the scenario we consider, vehicular users can be providers and consumers of generic information content, which is available through the vehicular network itself. Special infrastructure nodes act as information brokers and aid vehicles in content retrieval and dissemination. We study the performance of such a scheme by evaluating the probability that a content requested by a vehicle can be found and successfully delivered to the querying vehicle. To ensure high success probability, we design a credit‐based scheme integrated with a feedback‐based mechanism. The combined use of credit and feedback entices rational users to provide their content when requested by other users (thus discouraging free‐riding behavior), and guarantees that no user is unduly burdened by too many requests for the same content (thus guaranteeing users a fair treatment). Also, through a banning mechanism that temporarily inhibits service to misbehaving users, we effectively counter malicious users whose sole objective is to disrupt the system. Using a simple game‐theoretic formulation, we prove that these mechanisms ensure that cooperation is the best choice for rational users. The performance of our scheme for discovery and provision of content is shown through ns‐3 simulations, by using a real‐world road topology and realistic vehicular traces. Copyright © 2012 John Wiley & Sons, Ltd.

Online social networks (OSNs) play an increasingly important role today in informing users about content. At the same time, mobile devices provide ubiquitous access to this content through the cellular infrastructure. In this paper, we exploit the fact that the interest in content spreads over OSNs, which makes it, to a certain extent, predictable. We propose Proactive Seeding-a technique for minimizing the peak load of cellular networks, by proactively pushing (“seeding”) content to selected users before they actually request it. We develop a family of algorithms that take as input information primarily about (i) cascades on the OSN and possibly about (ii) the background traffic load in the cellular network and (iii) the local connectivity among mobiles; the algorithms then select which nodes to seed and when. We prove that Proactive Seeding is optimal when the prediction of information cascades is perfect. In realistic simulations, driven by traces from Twitter and cellular networks, we find that Proactive Seeding reduces the peak cellular load by 20%-50%. Finally, we combine Proactive Seeding with techniques that exploit local mobile-to-mobile connections to further reduce the peak load.

The proliferation of electric vehicles is envisaged to become a reality, as they represent one of the major solutions to fossil fuel shortage and polluting emissions. One aspect of primary importance is the point of view of drivers, who aim at minimizing their trip time, hence the overall time that battery recharging may take. New generation of vehicles will be always connected, either using cellular or vehicular communications and they will be part of a widespread Intelligent Transportation System (ITS). Thus, dissemination of information on charging stations (with time-related parameters), or about which charging station to use, will significantly alleviate drivers' concern on the time needed to reach a destination. In this work, we investigate the scenario outlined above, by taking a game-theoretic approach. We account for the travel time towards charging stations, the waiting time there and the time for battery recharge, and analyze the role of ITS, as well as of the information it can distribute, in reducing the trip time of electric vehicles. Our study highlights the importance of a Central Controller that can not only inform drivers on the current scenario, but also give specific advice on the charging station to use. Interestingly, we show that drivers, being rational, will conform to such advice even if suboptimal.

Content access and downloading in vehicular environments is expected to heavily rely on the availability of roadside infrastructure. Although vehicle-to-vehicle communication is foreseen, data will mostly flow through roadside access points, or RSUs (RoadSide Units), which suffer from less connectivity problems. However, at least in the early stages of deployment, the RSU coverage will be spotty, or limited to main avenues in urban areas. In this paper, we try to address such shortcomings by investigating the possibility of exploiting parked vehicles to extend the RSU service coverage. Our approach leverages optimization models aiming at maximizing both the freshness of the content that downloaders retrieve and the efficiency in the utilization of radio resources. Performance evaluation highlights that the use of parked vehicles enhances the benefits of the content downloading process and leads to a significant offload of the RSUs, with respect to the case where only mobile relays are used.

Content downloading by mobile users is expected to significantly increase the cellular network load. Vehicular users, in particular, are likely to engage in information retrieval on the move: in this context, Intelligent Transportation Systems (ITS) can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of ITS in this task, considering that roadside units (RSUs) can exploit mobility prediction to decide which data they should fetch from the Internet and schedule transmissions to vehicles, either potential relays or downloaders. Rather than presenting a specific prediction scheme, we propose a model that allows us to express and account for any prediction technique in a simple, yet effective, manner. We then provide a probabilistic graph-based representation of the system that accounts for the prediction uncertainty. We use such a representation to study the network dynamics by efficiently solving a (non-integer) LP problem. Our results show that the above approach to content downloading through ITS can achieve an 80% offload of the cellular network. Also, we investigate the dependency of the system performance on the accuracy of the mobility prediction, and which prediction errors have the largest impact.

We address content discovery in wireless networks with infrastructure, where mobile nodes store, advertise, and consume content while Broker entities running on infrastructure devices let demand and offer meet. We refer to this paradigm as match-making, highlighting its features within the confines of the standard publish-and-subscribe paradigm. We study its performance in terms of success probability of a content query, a parameter that we strive to increase by acting as follows: 1) We design a credit-based scheme that makes it convenient for rational users to provide their content (thus discouraging free-riding behavior), and it guarantees them a fair treatment. 2) We increase the availability of either popular or rare content, through an efficient caching scheme. 3) We counter malicious nodes whose objective is to disrupt the system performance by not providing the content they advertise. To counter the latter as well as free riders, we introduce a feedback mechanism that enables a Broker to tell apart well- and misbehaving nodes in a very reliable manner, and to ban the latter. The properties of our match-making scheme are analyzed through game theory. Furthermore, via ns-3 simulations, we show its resilience to different attacks by malicious users and its good performance with respect to other existing solutions.

Content downloading in vehicular networks is a topic of increasing interest: services based upon it are expected to be hugely popular and investments are planned for wireless roadside infrastructure to support it. We focus on a content downloading system leveraging both infrastructure-to-vehicle and vehicle-to-vehicle communication. With the goal to maximize the system throughput, we formulate a max-flow problem that accounts for several practical aspects, including channel contention and the data transfer paradigm. Through our study, we identify the factors that have the largest impact on the performance and derive guidelines for the design of the vehicular network and of the roadside infrastructure supporting it.

We consider content discovery in wireless networks with infrastructure, where mobile nodes store, advertise and consume contents while Broker entities running on infrastructure devices let demand and offer meet. We refer to this paradigm as match-making, highlighting its features within the confines of the standard publish-and-subscribe paradigm. We combine such content discovery scheme with an efficient caching strategy that ensures a high hit probability. Furthermore, through a reputation-based mechanism, we counter the nodes that deceive the Broker into believing they cached a content whereas they did not. The performance of our match-making scheme is derived through ns-3 emulation/simulation.

We present Figaro, a content discovery solution for mobile environments. Our main focus is on urban networks, in which high densities of users coexist in relatively narrow, circumscribed areas reached by an infrastructure (e.g., bus stops integrating an AP).