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.

Computer Vision (CV) tasks are among the most pivotal, yet challenging, operations for Uncrewed Aerial Vehicles (UAVs), especially in mission-critical applications. They require processing complex image data through Deep Neural Networks (DNNs), which demand computational resources far beyond UAVs’ capacity. To address this limitation, Split DNNs offer a promising solution by partitioning the model into: (i) a lightweightHead, deployed on the UAV for rapid, albeit less precise, initial image representations, and (ii) a more complexTail, executed at the network edge for refined, higher-accuracy results. However, this solution necessitates transmitting large tensor data from the UAV to the edge server, leading to significant bandwidth consumption. We tackle this challenge by introducing a goal-oriented framework named Compressed Tensor-based DNN Split (CoTeD). Our framework integrates an application- and system-aware optimization model that orchestrates computing and transmission resources in real time. At the UAV, CoTeD dynamically selects relevant tensor information and optimally trades-off between DNN detection quality and bandwidth consumption, guided by application requirements and system operational conditions. At the edge server, CoTeD reconstructs the tensor, enabling efficient inference by the Tail model. This approach effectively balances bandwidth usage with quality of the CV task output. Experimental results, obtained through our hardware-software testbed and using datasets with different sizes and characteristics, show that CoTeD can reduce data transmission over the radio link by up to 90% without noticeable loss in object detection quality and inference latency by up to 70% compared to local DNN deployment onboard the UAV. Also, CoTeD yields an inference request success rate of at least 90%, with an increase of 20%-80% compared to direct DNN splitting, static JPEG compression, and DNN model quantization.

In vehicular communications, accurate modeling of real-world radio propagation channels is essential. To this end, we propose a novel stochastic model, namedVehicular-Tapped Delay-Line(V-TDL) that accurately captures the statistical behavior of multipath channels characterized by path-dependent gains, delays, and Doppler shifts. V-TDL supports diverse traffic conditions and road geometries by generating channel instances through well-established probability distributions. Also, it effectively models the parameters of the distributions through realistic geometry-based simulations, achieving the accuracy of a ray-tracing-based model while maintaining the low complexity of a purely stochastic approach. In contrast to existing models, V-TDL accounts for the correlation between propagation paths. Our findings show that this correlation is inherent in high-speed vehicular environments and neglecting it leads to a significant overestimation of channel diversity and system performance. We compare our model to existing alternatives to assess the performance of OTFS and OFDM modulations. The results demonstrate that, unlike traditional models such as the 3GPP EVA, V-TDL captures variations in channel diversity influenced by traffic intensity and road geometry, which impact the OTFS and OFDM performance. Although OTFS is penalized by path correlation, it consistently outperforms OFDM in all evaluated vehicular environments, confirming its suitability for high-speed vehicular communication scenarios.

The availability of powerful Artificial Intelligence (AI) algorithms boosts the development of advanced functionalities in the automotive domain and is essential to enable the deployment of autonomous and semi-autonomous decision-making vehicles. However, integrating such advanced and complex functionalities in automotive systems is challenging due to several factors, including: (i) the mandatory compliance with strict safety regulations, which require effective strategies to ensure timely development while allowing thorough dependability evaluations, and (ii) the short time-to-market imposing limited development, verification, and validation periods. In particular, the analysis of the effects of faults affecting the hardware executing an AI-based application is made challenging by the target system’s complexity (in terms of both hardware and software). Reliability analysis often resorts to Fault Injection techniques. However, Fault Injection experiments are often unacceptably time-consuming and limited to some components of the overall system, thus failing to consider the fault impact at the vehicle level. This work proposes a new method, named Two-steps IntegrAted Reliability Assessment ( TIARA ), for early estimation of the impact at the vehicle level of faults affecting the hardware running AI-based perception tasks in the automotive domain. TIARA allows for the early exploration and evaluation of algorithms, driving agents, and critical operational scenarios. TIARA can estimate the effects of faults affecting a subsystem up to the vehicle level, integrating a fault injection approach at the neural network level with a commercial automotive-grade virtual scenario generator. When compared to previous works, TIARA dramatically reduces the required computational effort by adopting a two-stage evaluation strategy. It first performs static analysis to determine fault vulnerabilities and identify the most vulnerable parts (code blocks) in a targeted application. Then, it focuses on the most susceptible parts of the neural network and estimates system-level effects on vehicle dynamics by combining the system’s perception, control, and driving features. We validated our methodology through the exhaustive evaluation of two applications: Lane Centering Assistance (LCA) and Emergency Lane Keeping Assistance (ELKA), using the YoloP model for perception. The experimental results show that TIARA allows for an efective early estimation of systems reliability through relevant driving dynamics and comfort metrics on the nine evaluated driving scenarios, as mandated by standards, while reducing computing complexity by up to 43.2X in comparison with a fully exhaustive evaluation approach. In addition, the validation of the TIARA methodology through a hardware-in-the-loop implementation shows that the results closely match the behavior of a real-world system, demonstrating the versatility of the TIARA strategy for the evaluation of automotive systems.

Time Sensitive Networking (TSN) is fundamental for the reliable, low-latency networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome by using Target Wake Time (TWT), which enables the access point to instruct Wi-Fi stations to wake up and transmit in non-overlapping TWT Service Periods (SPs), and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), with the objective to schedule TWT SPs that maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, due to TASP being NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Using a TWT simulator based on ns-3, we compare TASPER to several baselines, including HSA, a state-of-the-art solution originally designed for WirelessHART networks. We demonstrate that TASPER obtains up to 24.97% lower mean transmission rejection cost and saves up to 14.86% more energy compared to the leading baseline, ShortestFirst, in a challenging, large-scale scenario. Additionally, when compared to HSA, TASPER also reduces the energy consumption by 34% and reduces the mean rejection cost by 26%. Furthermore, we validate TASPER on our IIoT testbed, which comprises 10 commercial TWT-compatible stations, observing that our solution admits more transmissions than the best baseline strategy, without violating any AoI deadline.

We consider the efficient resource allocation task in the blockchain-based mobile edge computing (MEC) system that requires decentralized transaction management to validate transactions between edge servers (ESs) and mobile devices (MDs). In such task allocation process (where MDs' resources are limited and privacy-sensitive), it is a significant challenge to guarantee individual rationality with satisfactory system stability while enabling flexible task offloading under various locality constraints (e.g., communication distance, bandwidth and delay). In this paper, we formulate the target problem as a blockchain-assisted task-resource matching model, and then propose a near optimal locality-aware resource allocation mechanism over smart contract to enable automatic and efficient transactions in MEC system. More specifically, for the service agents selection, we design the preference-based selection strategy to get highest estimated profit. For the flexible task offloading, we develop the minimum delay task graph partitioning algorithm to determine the optimal task offloading solution for MD under different resource bundles. For the task-resource matching, we propose a task-resource matching game (based on potential game) with the second lowest cost strategy to determine the matching of task-resource and decide the price of resource bundle. For the transaction verification and block allocation, we propose a social welfare-driven consensus mechanism to enable verified transaction and fair block allocation in a reward-free way. Strict theoretical analysis and extensive simulations demonstrate that our mechanism guarantees individual rationality, Nash Equilibrium, and stable near optimal solution.

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).

To address the limitations of existing wireless networks for demanding applications like brain-computer interfaces and intelligent transportation systems, we propose an advanced framework for joint resource allocation and task offloading across integrated terrestrial and non-terrestrial networks (TN-NTN). This framework utilizes multiple layers, including ground users, UAVs, HAPs, and satellites, to improve service quality and immersive experiences, particularly in scenarios like Metaverse applications. Ground users request resources, while UAVs and HAPs serve as resource providers, and satellites ensure reliable communication during emergencies. A double auction-based incentive scheme is employed in which operators control UAV and HAP resources to maximize utility, and users aim to minimize computation costs and protect data privacy. To handle the complexity of the operator-user interaction, which results in an NP-hard optimization problem, we applied a hierarchical multi-agent federated deep reinforcement learning (FeDRL) approach. Our simulation results demonstrate that the FeDRL algorithm significantly improves social welfare by 6.38%, 17.43%, and 28.73% over modified MADDPG, FRL, and DDPG algorithms, respectively.

Edge computing is a promising paradigm for deploying latency-sensitive applications (Apps) as it brings resources closer to end users. Edge Apps often adopt a microservice (MS) architecture, breaking monolithic Apps into lightweight, containerized MSs that can be dynamically and independently deployed. However, managing such Apps involves three key challenges: (i) optimizing the placement of MSs to reduce both response time and resource overhead, (ii) handling MS migration or relocation as users move while minimizing App service disruption (App downtime), and (iii) enabling MS sharing across Apps while ensuring performance guarantees. We formulate this as an optimization problem, named Multi-microservice Application Placement (MAP), prove its NP-hardness, and introduce STEP (State and Topology-aware Edge-MS Placement), a polynomial-time heuristic. STEP distinguishes itself from prior work by: (i) jointly considering stateful and stateless MS characteristics in deployment decisions, (ii) exploiting MS shareability to reduce resource usage, (iii) balancing response latency, App downtime, and resource utilization, and (iv) leveraging multiple versions of the same MS to adapt quality of service to available edge resources. Our results in a small-scale scenario show that STEP achieves near-optimal performance with only 7% higher CPU cost than the optimal solution. Large-scale real-time experiments on a Kubernetes cluster demonstrate that STEP consistently outperforms competing methods, achieving up to 50% lower deployment costs while delivering 50% gain in app quality and saving 15% in radio resources with over 90% request success rates.

We consider the collaborative inference acceleration task via cloud-edge-end collaboration, which involves a series of tightly coupled decision-making steps, includingwhichDNN model to be selected,how muchto compress model,howto partition model, andwhereto offload partitioned submodels. In practical deployments, these decisions jointly affect both fine-tuning and inference performance, and must jointly account for such aspects as the model being used, the computational resources and local datasets available at each device, as well as network latencies, which significantly increases the complexity of optimizing the problem. Yet, no existing studies focus on such joint optimization problem for these tightly coupled decisions. In this paper, we model this problem as a multi-dimensional optimization problem, jointly optimizing collaborative inference and fine-tuning by selecting the DNN model, compression level, partition strategy, and computational resource allocation, with the objective of minimizing the overall energy consumption of the learning-inference process, subject to accuracy and latency constraints. To this end, we propose an algorithmic framework called JQODI combining a time-energy tree diagram to represent the learning process, a dynamic programming solution strategy, and a data-driven theoretical approach to predict the expected total number of training epochs that meet the accuracy requirements. We prove that JQODI approximates the optimal solution with polynomial complexity. Numerical results demonstrate that JQODI surpasses state-of-the-art methods in both energy efficiency and latency.

Low Earth orbit (LEO) satellite communications play a critical role in achieving global connectivity, yet they face significant challenges due to high satellite mobility and incomplete channel state information (CSI). Moreover, the integration of reconfigurable intelligent surfaces (RIS) in certain scenarios introduces additional complexities. In this paper, we propose a novel MIMO channel prediction framework tailored for LEO satellite communications involving unmanned aerial vehicle-mounted RIS (UAV-RIS), employing a spatiotemporal-attention (ST-attention) mechanism to capture both the spatial correlations among antennas and the temporal dynamics of rapidly varying channels. Furthermore, we leverage masked pretraining to enhance the model’s robustness under scenarios of severe CSI incompleteness, enabling effective reconstruction of missing channel information. Comprehensive simulations demonstrate that our approach outperforms traditional model-based predictors, whether historical CSI is fully available or only partially observed.

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.

Edge computing reduces latency for mobile applications (Apps) by processing data closer to users, while containerized microservices (MSs) enable their modular deployment. Managing such Apps involves three key challenges: (i) strategically placing MSs to minimize response latency and resource consumption, (ii) managing MS migration/relocation during user mobility or traffic load changes while limiting App downtime, and (iii) enabling MS sharing across Apps while ensuring target performance. We formulate this as an optimization problem (proven to be NP-hard) and propose STEP, a polynomial-time heuristic. In contrast to prior art, STEP (i) jointly considers stateful and stateless MSs in its decisions, (ii) exploits MS shareability to reduce resource usage, (iii) balances response latency, App downtime, and resource utilization, and (iv) leverages multiple versions of the same MS to adapt QoS to available edge resources. Results show that STEP achieves near-optimal performance with only 1.6% higher deployment cost while reducing CPU usage by 42% compared to baselines. Also, it enables real-time App deployment in a large-scale scenario on a Kubernetes cluster with sub-second order execution time and reduced deployment cost by 16-17% compared to its benchmarks.

Distributed sensor fusion is a key component of a broad spectrum of applications, such as autonomous systems, where the ability of jointly process multi-sensor data at the edge boosts the range of operating conditions and overall task performance. However, existing distributed sensor fusion approaches encounter limitations in achieving efficient transmission and computation, primarily due to sensor data redundancy, unreliable sensor data transmission, and inflexible sensor fusion methods. In this paper, we propose HERACLES, a distributed sensor fusion framework that connects multi-branched dynamic neural network architectures, which we extend to include branches of different complexity, to (i) a computing methodology that distributes portions of the multi-branched neural network across mobile devices and edge servers, enabling flexible semantic feature extraction and sensor fusion; (ii) a hierarchical modulation-based transmission strategy, where multi-modal semantic features are allocated to different modulation layers to provide varying levels of error protection, and (iii) an infrastructure-level logic that controls the matching between semantic features and modulation layers, and the complexity of the neural model itself to meet an accuracy target while minimizing latency and energy consumption. As a result, HERACLES deeply connects computing, communications and resource allocations in a semantic and context-aware fashion. We evaluate HERACLES using real-world datasets and demonstrate that it can reduce the total delay and energy consumption by 20.39%–89.41% and 4.86%–88.17% (resp.), while maintaining near-optimal inference accuracy. The evaluation code is available at https://github.com/qlt315/HERACLES.

This paper evaluates the performance of LoRa technology operating in the ISM2400 band (2400-2483 MHz) in rural environments, focusing on its potential applications for precision agriculture. Sub-1 GHz bands like the EU868 and the US915 are already implemented for LoRa networks deployments, but the ISM2400 one may offer advantages with no duty cycle limitations and common regulatory prescriptions worldwide. However, the band faces challenges due to higher noise levels and reduced propagation performance through obstacles. For this reason, we have conducted some preliminary field tests, with results that demonstrate reliable communication in Line-of-Sight (LOS) over distances at least equal to 18 km with a Packet Delivery Ratio (PDR) of 100 %. We have also compared the obtained results in the EU868 band. Despite the limitations of the ISM2400 band, we highlight its potential for real-time, long-range, low-data-rate links, particularly suitable for agricultural applications such as autonomous farming vehicles control.

Federated learning (FL) enables collaborative training of machine learning models without sharing sensitive client data, making it a cornerstone for privacy-critical applications. However, FL faces the dual challenge of ensuring learning quality and robust privacy protection while keeping resource consumption low, particularly when using computationally expensive techniques such as homomorphic encryption (HE). In this work, we enhance an FL process that preserves privacy using HE by integrating it with synthetic data generation and an interleaving strategy. Specifically, our solution, named Alternating Federated Learning (Alt-FL), consists of alternating between local training with authentic data (authentic rounds) and local training with synthetic data (synthetic rounds) and transferring the encrypted and plaintext model parameters on authentic and synthetic rounds (resp.). Our approach improves learning quality (e.g., model accuracy) through datasets enhanced with synthetic data, preserves client data privacy via HE, and keeps manageable encryption and decryption costs through our interleaving strategy. We evaluate our solution against data leakage attacks, such as the DLG attack, demonstrating robust privacy protection. Also, Alt-FL provides 13.4% higher model accuracy and decreases HE-related costs by up to 48% with respect to Selective HE.

The Open Radio Access Network (O-RAN) architecture is key to enabling AI-driven dynamic network management. However, the complexity of this architecture introduces challenges, especially in managing conflicts between different AI-driven applications that operate concurrently within the network. These conflicts, if left unchecked, can lead to degraded network performance and service disruptions. To address this issue, we propose XAI4C (Explainable AI for Conflict Detection), a framework that leverages the SHAP (SHapley Additive exPlanations) explainable AI technique. XAI4C enhances transparency and interpretability in AI decision-making by helping network operators understand the factors driving AI decisions across different network components thereby allowing for early detection of conflicts between applications. In this paper, we first present the architecture and operation of the XAI4C framework. We then demonstrate its effectiveness in conflict detection through two case studies related to network slicing. Our results demonstrate that XAI4C outperforms the state-of-the-art PACIFISTA providing a detection accuracy increase up to 30%, while reducing the number of samples required for conflict detection by 41.17%.

The fusion of multi-modal information, such as images and LiDAR scans, is instrumental to maximize the performance of many computer vision tasks in next generation systems and applications. However, supporting fusion necessitates considerable effort, challenging the availability of computing and communication resources in edge systems. This work addresses this challenge by maximizing resource efficiency in systems where mobile devices collect multi-modal sensor data and use dynamic multi-branched DNN models to adapt inference to the operating context. To tune the overall system response to the context (e.g., weather conditions), we propose a dual-scale control approach: centralized orchestration of spectrum resources, and distributed individual device-level control of the execution path of the dynamic DNN fusion models. The control agents are driven by a novel context-aware decision-making method combined with game theory, named Context-Aware Network Slicing Auction (CANSA), which optimizes DNN inference performance, network slicing, and energy consumption. The decision-making performs such optimization by: (i) selecting data and features that best fit the current context; (ii) deciding on the appropriate DNN model complexity, including the use of multi-modal sensor fusion techniques for better data integration, and (iii) deploying these models on the most appropriate nodes (local nodes or edge servers). Results, obtained using real-world multi-modal data, show that CANSA surpasses conventional allocation methods by up to 52.3% in terms of inference task success rate.

Federated testbeds enable collaborative research by providing access to diverse resources, including computing power, storage, and specialized hardware like GPUs, programmable switches and smart Network Interface Cards (NICs). Efficiently sharing these resources across federated institutions is challenging, particularly when resources are scarce and costly. GPUs are crucial for AI and machine learning research, but their high demand and expense make efficient management essential. Similarly, advanced experimentation on programmable data plane requires very expensive programmable switches (e.g., based on P4) and smart NICs. This paper introduces SHARY (SHaring Any Resource made easY), a dynamic reservation system that simplifies resource booking and management in federated environments. We show that SHARY can be adopted for heterogenous resources, thanks to an adaptation layer tailored for the specific resource considered. Indeed, it can be integrated with FIGO (Federated Infrastructure for GPU Orchestration), which enhances GPU availability through a demand-driven sharing model. By enabling real-time resource sharing and a flexible booking system, FIGO improves access to GPUs, reduces costs, and accelerates research progress. SHARY can be also integrated with SUP4RNET platform to reserve the access of P4 switches.

The growing interest in Orthogonal Time Frequency Space (OTFS) modulation for vehicular communication systems requires the validation of its advantages using suitable channel models capable of emulating the dynamic and geometric complexities of vehicle-to-infrastructure systems. This paper presents a novel, realistic geometric-based channel model, called V-CORE, specifically designed for evaluating the performance of OTFS in vehicular scenarios. Our model accurately characterizes the scattered paths by exploiting the radar cross-section of the vehicles that populate a road according to a given vehicular traffic intensity. Multiple road scenarios with different geometry and vehicle velocities are considered, resulting in a flexible tool to evaluate system performance in different traffic contexts. The V-CORE model provides the channel variables, particularly relevant to OTFS implementation, such as multipath Doppler shift and delay. We assess the performance of OTFS against that of OFDM under different road structures, traffic intensity, and vehicle velocities. Further, we compare the proposed V-CORE model to the Extended Vehicular A model and demonstrate that ours provides a deeper insight into performance in high-speed vehicular scenarios.

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.

Microservices (MSs)-based architectures have become the de facto standard for designing and implementing edge computing applications. In particular, by leveraging Network Performance Metrics (NPMs) coming from the Radio Access Network (RAN) and sharing context-related information, AI-driven MSs have demonstrated to be highly effective in optimizing RAN performance. In this context, this work addresses the critical challenge of ensuring efficient data sharing and consistency by proposing a holistic platform that regulates the collection and usage of NPMs. We first introduce two reference platform architectures and detail their implementation using popular, off-the-shelf database solutions. Then, to evaluate and compare such architectures and their implementation, we develop PACE, a highly configurable, scalable, MS-based emulation framework of producers and consumers of NPMs, capable of realistically reproducing a broad range of interaction patterns and load dynamics. Using PACE on our cloud computing testbed, we conduct a thorough characterization of various NPM platform architectures and implementations under a spectrum of realistic edge traffic scenarios, from loosely coupled control loops to latency- and mission- critical use cases. Our results reveal fundamental trade-offs in stability, availability, scalability, resource usage, and energy footprint, demonstrating how PACE effectively enables the identification of suitable platform solutions depending on the reference edge scenario and the required levels of reliability and data consistency.

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.

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor.

In this paper we study spreading sequences for Code Division Multiplexing (CDM) generated from the AES algorithm in counter mode. These sequences are robust against jamming because they cannot be reconstructed from one of their segments. Additionally, they are flexible, have a large cardinality, and can be obtained from a small key. We show how their linear complexity profile, error probability, and acquisition performance are aligned with those of random sequences. To further enhance them, we introduce a new family of mixed AES/Gold sequences. First, we demonstrate how we can generate cosets of extended Gold sequences which are perfectly orthogonal for CDM. Then, we combine AES sequences and Gold cosets: the new sequences have better performance in terms of error probability and acquisition, while maintaining protection against jamming. All results are derived analytically and validated through simulation. As a case study, we consider an uplink scenario from a ground station to a constellation of Low Earth Orbit satellites. The proposed mixed sequences allow for a significant increase in the number of satellites that can be served in parallel, while maintaining the same level of performance and protection against jamming.

With the widespread adoption of massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) communication techniques, the overhead of beam measurement and the complexity of beam management become even more severe issues due to the dramatic increase of the number of beams. Traditional methods often overlook the selection of optimal probing beams, thus limiting beam prediction performance. Additionally, existing solutions based on deep learning exhibit high complexity, which often hinders their practical deployment. In this work, we propose a lightweight, integrated neural network approach tailored for joint probing-beam pattern selection and beam prediction. Specifically, our solution includes two main components. First, formulating the selection of probing beams as a sampling operation, we envision a sampling network where, to enable gradient back-propagation of network parameters in spite the non-differentiable nature of sampling, the standard sampling function is approximated with a fitting function. Then, drawing inspiration from the physical structure of antenna arrays and 3D beam formation, we develop a beam-prediction network based on convolutional neural networks and self-attention mechanisms. Experimental results demonstrate that, thanks to the learned pattern, our proposed scheme achieves very good prediction performance (exceeding the state of the art by over 15% in top-1 accuracy), using a neural network with almost 90% less parameters than existing machine learning-based solutions.

The rapid evolution of 5 G networks, with diverse traffic classes and demanding services, highlights the importance of Open Radio Access Networks (O-RAN) for enabling RAN intelligence and performance optimization. Machine Learning-powered xApps offer novel network control opportunities, but their resource demands necessitate efficient orchestration. To address these issues, we present OREO, an O-RAN xApp orchestrator that, using a multi-layer graph model, aims to maximize the number of RAN services concurrently deployed while minimizing their overall energy consumption. OREO's key innovation lies in the concept of sharing xApps across RAN services when they include semantically equivalent functions and meet quality requirements. Despite the NP-hard nature of the problem, numerical results show that OREO offers a lightweight and scalable solution that closely and swiftly approximates the optimum in several different scenarios. Also, OREO outperforms state-of-the-art benchmarks by enabling the co-existence of more RAN services (14.3% more on average and up to 22%), while reducing resource expenditure (by 48.7% less on average and up to 123% for computing resources). Moreover, using an experimental prototype deployed on the Colosseum network emulator and using real-world RAN services, we show that OREO leads to substantial resource savings (up to 66.7% of computing resources) while its xApp sharing policy can significantly enhance quality of service.

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%.

In resource-constrained cloud systems, e.g., at the network edge or in private clouds, serverless computing is increasingly adopted to deploy microservices-based applications, leveraging its promised high resource efficiency. Provisioning resources to serverless services, however, poses several challenges, due to the high cold-start latency of containers and stringent Service Level Agreement (SLA) requirements of the microservices. In response, we investigate the behavior of containers in different states (i.e., running, warm, or cold) and exploit our experimental observations to formulate an optimization problem that minimizes the energy consumption of the active servers while reducing SLA violations. In light of the problem complexity, we propose a low-complexity algorithm, named AiW, which utilizes a multi-queueing approach to balance energy consumption and system performance by reusing containers effectively and invoking cold-starts only when necessary. To further minimize the energy consumption of data centers, we introduce the two-timescale COmputing resource Management at the Edge (COME) framework, comprising an orchestrator running our proposed AiW algorithm for container provisioning and Dynamic Server Provisioner (DSP) for dynamically activating/deactivating servers in response to AiW’s decisions on request scheduling. COME addresses the mismatch in timescales for resource provisioning decisions at the container and server levels. Extensive performance evaluation through simulation shows AiW’s close match to the optimum and COME’s significant reduction in power consumption by 22–64% compared state-of-the-art alternatives.

Stateful migration has emerged as the dominant technology to support microservice mobility at the network edge while ensuring a satisfying experience to mobile end users. This work addresses two pivotal challenges, namely, the implementation and the orchestration of the migration process. We first introduce a novel framework that efficiently implements stateful migration and effectively orchestrates the migration process by fulfilling both network and application KPI targets. Through experimental validation using realistic microservices, we then show that our solution (i) greatly improves migration performance, yielding up to 77% decrease of the migration downtime with respect to the state of the art, and (ii) successfully addresses the strict user QoE requirements of critical scenarios featuring latency-sensitive microservices. Further, we consider two practical use cases, featuring, respectively, a UAV autopilot microservice and a multi-object tracking task, and demonstrate how our framework outperforms current state-of-the-art approaches in configuring the migration process and in meeting KPI targets.

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 paper introduces Opportunistic Federated Learning (OFL) as an approach to enhance the efficiency of distributed learning in intelligent IoT systems. OFL allows any node in the network to initiate a learning task and collaboratively use local resources. The framework enables nodes to adapt configurations based on circumstances, optimizing resource utilization. Hence, this paper proposes a reliable node selection mechanism that accommodates the dynamic nature of local data and computing resources. Incentives for participating nodes are explored through a peer-to-peer communication using the Bertrand game to determine optimal pricing strategies. Results demonstrate the Nash equilibrium of the game-based incentive mechanism in a realistic FL setup.

Early Exiting (EE) is an emerging computing paradigm where Deep Neural Networks (DNNs) are equipped with earlier classifiers, enabling trading-off accuracy with inference latency. EE can be effectively combined with edge computing, a paradigm that allows mobile nodes to offload complex tasks, such as the execution of DNNs, to servers at the edge of the network, thus reducing computing times and energy consumption at the mobile devices. The integration of such technologies is particularly attractive for the support of applications for connected and automated driving. In this paper, we consider a system that jointly leverages the benefits of EE and edge computing, and we model their complex interactions by means of a Markov Decision Process (MDP). We then formulate an optimization problem to select the inference strategy that maximizes the average task accuracy. Importantly, such an optimization problem has low complexity, as the optimal policy can be derived by mapping the MDP into a linear program. Our numerical results focus on a use case centered on automated vehicles connected with an edge server under varying channel and network conditions, and show that our solution achieves up to 11% higher accuracy compared to the optimal policy with no EE.

Emerging mobile applications often require the execution of computer vision (CV) tasks based on compute-and memory-intensive deep neural networks (DNNs). Although offloading CV tasks to edge servers can decrease resource consumption at the mobile devices, it poses the challenge of handling multiple concurrent tasks with limited computing and memory capacity. In stark opposition with the existing state of the art, we tackle this challenge by jointly optimizing (i) the utilization of resources at the edge, among which memory - so far widely overlooked - and the radio resources used for task offloading; (ii) which and how many offloaded tasks should be executed; and (iii) the structure of the DNNs. First, we formulate the DNN for scalable Offloading of Tasks (DOT) problem, prove that it is NP-hard, and envision a weighted-tree-based heuristic solution, named OffloaDNN, that efficiently solves the DOT problem. We evaluate OffloaDNN through extensive numerical analysis using state-of-the-art image classification ResNet-18, as well as real-world experiments on the Colosseum emulator. The numerical results show that, in small-scale scenarios, OffloaDNN matches the optimum very closely, and, in larger-scale scenarios, increases the number of admitted offloaded tasks by 26.9 % with respect to the state of the art, while saving 82.5 % memory and 77.4% per-inference computing time. The numerical results are confirmed by the real-world validation on Colosseum.

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.

The Open Radio Access Network (O-RAN) architecture aims to support a plethora of network services, such as beam management and network slicing, through the use of third-party applications called xApps. To efficiently provide network services at the radio interface, it is thus essential that the deployment of the xApps is carefully orchestrated. In this paper, we introduce OREO, an O-RAN xApp orchestrator, designed to maximize the offered services. OREO’s key idea is that services can share xApps whenever they correspond to semantically equivalent functions, and the xApp output is of sufficient quality to fulfill the service requirements. By leveraging a multi-layer graph model that captures all the system components, from services to xApps, OREO implements an algorithmic solution that selects the best service configuration, maximizes the number of shared xApps, and efficiently and dynamically allocates resources to them. Numerical results as well as experimental tests performed using our proof-of-concept implementation, demonstrate that OREO closely matches the optimum, obtained by solving an NP-hard problem. Further, it outperforms the state of the art, deploying up to 35% more services with an average of 30% fewer xApps and a similar reduction in the resource consumption.

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.

This paper presents a next-generation architecture that focuses on the advancement of edge computing and Internet of Things (IoT) technologies in the context of the automotive supply value chain for electric vehicles (EVs). First, we outline the general architecture design, the specific layers and their goals. Based on the principles of the proposed architecture, we also give a use case for improving the traceability, monitoring and efficiency of EV battery transportation using innovative approaches in federated data spaces, AI-powered inference and orchestration of a multi-objective computational continuum. The automotive supply chain use case is presented with potential Key Performance Indicators (KPIs) while emphasizing the potential impact on operational efficiency, cost reduction and sustainability. By addressing the current limitations in distributed intelligence, data governance, and cross-domain interoperability, we emphasize the importance of real-time data processing, dynamic field governance, and energy-efficient machine learning in the context of the electric vehicle supply chain. At the end of the paper, a discussion and comparative analysis highlights the advances over existing technologies and frameworks and identifies future directions to further improve innovations and applications in this area.

Time Sensitive Networking (TSN) is fundamental for the low-latency, reliable, and energy-efficient networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome using Target Wake Time (TWT) to instruct Wi-Fi stations to wake up and transmit in non-overlapped TWT Service Periods (SPs) and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), whose objective is to schedule TWT SPs as to maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, since the TASP is NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Finally, we compare TASPER with several baselines through numerical analysis and simulations, which we performed using a TWT-compatible simulator based on ns-3. We demonstrate that TASPER schedules traffic with up to 21.23% higher priority-weighted admission ratio and saves up to 7.42% energy compared to the ShortestFirst strategy, all while satisfying AoI constraints for 99.5% of transmissions.

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%.

Stateful migration has emerged as the dominant technology to support microservice mobility at the network edge while meeting the end users' QoE requirements. In this context, our work addresses the two pivotal challenges of implementing and orchestrating the migration process. We first introduce a novel orchestration framework that efficiently realizes stateful migration and effectively orchestrates the migration process by fulfilling both network and application KPI targets. Then, through experimental validation using realistic microservices, we show that our solution improves migration performance, yielding up to 80 % decrease of the migration downtime with respect to the state of the art. Finally, we demonstrate that our framework can be exploited to successfully address critical scenarios featuring latency-sensitive microservices and strict user QoE requirements.

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.

Network slicing represents a substantial technological advance in 5G mobile network, greatly expanding the variety and manifoldness of network services to be supported. Additionally, 3GPP 5G New Radio (NR) has introduced novel features such as mixed numerology and mini-slots, which can be harnessed by network slicing to cater to the diverse requirements of 5G services. While however the co-existence of multiple network slices leads to a challenging resource allocation problem, these new features also severely complicate the management of radio resources. As a further point of attention, the virtualization of radio functions may exact a significant toll from the, already limited, computing resources at the network edge. It follows that a cost-efficient resource allocation across all the slices becomes crucial. In this paper, we address the above-mentioned issues by modeling a cost-efficient radio resource management in 5G NR featuring network slicing, named CERS, through a Mixed Integer Quadratically constrained Program (MIQCP). We maximize the profit of all slices simultaneously guaranteeing the target data rate and delay specified in the service level agreements (SLAs) fo the different traffic flows. To reduce the complexity of the MIQCP problem, we decompose it into two sub-problems, namely, the scheduling problem of enhanced Mobile Broadband (eMBB) user equipments (UEs) on a time-slot basis and of Ultra-Reliable Low Latency Communications (uRLLC) UEs on a mini-slot basis, while keeping the objective unchanged. To address the scheduling issue of eMBB UEs, we employ a heuristic technique, and, by leveraging the outcome of this heuristic, we derive an optimal solution for the problem of uRLLC UEs. The significance of the proposed approach over a baseline approach is evaluated through extensive numerical simulations in terms of the number of allocated uRLLC resource blocks (RBs) per mini-slot. We also assess our approach by measuring the impact of the uRLLC slice changes on the eMBB slice, and vice versa, including delay for uRLLC users and data rates for eMBB users.

The automotive field is evolving towards high levels of automation, requiring seamless data exchange between vehicles through Vehicle-to-Everything (V2X) communications. Direct V2X technology is already being deployed on commercial vehicles, and it has the potential to deliver a range of safety and efficiency benefits on the road. However, the deployment of V2X-based applications is a complex process that demands extensive testing before these systems can be widely used by the public; indeed, high costs and safety concerns are among the main hurdles to overcome before applications leveraging V2X communication can become a reality. It is thus critical to reliably validate through simulation and emulation both the V2X technologies and the applications in realistic scenarios, before performing large-scale road tests. To address this pressing need, we present an open source framework for the virtual validation of V2X-based applications, amenable to the development and testing not only of different access technologies within the same environment (IEEE 802.11p, LTE-V2X, 5G NR-V2X, and LTE), but also of any kind of V2X-based application using ETSI-compliant messages. Our framework, called ms-van3t, is based on the ns-3 and SUMO (Simulation of Urban MObility) simulators, it implements a full ETSI C-ITS stack for CAM, DENM and IVIM messages, and it provides several novel features not found elsewhere. Further, ms-van3t enables the testing of V2X-based applications in HIL (Hardware-In-the-Loop) scenarios, thanks to a dedicated emulation mode, and it allows users to easily select different physical and MAC layer models, seamlessly collecting performance statistics. To showcase the capabilities of the framework, we present three sample applications as well as the performance results we obtained in terms of both application-related and network-related key performance indicators.

The next generation of mobile networks (NextG) will require careful resource management to support edge offloading of resource-intensive deep learning (DL) tasks. Current slicing frameworks treat all DL tasks equally without adjusting to their high-level objectives, resulting in sub-optimal performance. To overcome this, we proposeSEM-O-RAN, a semantic and flexible slicing framework for computer vision task offloading in NextG Open RANs. Our framework accounts for the semantic nature of object classes as well as the level of data quality to optimally tailor data compression and minimize the usage of networking and computing resources. In fact, we show that different object classes tolerate different levels of image compression while preserving detection accuracy. To address the above issues, we first present the mathematical formulation of the Semantic Flexible Edge Slicing Problem (SF-ESP), which turns out to be NP-hard. We thus define a greedy algorithm to solve it efficiently, which is also able to always select the resource allocation that yields the best resource utilization, whenever multiple allocations satisfy the DL task requirements. We evaluateSEM-O-RAN's performance through extensive numerical analysis and real-world experiments on the Colosseum testbed, considering state-of-the-art computer-vision tasks and DL models. The obtained results demonstrate thatSEM-O-RANallocates up to 169% more tasks and obtains 52% higher revenues than the state of the art.

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over$96\%$of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is$54\%$lower than a centralized framework based on deep-Q networks.

Stateful migration has emerged as the key solution to support latency-sensitive microservices at the edge while ensuring a satisfying experience for mobile users. In this paper, we address two relevant issues affecting stateful migration, namely, the migration of containerized microservices and that of the associated data connection. We do so by first introducing a novel network solution, based on OvS, that permits to preserve the established connection with mobile end users upon migrating a microservice. Then, using Podman and CRIU, we experimentally characterize the fundamental migration KPIs, i.e., migration duration and microservice downtime, and we devise an analytical model that, accounting for all the relevant real-world aspects of stateful migration, provides an accurate upper bound on such KPIs. We validate our model using real-world microservices, namely, MQTT Broker and Memcached, and show that it can predict KPIs values with an error that is up to 99.7% smaller than that yielded by the state of the art. Finally, we consider a UAV controller as relevant microservice use case and demonstrate how our model can be exploited to effectively configure the system parameters so that the required QoE level is met.

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.

Multi-view observations potentially offer a more comprehensive understanding of real-world phenomena compared to observations acquired from a single viewpoint. Existing models that utilize multi-view data often consider that all views are available during inference, but this assumption may not hold in practical scenarios. To address this limitation, we introduce MVLD, a novel method that, by employing a deterministic autoencoder and a score-based diffusion model, is capable of imputing missing views. We finally envision MVLD being used in a communication system for image transmission.

Serverless edge computing allows for highly efficient resource utilization, reducing the energy footprint of edge data centers. Indeed, the containers can be dynamically created and destroyed, allowing to adapt the workload to the available resources. Creating containers upon arrivals of service requests entails, however, a high start-up latency, which may be unsuitable for time-critical services. As alternative solution, pre-started containers (“warm containers”) are used to decrease start-up latency, but incurring in higher resource costs. In this work, we minimize the energy consumption of the active servers in the data center by optimally managing the various container states while meeting the target delay of the requested services. Further, in light of the problem complexity, we investigate how a simple threshold-based algorithm performs and show that it can closely match the optimum.

We consider the problem of accurately localizing$N$unmanned aerial vehicles (UAV) in 3D space where the UAVs are part of a swarm and communicate with each other through orthogonal time-frequency space (OTFS) modulated signals. Each receiving UAV estimates the multipath wireless channel on each link formed by the line-of-sight (LoS) transmission and by the single reflections from the remaining$N-2$UAVs. The estimated power delay profiles are communicated to an edge server, which is in charge of computing the exact location of the UAVs. To obtain the UAVs locations, we propose an iterative algorithm, named Turbo Iterative Positioning (TIP), which, using the belief-propagation approach, effectively exploits the time difference of arrival (TDoA) measurements between the LoS and the non-LoS paths. Enabling a full cold start (no prior knowledge), our solution first maps each TDoA's profile element to a specific ID of the reflecting UAV's. The localization of the$N$UAVs is then derived via gradient descent optimization, with the aid of turbo-like iterations that can progressively correct some of the residual errors in the initial ID mapping operation. Our numerical results, obtained also using real-world traces, show how the multipath links are beneficial to achieving very accurate localization of all UAVs, even with a limited delay resolution. Robustness of our scheme is proven by its performance approaching the Cramer-Rao bound.

The space-air-ground integrated network (SAGIN) is an emerging architecture that has the potential to provide seamless, high data rates, and reliable transmission with a vastly increased coverage for intelligent edge devices (iEDs). However, the SAGIN infrastructure is quite complex consisting of multiple network segments; it is thus critical to efficiently manage the network segments' resources to ensure QoS satisfaction (e.g., delay and rate) for the various services provided to the iEDs. In this regard, network slicing (NS) and overall network softwarization technologies can play an essential role in addressing iEDs QoS and utility needs. In this work, we propose an optimal intelligent end-to-end resource allocation with network slicing in multi-tier SAGIN to maximize the network performance. We model the network depending on its service requirements. As the above optimization problem turns out to be NP-hard, we transform it into a stochastic game model and efficiently solve it through hierarchical multi-agent deep reinforcement learning (HMADRL). In particular, we decompose it into two parts, i.e., optimizing the mapping combined with slice adjustment and the resource allocation with association problem. Both problems are then solved using multi-agent DRL. The simulation results demonstrate that our proposed HMADRL algorithm outperforms the baseline algorithms in terms of maximizing the utility and QoS satisfaction of iEDs.

The efficient allocation of radio resources is an essential trait of 5G/6G radio access networks (RANs), as they are called to meet diverse QoS requirements of highly demanding applications. To equip RANs with such an ability and, at the same time, meet their function split constraints, we envision a distributed learning approach for radio resource allocation that makes the most out of the Central Unit (CU) and Distributed Unit (DU) components by effectively exploiting their synergy. On the one hand, our solution, named MERGE, leverages the knowledge of the radio connectivity dynamics that each DU can acquire through the local use of a deep reinforcement learning radio agent. On the other hand, it lets the CU collect such agents in a crowdsourcing fashion, and, then, thanks to a meta-learning policy, properly select and aggregate them to create up-to-date radio agents of the right size (hence, complexity level) to fit the computing constraints of the individual DUs. Our results show that MERGE can match the performance of the highest-complexity radio model in [1] with 25% less computational requirements, and, for a given computational resource, it outperforms a single pruned model with a 19% increase in QoS.

To support latency sensitive microservices at the edge, stateful container migration has gathered momentum as a key solution to ensure a satisfying experience to mobile users. In this paper, we first investigate experimentally the stateful migration process, by using state-of-the-art tools, namely, Podman and CRIU. We then characterize the main migration KPIs, i.e., migration duration and downtime, and develop an analytical model that can effectively assess whether stateful migration is feasible while meeting the user's QoE requirements. Importantly, our model is validated using real-world microservices and, by accounting for all relevant real-world aspects of stateful migration, significantly outperforms state-of-the-art models.

5G and beyond cellular networks (NextG) will support the continuous execution of resource-expensive edgeassisted deep learning (DL) tasks.To this end, Radio Access Network (RAN) resources will need to be carefully "sliced" to satisfy heterogeneous application requirements while minimizing RAN usage.Existing slicing frameworks treat each DL task as equal and inflexibly define the resources to assign to each task, which leads to sub-optimal performance.In this paper, we propose SEM-O-RAN, the first semantic and flexible slicing framework for NextG Open RANs.Our key intuition is that different DL classifiers can tolerate different levels of image compression, due to the semantic nature of the target classes.Therefore, compression can be semantically applied so that the networking load can be minimized.Moreover, flexibility allows SEM-O-RAN to consider multiple edge allocations leading to the same task-related performance, which significantly improves system-wide performance as more tasks can be allocated.First, we mathematically formulate the Semantic Flexible Edge Slicing Problem (SF-ESP), demonstrate that it is NP-hard, and provide an approximation algorithm to solve it efficiently.Then, we evaluate the performance of SEM-O-RAN through extensive numerical analysis with state-of-the-art multi-object detection (YOLOX) and image segmentation (BiSeNet V2), as well as realworld experiments on the Colosseum testbed.Our results show that SEM-O-RAN improves the number of allocated tasks by up to 169% with respect to the state of the art.

Following the Industry 4.0 vision of a full digitization of the industry, time-critical services and applications, allowing network infrastructures to deliver information with determinism and reliability, are becoming more and more relevant for a set of vertical sectors. As a consequence, deterministic network solutions are progressively emerging, albeit they are still bounded to specific technological domains. Even considering the existence of interconnected deterministic networks, the provision of an end-to-end (E2E) deterministic service over them must rely on a specific control plane architecture, capable of seamlessly integrate and control the underlying multi-technology data plane. In this work, we envision such a control plane solution, extending previous works and exploiting several innovations and novel architectural concepts. The proposed control architecture is service-centric, in order to provide the necessary flexibility, scalability, and modularity to deal with a heterogenous data plane. The architecture is hierarchical and encompasses a set of management platforms to interact with specific network technologies overarched by an E2E platform for the management, monitoring, and control of E2E deterministic services. Furthermore, Artificial Intelligence (AI) and Digital Twinning are used to enable network predictability and automation, as well as smart resource allocation, to ensure service reliability in dynamic scenarios where existing services may terminate and new ones may need to be deployed.

Network slicing is a promising technique that has vastly increased the manifoldness of network services to be supported through isolated slices in a shared radio access network (RAN). Due to resource isolation, effective resource allocation for coexisting multiple network slices is essential to maximize network resource efficiency. However, the increased network flexibility and programmability offered by virtualized radio access networks (vRANs) come at the expense of a higher consumption of computing resources at the network edge. Additionally, the relationship between resource efficiency and computing cost minimization is still fuzzy. In this paper, we first perform extensive experiments using the vRAN testbed we developed and assess the vRAN resource consumption under different settings and a varying number of users. Then, leveraging our experimental findings, we formulate the problem of cost-efficient network slice dimensioning, named cost-efficient slicing (CES), which maximizes the difference between total utility and CPU cost of network slices. Numerical results confirm that our solution leads to a cost-efficient resource slicing, while also accomplishing performance isolation and guaranteeing the target data rate and delay specified in the service level agreements.

Since the start of 5G New Radio (NR) work in the 3rd Generation Partnership Project (3GPP) in early 2016, tremendous progress has been made in both standardization and commercial deployments. The first 5G NR release (Release 15) laid out a solid foundation in accommodating a diverse set of services, a wide range of spectra, and a variety of deployment scenarios, while being forward compatible. Expansion to vertical domain services [e.g., vehicle to everything (V2X), non-terrestrial networks (NTN)] was introduced in Release 16. Such an expansion was further accelerated in Release 17, with the standardization work being completed despite the extreme challenges due to COVID-19.

Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G standardization, known as5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of 5G-Advanced, includes a diverse set of features that cover both device and network evolutions, providing balanced mobile broadband evolution and further vertical domain expansion and accommodating both immediate and long-term commercial needs. 5G-Advanced will significantly expand 5G capabilities, address many new use cases, transform connectivity experiences, and serve as an essential step in developing mobile communications towards 6G. This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization.

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 resource-constrained cloud systems, e.g., at the network edge or in private clouds, it is essential to deploy microservices (MSs) efficiently. Unlike most of the existing approaches, we tackle this issue by accounting for two important facts: (i) the interference that arises when MSs compete for the same resources and degrades their performance, and (ii) the MSs’ deployment time. In particular, we first present some experiments highlighting the impact of interference on the throughput of MSs co-located in the same server, as well as the benefits of MSs’ parallel deployment. Then, we formulate an optimization problem that minimizes the number of used servers while meeting the MSs’ performance requirements. In light of the problem complexity, we design a low-complexity heuristic, called iPlace, that clusters together MSs competing for resources as diverse as possible and, hence, interfering as little as possible. Importantly, clustering MSs also allows us to exploit the benefit of parallel deployment, which greatly reduces the deployment time as compared to the sequential approach applied in prior art and by default in state-of-the-art orchestrators. Our numerical results show that iPlace closely matches the optimum and uses 21-92% fewer servers compared to alternative schemes while proving to be highly scalable. Further, by deploying MSs in parallel using Kubernetes, iPlace reduces the deployment time by 69% compared to state-of-the-art solutions.

We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services’ target delay. We model and formalize the problem of finding a feasible chain placement and resource allocation, while minimizing the migration, bandwidth, and computation costs. We tackle this problem by partitioning it into a (i) CPU allocation problem, and a (ii) placement problem. For the CPU allocation problem, we find an optimal solution. For the placement problem, we show that even finding a feasible solution is NP-hard, and envision an algorithm that is guaranteed to find a feasible solution while leveraging a bounded amount of resource augmentation. Our algorithms are incorporated into a solution framework that aims to minimize both the cost and the required resource augmentation. The results, obtained through trace-driven, large-scale simulations, show that our framework can provide a close-to-optimal solution while running several orders of magnitude faster than an ILP solver.

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.

Efficiently deploying microservices (MSs) is critical, especially in data centers at the edge of the network infrastructure where computing resources are precious. Unlike most of the existing approaches, we tackle this issue by accounting for the interference that arises when MSs compete for the same resources and degrades their performance. In particular, we first present some experiments highlighting the impact of interference on the throughput of co-located MSs. Then, we formulate an optimization problem that minimizes the number of used servers while meeting the MSs’ performance requirements. In light of the problem complexity, we design a low-complexity heuristic, called iPlace, that clusters together MSs competing for resources as diverse as possible and, hence, interfering as little as possible. Importantly, the choice of clustering MSs allows us to exploit the benefit of parallel MSs deployment, which, as shown by experimental evidence, greatly reduces the deployment time as compared to the sequential approach applied in prior art. Our numerical results show that iPlace closely matches the optimum and uses 10-63% fewer servers compared to alternative schemes, while proving to be highly scalable.

The combination of service virtualization and edge computing allows mobile users to enjoy low latency services, while keeping data storage and processing local. However, the network edge has limited resource availability, and when both virtualized user applications and network functions need to be supported concurrently, a natural conflict in resource usage arises. In this paper, we focus on computing and radio resources and develop a framework for resource orchestration at the edge that leverages a model-free reinforcement learning approach and a Pareto analysis, which is proved to make fair and efficient decisions. Through our testbed, we demonstrate the effectiveness of our solution in resource-limited scenarios, and show an improvement of around 60% in the CPU budget violation rate with respect to RL based standard multi-agent framework.

Given the plethora of sensors with which vehicles are equipped, today's automated vehicles already generate large amounts of data, and this is expected to increase in the case of autonomous vehicles, to enable data-driven solutions for vehicle control, safety and comfort, as well as to effectively implement convenience applications. It is expected that a crucial role in processing such data will be played by machine learning models, which, however, require substantial computing and energy resources for their training. In this paper, we address the use of cooperative learning solutions to train a Neural Network (NN) model while keeping data local to each vehicle involved in the training process. In particular, we focus on Federated Learning (FL) and explore how this cooperative learning scheme can be applied in an urban scenario where several cars, supported by a server located at the edge of the network, collaborate to train a NN model. To this end, we consider an LSTM model for trajectory prediction - a task that is an essential component of many safety and convenience vehicular applications, and investigate the performance of FL as the number of vehicles contributing to the learning process, and the data set they own, vary. To do so, we leverage realistic mobility traces of a large city and the FLOWER FL platform.

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.

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.

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.

Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies. However, with the purpose of achieving the well-known scalability, flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated.In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual network functions. Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements, and the number of robots consuming the service varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations. Moreover, the same analysis has been carried out as a function of the industrial processes, namely based on the commands imposed on the robots, and particularly of their abstraction-level, resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees. The derived results provide crucial insights for the design of network service scaling and resource orchestration frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate the minimum DT latency requirements.

A relevant fraction of human interactions occurs on online social networks. Freshness of content seems to play an important role, with content popularity rapidly vanishing over time. In this paper, we investigate how influencers' generated content (i.e., posts) attracts interactions, measured by number of likes or reactions. We analyse the activity of Italian influencers and followers over more than 5 years, focusing on two popular social networks: Facebook and Instagram, including more than 13 billion interactions and about 4 million posts. We characterise the influencers' and followers' behaviour over time, show that influencers' posts are short-lived with an exponential temporal decay, and characterise the time evolution of the interactions from their initial peak till the end of a post lifetime. Finally, leveraging our findings, we discuss how they can be exploited to develop an analytical model of the interactions temporal dynamics.

Trajectory prediction is crucial in assisting both human-driven and autonomous vehicles. Most of the existing approaches, however, focus on straight stretches of road and do not address trajectory prediction at intersections. This work aims to fill this gap by proposing a solution that copes with the higher complexity exhibited for the intersection scenario, leveraging the 5G-MEC capabilities. In particular, the reduced latency and edge computational power are exploited to centrally collect and process measurements from both vehicles (e.g., odometry) and road infrastructure (e.g., traffic light phases). Based on such a holistic system view, we develop a Long Short Term Memory (LSTM) recurrent neural network which, as shown through simulations using a real-world dataset, provides high-accuracy trajectory predictions. The encountered challenges and advantages of the presented approach are analyzed in detail, paving the way for a new vehicle trajectory prediction methodology.

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.

Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system.

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.

Empowered bynetwork softwarization, 5G systems have become the key enabler to foster the digital transformation of the vertical industries by expanding the scope of traditional mobile networks and enriching the network service offerings. To make this a reality, we propose anautomationsolution for vertical services provisioning and hierarchical Service Level Agreement (SLA) management.Service scalingis one of the most essential operations to adapt the service deployments and resource allocations to ensure SLA fulfilment. Three different scaling levels are addressed in this work: application-, service- and resource-level. We have implemented our solution in a proof-of-concept of a virtualized mobile network platform, spanning over three geographically-distributed sites. To evaluate our solution, we leverage field tests, focusing onautomotive vertical servicescomprising a mission-critical application (collision-avoidance) and an entertainment one (video streaming). The results demonstrate the excellent performance of our solution, and its ability to automatically deploy vertical services and ensure their SLAs through different levels of service scaling.

Full-duplex (FD)-enabled relay networks represent a relevant solution to two critical needs of next-generation networks, namely, radio coverage extension and high spectral efficiency of wireless communications. Under practical conditions, however, the FD mode may not be the best operational setting for the relay; rather, operating in half-duplex may be more convenient when harsh channel conditions add up to self-interference. One of the fundamental challenges in the design of FD relay networks is thus how to determine the relay operational mode and the value of transmit power at both the relay and the data source, so that the achievable data rate is maximized as time varies. We address this problem in a two-hop, MIMO network, accounting for practical operational conditions in which the source is unaware of the symbols that the relay is transmitting. In light of the problem complexity, we also derive a lower-bound to the maximum achievable rate, which proves to be tight, especially for low-medium SNR values. We then tackle massive MIMO networks, and exploit our asymptotic analysis in the number of antennas to derive a low-complexity, yet highly efficient, operational mode and transmit power allocation scheme for a finite-size scenario.

Several applications for connected cars leverage the mobility information periodically broadcasted by cars through standard vehicle-to-vehicle messages. We propose an architecture in which each car generates and sends reports including the messages received from its neighbors to the access network infrastructure. The infrastructure collects and stores the received reports through multiple blockchains, each of them referring to a different geographical area. A smart contract is then executed to verify the spatial coherence among the received data. We implement a proof-of-concept of such a solution using Hyperledger Fabric, and we investigate the scalability of our solution in terms of resource consumption in a MEC system.

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.

Due to their low complexity, Minimum Mean Squared Error (MMSE) and Zero-Forcing (ZF) emerge as two appealing MIMO receivers. Although they provide asymptotically the same achievable rate as the signal-to-noise ratio (SNR) grows large, a non-vanishing gap between the signal to interference and noise ratio (SINR) obtained through the two receivers exists, affecting the error and outage probability, and the multiuser efficiency. Interestingly, both the SINR and the multiuser efficiency gaps can be compactly expressed as quadratic forms of random matrices, with a kernel that depends solely on the statistics of the interfering streams. By leveraging, we derive the closed-form distribution of such indefinite quadratic forms with random kernel matrix, which turns out to be proportional to the determinant of a matrix containing the system parameters. Then, specializing our result to different fading conditions, we obtain the closed-form statistics of both the SINR gap and the multiuser efficiency gap. Although the focus of this work is on the finite-size statistics, for completeness we also provide some results on the doubly-massive MIMO case. We validate all our derivations through extensive Monte Carlo simulations.

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.

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the extended virtual sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.

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.

A 5G network provides several service types, tailored to specific needs such as high bandwidth or low latency. On top of these communication services, verticals are enabled to deploy their own vertical services. These vertical service instances compete for the resources of the underlying common infrastructure. We present a resource arbitration approach that allows to handle such resource conflicts on a high level and to provide guidance to lower-level orchestration components.

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.

The emergence of Internet of Things (IoT) applications and rapid advances in wireless communication technologies have motivated a paradigm shift in the development of viable applications such as mobile-health (m-health). These applications boost the opportunity for ubiquitous real-time monitoring using different data types such as electroencephalography (EEG), electrocardiography (ECG), etc. However, many remote monitoring applications require continuous sensing for different signals and vital signs, which result in generating large volumes of real time data that requires to be processed, recorded, and transmitted. Thus, designing efficient transceivers is crucial to reduce transmission delay and energy through leveraging data reduction techniques. In this context, we propose an efficient data-specific transceiver design that leverages the inherent characteristics of the generated data at the physical layer to reduce transmitted data size without significant overheads. The goal is to adaptively reduce the amount of data that needs to be transmitted in order to efficiently communicate and possibly store information, while maintaining the required application quality-of-service (QoS) requirements. Our results show the excellent performance of the proposed design in terms of data reduction gain, signal distortion, low complexity, and the advantages that it exhibits with respect to state-of-the-art techniques since we could obtain about 50% compression ratio at 0% distortion and sample error rate.

We consider a dual-hop, decode-and-forward network, where the relay can operate in full-duplex (FD) or half-duplex (HD) mode (X-duplex relay). We model the residual self-interference as an additive Gaussian noise with variance proportional to the relay transmit power, and we assume a Gaussian input distribution at the source. Unlike previous work, we assume that the source is only aware of the transmit power distribution adopted by the relay, but not of the symbols that the relay is currently transmitting. This assumption better reflects the practical situation, where the relay node forwards data traffic but modifies physical-layer or link-layer control information. We then identify the optimal power allocation strategy at the source and relay, which in some cases coincides with the HD transmission mode. We prove that such strategy implies either FD transmissions over an entire time frame or FD/HD transmissions over a variable fraction of the frame. We determine the optimal transmit power level at the source and relay for each frame, or fraction thereof. We compare the performance of our scheme against reference FD and HD techniques, which assume that the source is aware of the symbols instantaneously transmitted by the relay, and show that our solution closely approaches such strategies.

We present an information-theoretic analysis of a point-to-point multiple-input multiple-output (MIMO) link affected by Rayleigh fading and multiple scattering, under perfect channel state information at the receiver. Unlike previous work addressing this setting, we investigate the random coding error exponent, its associated cutoff rate and the expurgated error exponent, and derive closed-form expressions for them. Moreover, leveraging the average mutual information expression presented by Akemann et al., we derive another important metric, namely, the sum rate, under linear receive processing and independent stream decoding. In particular, we characterize the performance of the minimum mean squared error receiver in closed form, and that of the zero forcing receiver by resorting to bounding techniques. The bulk of the work relies on results about finite-dimensional random matrix products, a number of which are novel and detailed in the Appendices. The analysis, validated through numerical results, highlights the severe degradation in the performance of linear receivers due to multi-fold scattering. It also unveils the performance trend of multiple scattering MIMO channels as a function of the number of antennas and the number of scattering stages.

Online social networks offer the opportunity to collect a huge amount of valuable information about billions of users. The analysis of this data by service providers and unintended third parties are posing serious treats to user privacy. In particular, recent work has shown that users participating in more than one online social network can be identified based only on the structure of their links to other users. An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks. In this article, we show the crucial role of clustering, which is a relevant feature of social network graphs (and many other systems). Clustering has both the effect of making matching algorithms more prone to errors, and the potential to greatly reduce the number of seeds needed to trigger percolation. We show these facts by considering a fairly general class of random geometric graphs with variable clustering level. We assume that seeds can be identified in particular sub-regions of the network graph, while no a priori knowledge about the location of the other nodes is required. Under these conditions, we show how clever algorithms can achieve surprisingly good performance while limiting the number of matching errors.

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.

Users of device-to-device (D2D) communication need efficient content discovery mechanisms to steer their requests toward the node in their neighborhood that is most likely to satisfy them. The problem is further compounded by the lack of a central coordination entity as well as by the inherent mobility of devices, which leads to volatile topologies. In this paper, we first discuss group-based communication among non-rooted Android devices using Wi-Fi direct, a protocol recently standardized by the Wi-Fi alliance. We propose intra- and inter-group communication methodologies, which we validate through a simple testbed where content-centric routing is used. Next, we address the autonomous formation of groups with the goal of achieving efficient device resource utilization as well as full connectivity. Finally, we evaluate the performance of our group formation procedure both in simulation and in a real testbed involving Android devices in different topologies.

5G networks are expected to be highly energy efficient, with a 10 times lower consumption than today's systems. An effective way to achieve such a goal is to act on the backhaul network by controlling the nodes operational state and the allocation of traffic flows. To this end, in this paper we formulate energy-efficient flow routing on the backhaul network as an optimization problem. In light of its complexity, which impairs the solution in large-scale scenarios, we then propose a heuristic approach. Our scheme, named EMMA, aims to both turn off idle nodes and concentrate traffic on the smallest possible set of links, which in its turn increases the number of idle nodes. We implement EMMA on top of ONOS and derive experimental results by emulating the network through Mininet. Our results show that EMMA provides excellent energy saving performance, which closely approaches the optimum. In larger network scenarios, the gain in energy consumption that EMMA provides with respect to the simple benchmark where all nodes are active, is extremely high under medium-low traffic load.

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.

In this work, we consider a dual-hop, decode-and-forward network where the relay can operate in FD mode. We model the residual self interference as an additive Gaussian noise with variance proportional to the relay transmit power, and we assume a Gaussian input distribution at the source. Unlike previous work, however, we assume that the source is only aware of the transmit power distribution adopted by the relay over a given time horizon, not of the symbols that the relay is currently transmitting. This scenario better reflects practical situations in which the relay node may also have to forward signaling traffic, or data originated by other sources. Under these conditions, we show that the optimal communication strategy that source and relay can adopt is a time-division scheme, and, for each slot, we determine the optimal transmit power level that source and relay should adopt depending on the channel gains. Interestingly, the distribution of the optimal transmit power turns out to be discrete with two probability masses.

Deployment of Device-to-Device (D2D) communication within dense heterogeneous networks is a key solution in order to face the intense demand for high data rates and quality of the service in future 5G networks. However, this imposes challenges to develop innovative network selection mechanisms that account for both energy efficiency and user experience. In this paper, we propose a network association framework for heterogeneous systems where uplink data transfers can leverage direct device-to-infrastructure (D2I) links as well as underlay D2D connections. The proposed methodology is based on a distributed approach that optimizes the users' objectives, while accounting for the interference that underlaying D2D communication may cause, in order to enhance system performance and support reliable connectivity. Furthermore, to fully exploit the potential of D2D communication and prevent selfish behavior, a dynamic pricing strategy maximizing the profits of both source and relay nodes is proposed. Our results show that the proposed scheme provides significant performance gains and high efficiency compared to the centralized approach.

The growing trend of networks densification has motivated integrating the Device-to-Device (D2D) communication with the dense heterogeneous networks in order to face the intense demand of high data rates and enhance network performance. However, this imposes challenges to develop innovative network association schemes that consider energy efficiency while meeting application quality requirements. In this context, we propose an efficient network association mechanism over D2D-enabled heterogeneous wireless networks. We consider different Quality of service (QoS) requirements, networks characteristics, and application requirements, in order to obtain an efficient- distributed solution that grasps the conflicting nature of the various objectives. The proposed methodology leverages a user-centric networks association approach over D2D-enabled heterogeneous wireless networks to enhance system performance and support reliable connectivity. Our results demonstrate the efficiency of the proposed scheme compared to the state-of-the-art techniques that ignore the potential of D2D communication.

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.

Given the proven benefits cell densification brings in terms of capacity and coverage, it is certain that 5G networks will be even more heterogeneous and dense. However, as smaller cells are introduced in the network, interference will inevitably become a serious problem as they are expected to share the same radio resources. Another central feature envisioned for future cellular networks is carrier aggregation (CA), which allows users to simultaneously use several component carriers of various widths and frequency bands. By exploiting the diversity of the different carriers, CA can also be used to effectively mitigate the interference in the network. In this paper, we leverage the above key features of next-generation cellular networks and formulate a downlink power setting problem for the different available carriers. Using game theory, we design a distributed algorithm that lets cells dynamically adjust different transmit powers for the different carriers. The proposed solution greatly improves network performance by reducing interference and power consumption, while ensuring coverage for as many users as possible. We compare our scheme with other interference mitigation techniques, in a realistic large-scale scenario. Numerical results show that our solution outperforms the existing schemes in terms of user throughput, energy, and spectral efficiency.

The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real-time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques.

This paper presents a statistical characterization of the SNR gap between MIMO Zero-Forcing (ZF) and Minimum Mean Squared Error (MMSE) equalizers, beyond the Rayleigh assumption for the interfering streams amplitude fading. Results are valid for arbitrary transmit SNR values and number of transmit/receive antennas. Specifically, we provide the exact closed-form distribution of the random variable representing the difference between the output SNR on a generic receive filter branch, under MMSE and ZF equalization. Analytical results turn particularly useful for the study of heterogeneous cellular networks.

Efficient Multimedia Broadcast and Multicast Services (MBMS) to heterogeneous users in cellular networks imply adaptive video encoding, layered multimedia transmission, optimized transmission parameters, and dynamic broadcast area definition. This paper deals with MBMS by proposing a multi-dimensional approach for broadcast area definition, which provides an effective solution to all of the above aspects. By using multi-criteria K-means clustering, our scheme provides users with high levels of Quality-of-Experience (QoE) of multimedia services. Adaptive video encoding and allocation of radio resources (i.e., time-frequency resource blocks, and modulation and coding scheme) are performed based on user spatial distribution, channel conditions, service request, and user display capabilities. Simulation results show that our solution provides a 70% improvement in user QoE and 86% in number of served customers, as compared to an existing multimedia broadcast scheme.

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.

Rising numbers of mobile devices and wireless access technologies motivate network operators to leverage spectrum across multiple radio access networks, in order to significantly enhance quality of service as well as network capacity. However, there is a substantial need to develop innovative network selection mechanisms that consider energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient network selection mechanism over heterogeneous wireless networks. We consider different performance aspects, as well as network characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various objectives and addresses this ultimate tradeoff. The proposed methodology advocates a user-centric approach toward the utilization of heterogeneous wireless networks to enhance system performance and support reliable connectivity.

In cognitive radio networks, secondary users (SUs) utilize the unused spectrum slots in the assigned band for the primary users (PUs). Conventional cognitive radio networks operate in half-duplex (HD) mode. Recently, full-duplex (FD) communication has become feasible. SUs with full-duplex capabilities can sense the spectrum and transmit simultaneously, which improves the efficiency of cognitive radio networks. In this paper, we study the performance of spectrum sensing based on general likelihood ratio (GLR) when the SU is operating in FD mode. We compare our results to the HD GLR case. We present the effect of residual self interference on the performance of the spectrum sensing technique. Moreover, we consider uncertainty in estimating the variance of the combined residual self interference and noise and show its effect on the performance of the FD GLR.

Carrier aggregation, which allows users to aggregate several component carriers to obtain up to 100 MHz of bandwidth, is one of the central features envisioned for next generation cellular networks. While this feature will enable support for higher data rates and improve quality of service, it may also be employed as an effective interference mitigation technique, especially in multi-tier heterogeneous networks. Having in mind that the aggregated component carriers may belong to different frequency bands and, hence, have varying propagation profiles, we argue that it is not necessary, indeed even harmful, to transmit at maximum power at all carriers, at all times. Rather, by using game theory, we design a distributed algorithm that lets eNodeBs and micro base stations dynamically adjust the downlink transmit power for the different component carriers. We compare our scheme to different power strategies combined with popular interference mitigation techniques, in a typical large-scale scenario, and show that our solution significantly outperforms the other strategies in terms of global network utility, power consumption and user throughput.

Simultaneous wireless information and power transfer (SWIPT) can lead to uninterrupted network operation by integrating radio frequency (RF) energy harvesting with data communication. In this paper, we consider a two-hop source-relay-destination network and investigate the efficient usage of a decode-and-forward (DF) relay for SWIPT toward the energy-constrained destination. In particular, by assuming a Rician fading environment, we jointly optimize power allocation (PA), relay placement (RP), and power splitting (PS) so as to minimize outage probability under the harvested power constraint at the destination node. We consider the two possible cases of source-to-destination distance: (1) small distance with direct information transfer link; and (2) relatively large distance with no direct reachability. Analytical expressions for individual and joint optimal PA, RP, and PS are obtained by exploiting convexity of outage minimization problem for the no direct link case. In case of direct source-to-destination link, multipseudoconvexity of joint-optimal PA, RP, and PS problem is proved, and alternating optimization is used to find the global optimal solution. Numerical results show that the joint optimal solutions, although strongly influenced by the harvested power requirement at the destination, can provide respectively 64% and 100% outage improvement over the fixed allocation scheme for without and with direct link.

The dramatic increase in the number and sensing capabilities of mobile devices is fostering opportunistic sensing as a paramount data collection paradigm in smart cities. According to this paradigm, sensing of large-scale phenomena is autonomously performed by mobile devices that provide irregular samples in time and space. The collected data is then transferred to a central controller, and processed so as to obtain a representation of the phenomenon. In this paper, we investigate the factors that impact the accuracy of mobile opportunistic sensing. Specifically, we characterize the accuracy of a phenomenon representation obtained from samples collected by mobile devices and processed through the popular LMMSE filter. We do so by drawing on random matrix theory, which allows us to deal with irregularly spaced samples. Our analytical expressions capture the fundamental relationships existing between the accuracy and the parameters of mobile opportunistic sensing. We apply our analytical results to a realistic scenario where atmospheric pollution samples are collected by vehicular and pedestrian users. We validate the proposed analytical framework, and then exploit the model to investigate the impact on mobile sensing accuracy of a number of parameters. These include the pedestrian and vehicle density, the participation ratio to the sensing application, the type of phenomenon to be sensed, and the level of noise and position errors affecting the collected samples.

We tackle the problem of user de-anonymization in social networks characterized by scale-free relationships between users. The network is modeled as a graph capturing the impact of power-law node degree distribution, which is a fundamental and quite common feature of social networks. Using this model, we present a de-anonymization algorithm that exploits an initial set of users, called seeds, that are known a priori. By employing the bootstrap percolation theory and a novel graph slicing technique, we develop a rigorous analysis of the proposed algorithm under asymptotic conditions. Our analysis shows that large inhomogeneities in the node degree lead to a dramatic reduction in the size of the seed set that is necessary to successfully identify all the other users. We characterize this set size when seeds are properly selected based on the node degree as well as when seeds are uniformly distributed. We prove that, given n nodes, the number of seeds required for network de-anonymization can be as small as n∈, for any small ∈ > 0. In addition, we discuss the complexity of our de-anonymization algorithm and validate our results through numerical experiments on a real social network graph.

Abstract The vast majority of existing secret key generation protocols exploit the inherent randomness of the wireless channel as a common source of randomness. However, independent noise added at the receivers of the legitimate nodes affects the reciprocity of the channel. In this paper, we propose a new simple technique to generate the secret key that mitigates the effect of noise. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We study the properties of our generated SRP and derive a closed form expression for the probability mass function of the realizations of our SRP. We simulate an orthogonal frequency division multiplexing system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate between the legitimate nodes when compared with the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques. Moreover, we compute the conditional probabilities used to estimate the secret key capacity. Copyright © 2016 John Wiley & Sons, Ltd.

Optimal resource allocation in LTE networks is known to be a hard problem, and is further exacerbated when support for advanced features such as heterogeneity and carrier aggregation are also considered. In particular, in LTE heterogeneous networks (HetNets) where radio resources are shared between different layers of base stations, interference management can be a daunting task. Carrier aggregation (CA), which allows the simultaneous use of several LTE component carriers to achieve high user data rates, also adds to the complexity. In this paper, we propose an interference-aware heuristic algorithm that jointly performs carrier selection and resource allocation to serve a mix of users with CA-enabled and legacy terminals. We evaluate the performance of our approach in a large-scale scenario and compare it with other widely used heuristic algorithms such as Proportional-Fair scheduling and Enhanced Inter Cell Interference Coordination (eICIC) techniques. Simulation results show that the solution we propose increases system throughput, minimises energy consumption and improves spectrum utilisation, while also ensuring better fairness between CA-enabled and legacy user terminals.

We address the problem of social network de-anonymization when relationships between people are described by scale-free graphs. In particular, we propose a rigorous, asymptotic mathematical analysis of the network de-anonymization problem while capturing the impact of power-law node degree distribution, which is a fundamental and quite ubiquitous feature of many complex systems such as social networks. By applying bootstrap percolation and a novel graph slicing technique, we prove that large inhomogeneities in the node degree lead to a dramatic reduction of the initial set of nodes that must be known a priori (the seeds) in order to successfully identify all other users. We characterize the size of this set when seeds are selected using different criteria, and we show that their number can be as small as n% for any small ε > 0. Our results are validated through simulation experiments on real social network graphs.

The broadcast nature of wireless communications imposes the risk of information leakage to adversarial users or unauthorized receivers. Therefore, information security between intended users remains a challenging issue. Most of the current physical layer security techniques exploit channel randomness as a common source between two legitimate nodes to extract a secret key. In this paper, we propose a new simple technique to generate the secret key. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We simulate an orthogonal frequency division multiplexing (OFDM) system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate (BMR) between the legitimate nodes when compared to the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques.

Motivated by safety challenges resulting from distracted pedestrians, this paper presents a sensing technology for fine-grained location classification in an urban environment. It seeks to detect the transitions from sidewalk locations to in-street locations, to enable applications such as alerting texting pedestrians when they step into the street. In this work, we use shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. This approach is different from existing shoe sensing solutions that focus on dead reckoning and inertial navigation. The shoe sensors relay inertial sensor measurements to a smartphone, which extracts the step pattern and the inclination of the ground a pedestrian is walking on. This allows detecting transitions such as stepping over a curb or walking down sidewalk ramps that lead into the street. We carried out walking trials in metropolitan environments in United States (Manhattan) and Europe (Turin). The results from these experiments show that we can accurately determine transitions between sidewalk and street locations to identify pedestrian risk.

This video is a demonstration of the work discussed in our full paper available in the MobiSys'15 proceedings. The video illustrates a sensing technology for fine-grained location classification in an urban environment, for enhancing pedestrian safety. Our system seeks to detect the transitions from sidewalk locations to in-street locations, to enable applications such as alerting texting pedestrians when they step into the street. Existing positioning technologies are not sufficiently precise to allow distinguishing a position on the sidewalk from a position in the street, as explored in our previous work. To this end, we use shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. This approach is different from existing shoe sensing solutions that focus on dead reckoning and inertial navigation. The shoe sensors relay inertial sensor measurements to a smartphone, which extracts the step pattern and the inclination of the ground a pedestrian is walking on. This allows detecting transitions such as stepping over a curb or walking down sidewalk ramps that lead into the street. We carried out walking trials in metropolitan environments in United States (Manhattan) and Europe (Turin). The results from these experiments show that we can accurately determine transitions between sidewalk and street locations to identify pedestrian risk.

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.

In the context of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. Therefore an accurate estimation of this characteristic is the core for reliable secret key generation. Estimation of almost all the existing physical layer characteristic suffer dramatically at low signal to noise (SNR) levels. In this paper, we propose a novel secret key generation algorithm that is based on the estimated angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an outstanding performance at very low SNR levels. Our algorithm can exploit either the Azimuth AoA to generate the secret key or both the Azimuth and Elevation angles to generate the secret key. Exploiting a second common source of randomness adds an extra degree of freedom to the performance of our algorithm. We compare the performance of our algorithm to the algorithm that uses the most commonly used characteristics of the physical layer which are channel amplitude and phase. We show that our algorithm has a very low bit mismatch rate (BMR) at very low SNR when both channel amplitude and phase based algorithm fail to achieve an acceptable BMR.

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.

The added value of Device-to-Device (D2D) communication amounts to an efficient content discovery mechanism that enables users to steer their requests toward the node most likely to satisfy them. In this paper, we address the implementation of content-centric routing in a D2D architecture for Android devices based on WiFi Direct, a protocol recently standardised by the Wi-Fi Alliance. After discussing the creation of multiple D2D groups, we introduce novel paradigms featuring intra- and inter-group bidirectional communication. We then present the primitives involved in content advertising and requesting among members of the multi-group network. Finally, we evaluate the performance of our architecture in a real testbed involving Android devices in different group configurations. We also compare the results against the ones achievable exploiting Bluetooth technologies.

In our companion WoWMoM 2015 paper [1], we propose a content-centric routing mechanism for Wi-Fi Direct networks. In that paper, we showed how to implement a Wi-Fi Direct multi-group network with unrooted Android devices that supports bidirectional communication between different groups. We devised on it a content-centric application, denoted as Multi-Group Content (MGC), that enables content request and delivery in a distributed and cooperative way. Finally, we demonstrated our application with Android devices to fully validate our approach and assess its performance. Aim of the current demonstration is to allow demo session attendees to experiment MSG and assess its behavior and performance in real time.

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.

The proliferation of overlapping, always-on IEEE 802.11 access points (APs) in urban areas, can cause inefficient bandwidth usage and energy waste. Cooperation among APs could address these problems by allowing underused devices to hand over their wireless stations to nearby APs and temporarily switch off, while avoiding to overload a BSS and thus offloading congested APs. The federated house model provides an appealing backdrop to implement cooperation among APs. In this paper, we outline a distributed framework that assumes the presence of a multipurpose gateway with AP capabilities in every household. Our framework allows cooperation through the monitoring of local wireless resources and the triggering of offloading requests toward other federated gateways. Our simulation results show that, in realistic residential settings, the proposed framework yields an energy saving between 45 and 86 percent under typical usage patterns, while avoiding congestion and meeting user expectations in terms of throughput. Furthermore, we show the feasibility and the benefits of our framework with a real test-bed deployed on commodity hardware.

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.

This paper presents an energy efficient relay deployment algorithm that determines the optimal location and number of relays for future wireless networks, including Long Term Evolution (LTE)-Advanced heterogeneous networks. We formulate an energy minimization problem for macro-relay heterogeneous networks as a Mixed Integer Linear Programming (MILP) problem. The proposed algorithm not only optimally connects users to either relays or eNodeBs (eNBs), but also allows eNBs to switch into inactive mode. This is possible by enabling relay-to-relay communication which forms the basis for relays to act as donors for neighboring relays instead of eNBs. Moreover, it relaxes traffic load of some eNBs in order to allow them to enter the inactive mode. We characterize the optimal as well as provide an approximate solution, which, however, performs very closely to the optimum. Our performance evaluation shows that an optimal relay deployment with relays acting as donors can significantly improve system energy efficiency.

The information that can be transmitted through a wireless channel, with multiple-antenna-equipped transmitter and receiver, is crucially influenced by the channel behavior as well as by the structure of the input signal. We characterize in closed form, the probability density function (pdf) of the output of multiple-input and multiple-output block-fading channels, for an arbitrary signal-to-noise ratio value. Our results provide compact expressions for such output statistics, paving the way to a more detailed analytical information-theoretic exploration of communications in presence of block fading. The analysis is carried out assuming two different structures for the input signal: 1) the independent identically distributed (i.i.d.) Gaussian distribution and 2) a product form that has been proved to be optimal for noncoherent communication, i.e., in absence of any channel state information. When the channel is fed by an i.i.d. Gaussian input, we assume the Gramian of the channel matrix to be unitarily invariant and derive the output statistics in both the noise- and interference-limited scenario, considering different fading distributions. When the product-form input is adopted, we provide the expressions of the output pdf as the relationship between the overall number of antennas and fading coherence length varies. We also highlight the relation between our newly derived expressions and the results already available in the literature, and, for some cases, we numerically compute the mutual information based on the proposed expression of the output statistics.

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.

We analyze the achievable data rate of cooperative relaying strategies in networks where nodes operate in half-duplex mode. Nodes have to deliver their data to a gateway, at a certain rate, and may have limited energy capabilities, as in the case of energy-harvesting communication networks. Both the requested data rate and the available energy capabilities may vary from node to node. Under such constraints, we take an information-theoretic approach and derive cut-set upper bounds to the achievable rate. Furthermore, we devise two kinds of communication strategies, each aiming at a different objective. The former ensures a fair rate allocation to the network nodes, but it neglects their energy constraints. The latter does consider energy constraints by meeting the requirements on the average power consumption at each node and by providing fairness in the data rate allocation. We show the performance of the aforementioned communication strategies, highlighting their effectiveness and providing useful insights on the system behavior.

We compute the optimal communication rate achieved by the nodes of a wireless multihop network with arbitrary topology. The network nodes operate in half-duplex mode and generate information that has to be delivered to a gateway node, possibly through a decode-and-forward relaying strategy. Nodes may make use of multiuser processing, thus transmissions from multiple nodes toward the same receiver are allowed. Additionally, nodes may be energy-constrained, as in the case of battery-powered or energy-harvesting communication networks. In such a scenario, we define the possible (and meaningful) network operational states. Then, by solving a linear optimization problem, we select the most efficient network states and for how long the network should work in each of them. More specifically, the resulting communication strategy maximizes the data rate achievable by the network while meeting the constraints that may exist on the node energy consumption. The results we present show how our approach can be effectively used for an optimal design and usage of wireless networks, as well as under which conditions multiuser processing and long-distance communication between nodes are most beneficial.

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.

The information that can be conveyed through a wireless channel, with multiple-antenna equipped transmitter and receiver, crucially depends on the channel behavior as well as on the input structure. In this paper, we derive analytical results, concerning the probability density function (pdf) of the output of a single-user, multiple-antenna communication. The analysis is carried out under the assumption of an optimized input structure, and assuming Gaussian noise and a Rayleigh block-fading channel. Our analysis therefore provides a quite general and compact expression for the conditional output pdf. We also highlight the relation between such an expression and the results already available in the literature for some specific input structures.

Efficient schemes for warning message dissemination in vehicular ad hoc networks (VANETs) use context information collected by vehicles about their neighbor nodes to guide the dissemination process. These schemes maximize their performance when all the vehicles advertise correct information about their positions, and hence position errors may drastically reduce the performance of the dissemination process. We present a proactive Cooperative Neighbor Position and Verification (CNPV) protocol that detects nodes advertising false locations so as to mitigate the impact of adversarial users. We combine our mechanism with two warning dissemination schemes for VANETs, and demonstrate how these algorithms can benefit from the use of our security scheme in the presence of malicious nodes trying to exploit the inherent vulnerabilities of each algorithm.

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.

Cooperation among federated APs in dense urban areas can yield energy saving by allowing under-used devices to hand over their wireless stations (WS) to nearby APs and temporarily switch off while meeting user expectations in terms of throughput. We demonstrate the effectiveness and the benefits of our energy-efficient cooperative protocol through a real deployment emulating a residential scenario. The demo we propose is highly interactive, as users can generate traffic within a BSS through a wireless station, like a smartphone or a notebook, and observe, through a web interface, the protocol behavior and the network topology changes caused by the new traffic scenario.

We present a communication protocol, called LINGER, for persistent dissemination of delay-tolerant information to vehicular users, within a geographical area of interest. The goal of LINGER is to dispatch and confine information in localized areas of a mobile network with minimal protocol overhead and without requiring knowledge of the vehicles' routes or destinations. LINGER does not require roadside infrastructure support: it selects mobile nodes in a distributed, cooperative way and lets them act as "information bearers", providing uninterrupted information availability within a desired region. We analyze the performance of our dissemination mechanism through extensive simulations, in complex vehicular scenarios with realistic node mobility. The results demonstrate that LINGER represents a viable, appealing alternative to infrastructure-based solutions, as it can successfully drive the information toward a region of interest from a far away source and keep it local with negligible overhead. We show the effectiveness of such an approach in the support of localized broadcasting, in terms of both percentage of informed vehicles and information delivery delay, and we compare its performance to that of a dedicated, state-of-the-art protocol.

A growing number of ad hoc networking protocols and location-aware services require that mobile nodes learn the position of their neighbors. However, such a process can be easily abused or disrupted by adversarial nodes. In absence of a priori trusted nodes, the discovery and verification of neighbor positions presents challenges that have been scarcely investigated in the literature. In this paper, we address this open issue by proposing a fully distributed cooperative solution that is robust against independent and colluding adversaries, and can be impaired only by an overwhelming presence of adversaries. Results show that our protocol can thwart more than 99 percent of the attacks under the best possible conditions for the adversaries, with minimal false positive rates.

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.

Wireless networks with linear topology, where nodes generate their own traffic and relay other nodes' traffic, have attracted increasing attention. Indeed, they well represent sensor networks monitoring paths or streets, as well as multihop networks for videosurveillance of roads or vehicular traffic. We study the performance limits of such network systems when (i) the nodes' transmissions can reach receivers farther than one-hop distance from the sender, (ii) the transmitters cooperate in the data delivery, and (iii) interference due to concurrent transmissions is taken into account. By adopting an information-theoretic approach, we derive analytical bounds to the achievable data rate in both the cases where the nodes have full-duplex and half-duplex radios. The expressions we provide are mathematically tractable and allow the analysis of multihop networks with a large number of nodes. Our analysis highlights that increasing the number of cooperating transmitters beyond two leads to a very limited gain in the achievable data rate. Also, for half-duplex radios, it indicates the existence of dominant network states, which have a major influence on the bound. It follows that efficient, yet simple, communication strategies can be designed by considering at most two cooperating transmitters and by letting half-duplex nodes operate according to the aforementioned dominant states.

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.

We analyze the achievable data rate of relaying strategies in a wireless network where nodes are equipped with half-duplex radios. The nodes need to deliver their data to a destination node, possibly at different rates, through multi-hop communications. In such a scenario, we first derive a cut-set upper bound to the achievable rate(s), then we take into account the interference among simultaneous transmissions and devise a communication strategy. Such a strategy ensures a high rate allocation to the nodes, which is also fair, i.e., it provides the desired proportion among the average data rates of the nodes. Our results show that the proposed strategy closely approximates the bound for any value of SNR.

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.

The proliferation of overlapping, always-on IEEE 802.11 Access Points (APs) in urban areas can cause inefficient bandwidth usage and energy waste. Cooperation among federated APs could address these problems (i) by allowing under-used devices to hand over their wireless stations to nearby APs and temporarily switch off, (ii) by balancing the load of stations among APs and thus offloading congested APs. We outline a framework that allows such cooperation, yielding a 60% energy saving in realistic residential settings, while providing load balancing and meeting the user expectations in terms of throughput.

Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. In this work, we design a practical, distributed solution to content replication that is suitable for dynamic environments and achieves load balancing. Simulation results show that our mechanism, which uses local measurements only, approximates well an optimal solution while being robust against network and demand dynamics. Also, our scheme outperforms alternative approaches in terms of both content access delay and access congestion.

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.

We present a lightweight algorithm for the estimation of the node achievable throughput and available bandwidth in IEEE 802.11 wireless networks. We consider a multirate WLAN with access point (AP), where there may be both elastic and inelastic traffic flows. Through our algorithm and leveraging previous theoretical results, the AP can estimate: (i) the available bandwidth that a new station wishing to associate with the AP can use, (ii) the impact on the system performance of admitting the new station, (iii) the bandwidth still available (if any) for inelastic traffic. The above quantities can be effectively used for admission control in WLANs and load balancing among APs with overlapping coverages. Indeed, simulation results show that the estimates yielded by our algorithm accurately reflect the system throughput behavior when there are both elastic and inelastic flows, in the uplink and downlink directions.

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.

Increasing the performance of wireless mesh networks is an important topic, which has been addressed in both theory and practice. Indeed, the availability of low-cost hardware justifies hands-on investigations and comparisons of protocols used in mesh networks deployments. In this work, we describe a real-life testbed that we have developed in order to investigate traffic routing in multihop, multiradio meshed networks with mobile users. We consider an environment where a user connects to different infrastructure mesh nodes as it moves, and we compare the performance of state-of-the-art routing protocols, namely, OLSR, OLSR-ETX, B.A.T.M.A.N and our modified version of this protocol, called sw-B.A.T.M.A.N. Our experimental measurements show that sw-B.A.T.M.A.N outperforms the other protocols, ensuring a seamless connectivity in presence of MPEG video streaming, with and without background traffic.

We propose a fully-distributed approach to the on line estimation of vehicle traffic density. Our approach envisions vehicles communicating within a VANET and cooperating to collect density measurements through a uniform sampling of the road sections of interest. The proposed scheme does not require the presence of any network infrastructure, central controller or devices triggered by the passage of vehicles, and it is suitable for both highway and urban environments. Results derived through ns-2 simulations in realistic mobility scenarios show that our solution is very effective, providing accurate, on-line estimates of the traffic density with minimal protocol overhead.

The provision of UDP-based multimedia streams to vehicular users through a roadside wireless mesh network requires a fast-switching, robust protocol architecture. We consider vehicles (e.g., cars, buses or streetcars) that connect to different roadside mesh nodes as they move in an urban environment, and study the joint problem of traffic delivery and connectivity management in such scenario. We identify BATMAN as a candidate layer-2 implementation of a routing protocol for vehicular networks, and we use simulation to compare its performance with other routing protocols for wireless ad hoc and mesh networks. Since BATMAN shows some inconsistencies in its behavior, we propose an improved version of the protocol, named smart-window BATMAN (sw-BATMAN). Then, we design two testbeds that include both roadside and vehicular mesh nodes. There, we implement the selected routing solution along with a handover mechanism that, by leveraging a channel selection scheme, allows vehicles to connect to the different roadside mesh nodes in a seamless manner. The performance assessment on our testbeds shows the efficiency of the proposed solution and highlights that our traffic routing and connectivity management are suitable for sustaining the handover of UDP streams in a vehicular environment, in a seamless manner.

We analyze the capacity scaling laws of clustered ad hoc networks comprising significant inhomogeneities in the node spatial distribution over the area. In particular, we consider the class of networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process, which allows to model a wide variety of inhomogeneous topologies. For this class of networks, we derive information theoretic upper-bounds to the capacity, identifying six operational regions. We also provide constructive lower bounds by devising, for each region, an optimal communication strategy to achieve the maximum network throughput. The performance of our communication schemes match, in terms of scaling exponent, the theoretical upper-bounds.

Streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. A problem, scarcely addressed so far, is how to distribute video streaming traffic from one source to all nodes in an urban vehicular network. This problem significantly differs from previous work on broadcast and multicast in ad hoc networks because of the highly dynamic topology of vehicular networks and the strict delay requirements of streaming applications. We present a solution for intervehicular communications, called Streaming Urban Video (SUV), that 1) is fully distributed and dynamically adapts to topology changes, and 2) leverages the characteristics of streaming applications to yield a highly efficient, cross-layer solution.

We study the delay performance of a sensor network, whose nodes access the medium by using the unslotted MAC protocol specified by the IEEE 802.15.4 standard. Unlike previous works, which focus on the average throughput and delay analysis, we develop a detailed model that allows us to obtain the delivery delay distribution of messages sent by concurrently contending sensors toward a central controller. We carry out a transient analysis that is of particular interest when sensor networks are deployed to provide k-coverage for real-time applications, and we study both single- and multi-hop network topologies. We validate our analytical results against simulation results obtained through ns2.

We analyze the capacity scaling laws of clustered ad hoc networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process. We identify five different operational regimes, and for each regime we devise a communication strategy that allows to achieve a throughput featuring the same scaling exponent as the maximum theoretical capacity.

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.

Performance and reliability of content access in mobile networks is conditioned jointly by the number and location of content replicas deployed at the network nodes. The endeavour of this work is to address such an optimization problem with a distributed, lightweight solution that handles network dynamics. We devise a mechanism that lets nodes share the burden of storing and providing content, so as to achieve load balancing, and decide whether to replicate or drop the information so as to adapt to a dynamic content demand and time-varying topology. Simulation results show that our mechanism, which uses local measurements only, is: (i) extremely precise in approximating an optimal solution to content placement and replication; (ii) robust against network mobility; (iii) flexible in accommodating variation in time and space of the content demand.

A wireless sensor network with randomly deployed nodes can be used to provide an irregular sampling of a physical field of interest. We assume that a sink node collects the data gathered by the sensors and uses a linear filter for the reconstruction of a bandlimited scalar field defined over a d -dimensional domain. Sensors' locations are assumed to be known at the sink node, up to a certain position error. We then take the mean square error (MSE) of the reconstructed field as performance metric, and evaluate the effect of both uniform and quasi-equally spaced sensor layouts on the quality of the reconstructed field. We define a parameter that provides a measure of the regularity of the sensors deployment, and, through asymptotic analysis, we derive the MSE in the case of different sensor spatial distributions. For two of them, an approximate closed form expression is obtained. We validate our analysis through numerical results, and we show that an excellent match exists between analysis and simulation even for a small number of sensors.

We study the delay performance of a sensor network, whose nodes access the medium by using the unslotted MAC protocol specified by the IEEE 802.15.4 standard. Unlike previous works which focus on the average throughput and delay analysis, we develop a detailed model that allows us to obtain the delivery delay distribution of messages sent by concurrently contending sensors toward a central controller. We carry out a transient analysis, which is of particular interest when sensor networks are deployed to provide k-coverage for real-time applications, and validate our analytical results against simulation results obtained through ns2.

We study the performance of signal estimation and reconstruction systems, that exploit the linear minimum mean square error (LMMSE) technique. This model often occurs in signal processing and wireless communications; some examples are radar applications, MIMO communications, or sensor networks sampling a physical field. Our performance analysis implies the characterization of a random matrix product, involving a multifold Vandermonde matrix with complex exponential entries. We therefore derive the LMMSE by computing the eta-transform of this matrix product, which can be evaluated either by implicit as well as by explicit expression, using the matrix asymptotic moments. Finally, we show how our results can be applied in some cases of practical interest.

We address cooperative caching in mobile ad hoc networks where information is exchanged in a peer-to-peer fashion among the network nodes. Our objective is to devise a fully-distributed caching strategy whereby nodes, independently of each other, decide whether to cache or not some content, and for how long. Each node takes this decision according to its perception of what nearby users may be storing in their caches and with the aim to differentiate its own cache content from the others'. We aptly named such algorithm "Hamlet". The result is the creation of a content diversity within the nodes neighborhood, so that a requesting user likely finds the desired information nearby. We simulate our caching algorithm in an urban scenario, featuring vehicular mobility, as well as in a mall scenario with pedestrians carrying mobile devices. Comparison with other caching schemes under different forwarding strategies confirms that Hamlet succeeds in creating the desired content diversity thus leading to a resource-efficient information access.

We investigate the problem of spreading information contents in a wireless ad hoc network. In our vision, information dissemination should satisfy the following requirements: (i) it should result in a desirable distribution of information replicas in the network and (ii) the information should be evenly and fairly carried by all nodes in their turn. In this paper, we show that these goals can be achieved by simple cache-and-forward mechanisms inspired by well-known node mobility models, provided that a sufficient number of information replicas are injected into the network. The proposed approach works under different network scenarios, is fully distributed and comes at a very low cost in terms of protocol overhead.

We propose a localized channel assignment scheme called LOCA for multi-channel multi-radio (MCMR) wireless mesh networks. The scheme combines the advantage of using multiple channels with random assignment, typical of the dynamic/hybrid approach, with the advantage of using all node interfaces for both transmission and reception, as done in static assignment. Since optimal channel assignment in MCMR networks is an NP-hard problem, we resort to a heuristic, which uses only localized (single-hop) information to perform channel assignment. Also, we consider that the BATMAN routing protocol is implemented in the mesh network and we exploit the local information that nodes can collect through BATMAN to implement our strategy. Simulation results obtained through ns2 show that the proposed scheme ensures a high network connectivity level and that a low number of reassignment procedures is needed to adapt the channel usage to the changes in the network topology or in the interference level. Furthermore, when compared against a static approach, LOCA provides significantly better performance in terms of throughput and packet delivery ratio, for networks with low-medium node density.

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).

We propose a framework, called LINGER, for the support of cooperative creation and distribution of information contents in vehicular networks. The goal of LINGER is to dispatch and confine information in localized areas of a mobile network with no infrastructure availability and minimal protocol overhead. LINGER selects mobile nodes in a distributed, cooperative way and lets them act as "information bearers", ensuring uninterrupted information availability to as many nodes as possible in a desired region. Simulation results in vehicular scenarios with realistic node mobility prove that LINGER successfully drives information toward a target area from a far away source and keeps it local with negligible overhead. Further tests with a beaconing application leveraging the LINGER framework show that a mobile information bearer may be as reliable as an infrastructure-based access point in providing service to users. Finally, in a large-scale scenario, LINGER is proven to be effective for delay-tolerant broadcast applications.

In this work we define and study a new model for the caching problem in a heterogeneous wireless network under a flash-crowd scenario. Using non-cooperative game theory, we cast the caching problem as an anti-coordination game. We start by defining the social optimum in the general case and then focus on a two-player game to obtain insights into the design of efficient caching strategies. Based the theoretical findings, our current work focuses on the development of strategies to be implemented in a practical network setting.

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.

We describe an analytical model, based on a Markov chain, suitable to study different bandwidth allocation policies for video streams over a WiMax access link. The Markov chain models an MPEG source, wireless channel conditions derived from a model compliant with WiMax specifications, and different bandwidth allocation policies. Validation with simulation results shows the correctness of the analytical model. The model permits to discuss the properties of various bandwidth allocation policies in terms of wasted slots, amount of lost data and access delays.

We consider that a given number of dissemination points (DPs) have to be deployed for disseminating information to vehicles travelling in an urban area. We formulate our problem as a maximum coverage problem (MCP) so as to maximize the number of vehicles that get in contact with the DPs and as a second step with a sufficient amount of time. Since the MCP is NP-hard, we solve it though heuristic algorithms. Evaluation of the proposed solutions in a realistic urban environment shows how knowledge of vehicular mobility plays a major role in achieving an optimal coverage of mobile users, and that simple heuristics provide near-optimal results even in large-scale scenarios.

The authors focus on a wireless mesh network, that is, an ad hoc IEEE 802.11-based network whose nodes are either user devices or Access Points providing access to the mesh network or to the Internet. By relying on some work done within the IEEE 802.11s TG, the network nodes can use one control channel and one or more data channels, each on separate frequencies. Then, some problems related to channel access are identified and a MAC scheme is proposed that specifically addresses the problem of hidden terminals and the problem of coexisting control and data traffic on different frequency channels. An analytical model of the MAC scheme is presented and validated by using the Omnet++ simulator. Through the developed model, we show that our solution achieves very good performance both in regular and in very fragmented mesh topologies, and it significantly outperforms the standard 802.11 solution.

A fundamental issue arising in mobile ad hoc networks (MANETs) is the selection of the optimal path between any two nodes. A method that has been advocated to improve routing efficiency is to select the most stable path so as to reduce the latency and the overhead due to route reconstruction. In this work, we study both the availability and the duration probability of a routing path that is subject to link failures caused by node mobility. In particular, we focus on the case where the network nodes move according to the Random Direction model, and we derive both exact and approximate (but simple) expressions of these probabilities. Through our results, we study the problem of selecting an optimal route in terms of path availability. Finally, we propose an approach to improve the efficiency of reactive routing protocols.

The paper focuses on a cooperative environment in wireless ad hoc networks, where mobile nodes share information in a peer-to-peer fashion. Nodes follow a pure peer-to-peer approach (i.e., without the intervention of servers), thus requiring an efficient query/response propagation algorithm to prevent network congestion. The main contribution of the paper is the proposal of a novel solution, called Eureka, that identifies the regions of the network where the required information is more likely to be stored and steers the queries toward those regions. To discriminate among regions, the concept of information density is introduced, along with a procedure that allows nodes its estimation. Eureka does not require the use of satellite positioning systems, and proves to be very effective in both vehicular and pedestrian environments.

Streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. A problem, scarcely addressed so far, is how to support streaming traffic in vehicular networks (VANETs). This problem significantly differs from previous work on broadcast and multicast in ad hoc networks, because of the highly-dynamic topology of VANETs and the strict delay requirements of streaming applications. Our solution, completely relies on inter-vehicular communication for the distribution of multimedia content in a urban environment and it has the following appealing features: (i) it is fully distributed and dynamically adapts to topology changes; (ii) it leverages the characteristics of streaming applications to yield a highly-efficient, cross-layer solution. After optimally dimensioning the network system, we compare the performance of our solution against theoretical results for broadcast capacity in multihop networks.

We consider a wireless sensor network monitoring a field of interest, and we study the benefits of grouping nodes into clusters. The data gathered within each cluster are compressed by the cluster head and sent to a sink node, where a reconstructed version of the field is obtained. We represent the compressed data through the Fourier coefficients of the field spectrum, and analyze both the case where the sensor positions are known to the sink, and the case where they are available at the cluster head only. We show that clustering significantly reduces the energy expenditure due to data transmission, and, most importantly, we derive the possible degradation of the quality of the reconstructed field due to compression.

In this paper we study wireless sensor networks for monitoring applications. We focus on the problem of sampling and reconstruction of multidimensional bandlimited signals, when the sensor locations are equally spaced points affected by some jitter, and the sensor measurements are affected by noise. We show how the mean square reconstruction error can be estimated from the eigenvalue distribution of a certain Toeplitz matrix. We analyze the d-dimensional case, and we show how the mean square error can be easily estimated by using asymptotic analysis.

We consider a cooperative environment in wireless mobile networks where information is exchanged among nodes in a peer-to-peer fashion. We apply a pure peer-to-peer approach (i.e., without the intervention of servers) and we seek to devise an efficient query/response propagation algorithm. Our approach, calledEureka, identifies the regions of the network where the required information is more likely to be stored and steers the queries toward those regions. To discriminate among regions, we introduce the concept ofinformation densityand a procedure that allows nodes its estimation. The effectiveness of our scheme is evaluated through simulation in a vehicular environment with realistic mobility models.

We consider the problem of obtaining a high quality estimates of band-limited sensor fields when sensor measurements are noisy and the nodes are irregularly deployed and subject to random motion. We consider the mean square error (MSE) of the estimate and we analytically derive the performance of several reconstruction/estimation techniques based on linear filtering. For each technique, we obtain the mean value of the MSE, as well as its asymptotic expression in the case where the field bandwidth and the number of sensors grow to infinity, while their ratio is kept constant. Our results provide useful guidelines for the design of sensor networks when many system parameters have to be traded off.

alessandro.nordio © polito.it chiasserini © polito.it viterbo © deis.unical.it ABSTRACT The irregular sampling theory is concerned with the problem We consider wireless sensor networks whose nodes are randomly of recovering a bandlimited signal from a sequence of samples, deployed and, thus, provide an irregular sampling of the sensed which may be taken in an irregular way. Several reconstruction field. The field is assumed to be bandlimited; a sink node col- algorithms have been proposed in the literature (see e.g., [1]) and lects the data gathered by the sensors and reconstructs the field by have found application in a variety of fields, such as digital medical using a technique based on linear filtering. By taking the mean imaging [2,3], geophysics [4], weather forecast [5], astronomy, and square error (MSE) as performance metric, we evaluate the effect oceanography [6]. of quasi-equally spaced sensor layouts on the quality of the recon-Recently, a great deal of attention has been devoted to sensor netstructed signal. The MSE is derived through asymptotic analysis works, whose nodes sample a physical field, like air temperature, for different sensor spatial distributions, and for two of them we light intensity, pollution levels or rain falls, and report the data to are able to obtain an approximate closed form expression. The case a common processing unit (sink node). The sink node is in charge of uniformly distributed sensors is also considered for the sake of of reconstructing the sensed field. In general, sensors are not regcomparison. The validity of our asymptotic analysis is shown by ularly deployed in the area of interest and, if not synchronized to

We consider the problem of self-deployment and relocation in mobile wireless networks, where nodes are both sensors and actuators. We propose a unified, distributed algorithm that has the following features. During deployment, our algorithm yields a regular tessellation of the geographical area with a given node density, called monitoring configuration. Upon the occurrence of a physical phenomenon, network nodes relocate themselves so as to properly sample and control the event, while maintaining the network connectivity. Then, as soon as the event ends, all nodes return to the monitoring configuration. To achieve these goals, we use a virtual force-based strategy, which proves to be very effective even when compared to an optimal centralized solution.

This paper introduces WiSE, a sender-side, transport-layer protocol that modifies the standard SCTP protocol. WiSE aims at exploiting SCTP's multihoming capabilities by selecting in real time the best choice among available, alternate paths to the same destination. Through the use of bandwidth estimation techniques, WiSE tries to infer whether losses are due to congestion or to radio channel errors. At the same time, the available bandwidth of the current path used for transmission is matched to that of an alternate path, also probed for available bandwidth. If the current path is severely congested and the alternate path is lightly loaded, WiSE switches the transmission onto the alternate path using SCTP's flexible path management capabilities. Extensive simulations under different scenarios highlight the superiority of the proposed solution with respect to TCP and the standard SCTP implementation.

We focus on a vehicular network supporting safety applications, and we present an application and a channel access mechanism for efficient multihop broadcasting. We study the performance of the proposed solution by developing an analytical framework, which provides several metrics relevant to message dissemination. Analytical results are compared with the performance obtained through ns

We address the problem of wireless stations self-configuration in a WLAN environment with overlapping access point coverages. We propose and investigate a transport-layer solution called AISLE (autonomic interface selection) that builds on top of the SCTP protocol and exploits its multihoming features. Through simulation, we evaluate AISLE's capability to maximize the throughput of multi-interface stations and to achieve an optimal partition of the stations across overlapping WLANs. Although we focus on a WLAN scenario, AISLE is independent of the technology used at the physical and MAC layers

We consider a wireless sensor network whose nodes may enter the so-called sleep mode, corresponding to low power consumption and reduced operational capabilities. We develop a Markov model of the network representing: 1) the behavior of a single sensor as well as the dynamics of the entire network, 2) the channel contention among sensors, and 3) the data routing through the network. We use this model to evaluate the system performance in terms of energy consumption; network capacity, and data delivery delay. Analytical results present a very good matching with simulation results for a large variety of system scenarios, showing the accuracy of our approach

In this paper, we address the problem of determining an optimal topology for Bluetooth wireless personal area networks (BT-WPANs). In BT-WPANs, multiple communication channels are available, through a frequency hopping technique. The way network nodes are grouped to share the same channel, and which nodes are selected to bridge traffic from a channel to another, has a significant impact on the capacity and throughput of the system, as well as the nodes' battery lifetime. The determination of an optimal topology is thus extremely important. Our approach is based on a min-max formulation of the optimization problem, which produces topologies that minimize the traffic load of the most congested node in the network (thus also minimizing energy consumption) while meeting the traffic requirements and the constraints posed by the BT-WPAN technology. We investigate the performance of the topologies produced by our optimization approach as the system requirements vary, and evaluate the trade-offs existing between system complexity and network efficiency. Results show that a topology optimized for some traffic requirements is remarkably robust to changes in the traffic pattern. Due to the problem complexity, the optimal solution is attained in a centralized manner. Although this implies severe limitations, a centralized solution can be applied whenever a network coordinator is elected, and provides a useful term of comparison for any distributed heuristics.

The behavior of large-scale wireless sensor networks has been shown to be surprisingly complex and difficult to analyze, both by empirical experiment and simulation. In this paper we develop a new analytical model of the behavior of wireless sensor networks, based on a fluid approach, i.e., we represent the sensor network by a continuous fluid entity distributed on the network area. The model accounts for node energy consumption, channel contention, as well as traffic routing; thus, it is well suited for describing the properties of sensor networks and understanding their complex behavior.

This paper presents WiSE, a transport-layer protocol that modifies the standard SCTP protocol. WiSE aims at exploiting SCTP's multihoming capabilities by selecting in real time the best choice among available, alternate paths to the same destination. Through the use of bandwidth estimation techniques, WiSE tries to infer whether losses are due to congestion or radio channel errors. At the same time, the available bandwidth on the current path used for transmission is matched to that of an alternate path, also probed for available bandwidth; if the current path is severely congested, and the alternate path is lightly loaded, WiSE switches the transmission onto the alternate path, using SCTP's flexible path management capabilities. Extensive simulations under different scenarios highlight the superiority of the proposed solution with respect to the standard SCTP implementation.

In this work we present an approach for maintaining the topology of a Bluetooth scatternet at an optimal configuration despite the dynamic behavior of the nodes in time. Our goal is to keep the ratio of the average scatternet throughput and node power consumption as high as possible while nodes unpredictably change their communication peers and migrate across the network. The approach consists in keeping the total number of hops between communicating nodes relatively low by periodically reconfiguring the scatternet topology based on the actual traffic pattern of the network

We propose an information-sharing application for wireless intervehicular networks (IVNs), called Infoshare. The Infoshare application leverages the broadcast nature of the wireless medium to achieve maximum spreading of information queries among vehicles, while a smart caching policy limits the overhead resulting from useless queries and duplicated replies. Simulation by ns2 is used to investigate the performance of Infoshare, highlighting the impact of various system parameters on spreading dynamics. A network scenario featuring one- and two-lane traffic traveling either at the same or at different speeds is considered. Simulation results come in handy for the development of analytical models, identifying critical parameters and justifying assumptions.

This paper proposes a distributed self-healing technique for topology formation in dynamic Bluetooth wireless personal area networks (BT-WPANs) and analyzes three new algorithms for scatternet formation. The three algorithms employ distributed procedures for the insertion of one or more nodes in a BT-WPAN, and are able to effectively compromise between the need for system efficiency and the desire to promptly adapt to topology changes. Depending on which algorithm is employed, the proposed approach generates BT-WPANs with different connectivity properties as well as topology structures.

In wireless ad hoc networks, nodes communicate with far off destinations using intermediate nodes as relays. Since wireless nodes are energy constrained, it may not be in the best interest of a node to always accept relay requests. On the other hand, if all nodes decide not to expend energy in relaying, then network throughput will drop dramatically. Both these extreme scenarios (complete cooperation and complete noncooperation) are inimical to the interests of a user. In this paper, we address the issue of user cooperation in ad hoc networks. We assume that nodes are rational, i.e., their actions are strictly determined by self interest, and that each node is associated with a minimum lifetime constraint. Given these lifetime constraints and the assumption of rational behavior, we are able to determine the optimal share of service that each node should receive. We define this to be the rational Pareto optimal operating point. We then propose a distributed and scalable acceptance algorithm called Generous TIT-FOR-TAT (GTFT). The acceptance algorithm is used by the nodes to decide whether to accept or reject a relay request. We show that GTFT results in a Nash equilibrium and prove that the system converges to the rational and optimal operating point.

One of the most critical issues in wireless sensor networks is represented by the limited availability of energy within network nodes; thus, making good use of energy is a must to increase network lifetime. We define as network lifetime the period from the time instant when the network starts functioning till the network runs, satisfying its quality requirements, i.e., a given level of coverage in the area of interest is guaranteed. To maximize system lifetime, we exploit sensor spatial redundancy by defining sub-sets of sensors active in different time periods, to allow sensors to save energy when inactive. Two approaches are presented: the first one, based on mathematical programming techniques, must run in a centralized way; whereas the second one is based on a greedy algorithm, aimed at a distributed implementation. To assess their performance and provide guidance to network design, the two approaches are compared by varying several network parameters.

We propose a distributed fair scheduling framework for wireless ad hoc networks. Unlike previous works, which assume error-free or predictable channels, our work is based on the signal-to-interference-plus-noise ratio (SINR) model and views channel errors as a result of the interference among the scheduled flows. We show by analysis that under the presented framework long term fairness is guaranteed; furthermore, the framework enables us to maximize throughput and minimize transmit power in a distributed manner.

Wireless local area networks (WLANs) based on the IEEE 802.11 technology are becoming increasingly popular and widely deployed. However, the growing need for quality of service (QoS) guarantees is difficult to implement in distributed systems like WLANs, where the random access protocol and the unpredictability of the wireless channel hamper their interaction with well-established architectures like DiffServ. Even without trying to provide deterministic QoS guarantees, simpler requirements are hard to get by. For example, the basic requirement of providing fair access to all users is conflicting with the nature of higher-layer protocols: TCP is fair only under certain conditions, hardly met by 802.11b WLANs. Another basic requirement is the protection for short-lived TCP flows, that are sensitive to losses during the early stages of the TCP window growth. The main contribution of this paper is the proposal of an LLC-layer algorithm that can be implemented at both access point (AP) and wireless stations (WSs). The algorithm aims at guaranteeing fair access to the medium to every user, by awarding longer transmission opportunities to WSs that experienced short channel failures. At the same time, such award mechanism can protect short-lived flows while they strive to get past the critical "small window regime". We outline the proposed solution and present a simulation study that shows the effectiveness of the new algorithm in comparison to the standard 802.11b implementation.

A critical issue in wireless sensor networks is represented by the limited availability of energy within network nodes; therefore making good use of energy is a must. A widely employed energy-saving technique is to place nodes in sleep mode, corresponding to a low-power consumption as well as to reduced operational capabilities. In this work, we develop a Markov model of a sensor network whose nodes may enter a sleep mode, and we use this model to investigate the system performance in terms of energy consumption, network capacity, and data deliver delay. Furthermore, the proposed model enables us to investigate the trade-offs existing between these performance metrics and the sensor dynamics in sleep/active mode. Analytical results present an excellent matching with simulation results for a large variety of system scenarios showing the accuracy of our approach.

This paper focuses on energy saving in 802.11-based WLANs. Typically, 802.11 wireless interfaces consume a significant amount of energy. Previous work has shown that the power saving function specified in the IEEE 802.11 standard is not enough to ensure energy efficiency; thus, other solutions to energy saving are highly needed. Here we consider the 802.11 distributed access scheme and explore the possibility to increase the time period that a wireless station spends in the low-power operational state, the so-called doze state. The key feature of the proposed mechanism is that it enables a station to enter the doze state during channel contention, by exploiting the virtual carrier sense mechanism and the backoff function. By using the network simulator ns-2, we compare the performance obtained through our scheme with the results attained when the standard DCF mechanism is employed.

Wireless local area networks (WLAN) using IEEE 802.11 technology are expected to become a widespread networking solution to provide both real-time services and data applications. In this paper, we consider a WLAN with infrastructure using the IEEE 802.11e distributed contention-based access scheme, the so-called EDCA. We investigate the EDCA performance in presence of voice and data integrated traffic, and observe the inefficiencies that arise when access differentiation is performed on the basis of traffic type only We then present a solution that improves both fairness and throughput of real-time traffic in WLAN employing EDCA, and we show the benefits of our proposal through some simulation results obtained with ns-2.

Mobility, and the fact that nodes may change their communication peers in time, generates a permanently changing traffic flows in the Bluetooth scatternet. Thus, forming an optimal scatternet for a given traffic pattern may not be enough, rather a scatternet that best supports the traffic flows as they vary in time is required. In this article we propose an algorithm suite that enables us to modify the nodes' links and roles. Periodically executing these algorithms helps in maintaining the distance (measured in hops weighted with the corresponding traffic intensity) between every source-destination pair at a minimum. This allows for a higher network throughput, lower packet delivery delay, nodes' energy consumption, and reduced communication overhead.

In this paper, we address the problem of energy efficiency in wireless ad hoc networks. We consider an ad hoc network comprising a set of sources, communicating with their destinations using multiple routes. Each source is associated with a utility function which increases with the total traffic flowing over the available source-destination routes. The network lifetime is defined as the time until the first node in the network runs out of energy. We formulate the problem as one of maximizing the sum of the source utilities subject to a required constraint on the network lifetime. We present a primal formulation of the problem, which uses penalty functions to take into account the system constraints, and we introduce a new methodology for solving the problem. The proposed approach leads to a flow control algorithm, which provides the optimal source rates and can be easily implemented in a distributed manner. When compared with the minimum transmission energy routing scheme, the proposed algorithm gives significantly higher source rates for the same network lifetime guarantee.

This paper addresses the problem of power control in ad hoc networks supporting multicast traffic. First, we present a distributed algorithm which, given the set of multicast transmitters and their corresponding receivers, provides an optimal solution to the power control problem, if there is any. The transmit power levels obtained by solving the optimization problem minimize the network power expenditure while meeting the requirements of the SNR at the receivers. Whenever no optimal solution can be found for the given set multicast transmitters, we introduce a joint scheduling and power control algorithm, which eliminates the strong interferers thus allowing the other transmitters to solve the power control problem. The algorithm can be implemented in a distributed manner; however, it provides a sub-optimal solution since it is based on local information. Simulation results show that the obtained solution is close to the global optimum, when it exists. When there is no optimal solution, the proposed algorithm tries to maximize the number of successful multicast transmission.

In wireless ad hoc networks, nodes communicate with far off destinations using intermediate nodes as relays. Since wireless nodes are energy constrained, it may not be in the best interest of a node to always accept relay requests. On the other hand, if all nodes decide not to expend energy in relaying, then network throughput will drop dramatically. Both these extreme scenarios (complete cooperation and complete noncooperation) are inimical to the interests of a user. In this paper we address the issue of user cooperation in ad hoc networks. We assume that nodes are rational, i.e., their actions are strictly determined by self interest, and that each node is associated with a minimum lifetime constraint. Given these lifetime constraints and the assumption of rational behavior, we are able to determine the optimal throughput that each node should receive. We define this to be the rational Pareto optimal operating point. We then propose a distributed and scalable acceptance algorithm called generous tit-for-tat (GTFT). The acceptance algorithm is used by the nodes to decide whether to accept or reject a relay request. We show that GTFT results in a Nash equilibrium and prove that the system converges to the rational and optimal operating point.

In this paper, we address the problem of energy-conscious cache placement in wireless ad hoc networks. We consider a network comprising a server with an interface to the wired network, and some nodes requiring access to the information stored at the server. In order to reduce access latency in such a communication environment, an effective strategy is caching the server information at some nodes distributed across the network. Caching, however, can considerably impact the system energy expenditure; for instance, disseminating information incurs additional energy burden. Since wireless devices have limited amounts of available energy, we need to design caching strategies that optimally trade-off between energy consumption and access latency. We pose our problem as an integer linear program. We show that this problem is the same as a special case of the connected facility location problem, which is known to be NP-hard. We devise a polynomial time algorithm which provides a sub-optimal solution. The proposed algorithm applies to any arbitrary network topology and can be implemented in a distributed and asynchronous manner. In the case of a tree topology, our algorithm gives the optimal solution. In the case of an arbitrary topology, it finds a feasible solution with an objective function value within a factor of 6 of the optimal value. This performance is very close to the best approximate solution known today, which is obtained in a centralized manner. We compare the performance of our algorithm against three candidate caching schemes, and show via extensive simulation that our algorithm consistently outperforms these alternative schemes.

Wireless technologies sharing the same frequency band and operating in the same environment often interfere with each other, causing severe decrease in performance. We propose two coexistence mechanisms based on traffic scheduling techniques that mitigate interference between different wireless systems operating in the 2.4-GHz industrial, medical, and scientific band. In particular, we consider IEEE 802.11 wireless local area networks (WLANs) and Bluetooth (BT) voice and data nodes, showing that the proposed algorithms can work when the two systems are able to exchange information as well as when they operate independently of one another. Results indicate that the proposed algorithms remarkably mitigate the interference between the IEEE 802.11 and BT technologies at the expense of a small additional delay in the data transfer. It is also shown that the impact of the interference generated by microwave ovens on the IEEE 802.11 WLANs performance can be significantly reduced through the mechanisms presented.

We develop a novel approach for conserving energy in battery-powered communication devices. There are two salient aspects to this approach. First, the battery-powered devices move through multiple, progressively deeper, sleep states in a predictable manner. Nodes in deeper sleep states consume lower energy while asleep, but incur a longer delay and higher energy cost to awaken. Second, the nodes are woken up on demand through a paging signal. To awaken nodes that are in deep sleep, the paging signal has to be decoded using very low power circuits such as those used in radio frequency tags. To accommodate this need, in a manner that scales well with the number of nodes, the number of distinct paging signals has to be much less than the number of possible nodes. This is accomplished through a group-based wakeup scheme, which initially awakens the targeted node along with a number of other similarly disposed nodes that subsequently return to their original sleep state. Tradeoffs among energy consumption, delay and overhead are presented; comparisons with other protocols show the potential for 16% to 50% improvement in energy consumption.

Wireless networking has experienced a great deal of popularity, and significant advances have been made in wireless technology. However, energy efficiency of radio communication systems is still a critical issue due to the limited battery capacity of portable devices. In this paper, we deal with the charge recovery effect that takes place in electrochemical cells and show how we can take advantage of this mechanism to increase the energy delivered by a battery. Then, we present energy-aware traffic shaping techniques, as well as scheduling and routing algorithms, which exploit the battery recovery effect.

Different wireless systems sharing the same frequency band and operating in the same environment are likely to interfere with each other and experience a severe decrease in throughput. We consider IEEE 802.11 WLANs and Bluetooth-based WPANs, which operate in the 2.4 GHz ISM bands. We propose two coexistence mechanisms based on traffic scheduling techniques, which mitigate interference between the two technologies. The proposed algorithms can be applied either when 802.11 and Bluetooth are able to exchange information as well as when they operate independently of one another. Results show that through the proposed coexistence mechanisms the interference between 802.11 and Bluetooth can be reduced and the throughput of the two systems is significantly improved at the expense of a small additional delay in the transfer of data traffic.

In this paper, we address the problem of determining an optimal topology for Bluetooth wireless personal area networks (BT-WPAN). In BT-WPAN, multiple communication channels are available, thanks to the use of a frequency hopping technique. The way network nodes are grouped to share the same channel, and which nodes are selected to bridge traffic from a channel to another, has a significant impact on the capacity and the throughput of the system, as well as the nodes' battery lifetime. The determination of an optimal topology is thus extremely important; nevertheless, to the best of our knowledge, this problem is tackled here for the first time. Our optimization approach is based on a model derived from constraints that are specific to the BT-WPAN technology, but the level of abstraction of the model is such that it can be related to the more general field of ad hoc networking. By using a min-max formulation, we find the optimal topology that provides full network connectivity, fulfills the traffic requirements and the constraints posed by the system specification, and minimizes the traffic load of the most congested node in the network, or equivalently its energy consumption. Results show that a topology optimized for some traffic requirements is also remarkably robust to changes in the traffic pattern. Due to the problem complexity, the optimal solution is attained in a centralized manner. Although this implies severe limitations, a centralized solution can be applied whenever a network coordinator is elected, and provides a useful term of comparison for any distributed heuristics.

In this paper, we address the problem of energy efficiency in ad hoc wireless networks. We consider a network that is shared by a set of sources, each one communicating with its corresponding destination using multiple routes. Each source is associated with a utility function which increases with the total traffic flowing over the available source-destination routes. The network lifetime is defined as the time until the first node in the network runs out of energy. We formulate the problem as one of maximizing the sum of the sources' utilities subject to the required constraint on network lifetime. We present a primal formulation of the problem, which uses penalty functions to take into account the system constraints, and we introduce a new methodology for solving the problem. The proposed approach leads to a flow control algorithm, which provides the optimal sources' rate and can be easily implemented in a distributed manner. When compared with the minimum transmission energy routing scheme, the proposed algorithm gives significantly higher sources' rates for same network lifetime guarantee.

In wireless ad hoc networks, nodes communicate with far off destinations using intermediate nodes as relays. Since nodes are energy constrained, it may not be in the best interest of a node to always accept relay requests. However, if all nodes decide not to expend energy in relaying, then network throughput will drop dramatically. Both these extreme scenarios (complete cooperation and complete non-cooperation) are inimical to the interests of a user. In this paper, we address the issue of user cooperation in ad hoc networks. We assume that nodes are rational, i.e. their actions are strictly determined by self-interest, and that each node is associated with a minimum lifetime constraint. Then, we are able to determine the optimal throughput that each node should receive, and we define this to be the rational Pareto optimal operating point. We propose a distributed and scalable acceptance algorithm, which is used by the nodes to decide whether to accept or reject a relay request. The algorithm results in a Nash equilibrium, and we prove that the system converges to the rational and optimal operating point.

Wireless video-surveillance networks are gaining increasing popularity due to the number of applications they make possible. We address the problem of designing a wireless video-surveillance network so as to optimize its performance. In particular, we investigate the possible trade-offs between energy consumption and image quality. We provide simulation results showing that video compression can be very beneficial in reducing data transmission costs, provided that the energy cost of video compression is low. Moreover, we discuss the impact of compression on image delay.

Wireless access to the Internet requires that information integrity is preserved while transmitting data over the radio channel. ARQ schemes and TCP are often used as error-control techniques at the link layer and at the transport layer, respectively. We study the interactions between an ARQ protocol and TCP when a data traffic connection includes both wired and wireless links. By using standard Markovian techniques, we analyze the impact of different parameter settings of the ARQ scheme and of the radio channel conditions on the TCP performance.

In this paper, we deal with dynamic pricing strategies for connection-oriented services in wireless systems. Dynamic pricing policies allow the network operator to charge a cost per time unit depending on the network usage. In this way, the users behavior can be regulated and the network management is significantly improved. We model the user demand and the call duration as functions of the service price. By using standard Markovian techniques to represent the system evolution, we devise an optimal linear pricing scheme, which can be easily computed and controlled. When compared with a flat-rate policy, where a constant price for the network services is fixed, the proposed solution is able to provide a better quality of service to the users as well as a greater revenue to the network operator. For example, when eight radio channels are available and the traffic load is equal to 0.8, we obtain a 25% improvement in the network revenue with respect to the flat-rate policy, while the blocking probability is halved.

The first part of this paper presents a novel scheme for handover provisioning in wireless asynchronous transfer mode (W-ATM) networks based on in-band signaling. First, the network architecture and principles of in-band signaling are described, discussing advantages and interaction with other procedures and signaling techniques. Then, loss-free protocols for the handover procedures are presented and compared with existing proposals. The second part of the paper is devoted to performance analysis of the handover procedures. A general methodology for evaluating handover delays and required buffer capacity is introduced and exemplified for one of the protocols introduced before. Numerical results give insight into the handover procedure characteristics and are compared with estimates provided by detailed discrete event simulations for validation purposes. Finally, additional simulation results are presented for parallel, concurrent handovers, evaluating the requirements posed to the network by different handover procedures.

Consider a communication link where the last hop is wireless and TCP is used as transport protocol over the end-to-end connection. We study the capability of the link layer to hide losses over the wireless link to TCP in spite of the time varying transmission quality. We focus on link-layer retransmission mechanisms and determine their parameter setting in such a way that a reliable communication link is provided. In particular, we choose a significant QoS metric at the transport layer and fixed its targeted value, and we adapt the maximum number of link-layer transmissions to the characteristics of the wireless link so that the desired QoS at the transport layer is provided. Results showing the impact of the link-layer retransmissions on the TCP performance are derived by using analytical models based on Markovian techniques.

In this paper we develop a novel approach to conserving energy in battery powered communication devices. There are two salient aspects to this approach. First, the battery powered devices move through multiple, progressively deeper, sleep states in a predictable manner. Nodes in deeper sleep states consume lower energy while asleep but incur a longer delay and higher energy cost to wake up. Second, the nodes are woken up on demand through a paging signal. To awaken nodes that are in deep sleep, the paging signal has to be decoded using very low power circuits such as those used in RF tags. To accommodate this need, in a manner that scales with with the number of nodes, the number of distinct paging signals has to be much less than the number of possible nodes. This is accomplished through a group based wake up scheme, that initially awakens the targeted node along with a number of other similarly disposed nodes that subsequently return to their original sleep state. Trade-offs among energy consumption, delay as well as overhead are presented; comparisons with other protocols show the potential for 16 to 50% improvement in energy consumption.

A challenging aspect of mobile communications consists in exploring ways in which the available run time of terminals can be maximized. We present a detailed electrochemical battery model and a simple stochastic model that captures the fundamental behavior of the battery. The stochastic model is then matched to the electrochemical model and used to investigate battery management techniques that may improve the energy efficiency of radio communication devices. We consider an array of electrochemical cells. Through simple scheduling algorithms, the discharge from each cell is properly shaped to optimize the charge recovery mechanism, without introducing any additional delay in supplying the required power. Then, a battery management scheme, which exploits knowledge of the cells' state of charge, is implemented to achieve a further improvement in the battery performance. In this case, the discharge demand may be delayed. Results indicate that the proposed battery management techniques improve system performance no matter which parameters values are chosen to characterize the cells' behavior.

We present a new approach to minimizing energy consumption by addressing battery management techniques that exploit the charge recovery effect inherent to many secondary storage batteries. We review results that pertain to the capacity of a battery and its dependence on the intensity of the discharge current. The phenomenon of charge recovery that takes place under bursty or pulsed discharge conditions is identified as a mechanism that can be exploited to enhance the capacity of a battery. The bursty nature of many data traffic sources suggests that data transmissions may provide natural opportunities for charge recovery. We explore stochastic models to track charge recovery in conjunction with bursty discharge processes. Using the postulated model, we identify the improvement to battery capacity that results from a pulsed discharge driven by bursty stochastic discharge demand. The insight from this analysis leads us to propose discharge shaping techniques that trade-off energy efficiency with delay.

A challenging aspect of mobile communications consists of exploring ways in which the available run time of the terminals can be maximized. In this paper we investigate battery management techniques that can dramatically improve the energy efficiency of radio communication devices. We consider an array of electrochemical cells connected in parallel. Through simple scheduling algorithms the discharge from each cell is properly shaped to optimize the charge recovery mechanism, without introducing any additional delay in supplying the required power. Then, a traffic management scheme, that exploits the knowledge of the cells' state of charge, is implemented to achieve a further improvement in the battery performance. In this case, the discharge demand may be delayed. Results indicate that the proposed battery management techniques improve system performance no matter which parameter values are chosen to characterize the cell behavior.

The wireless local loop (WLL) is an emerging technology that allows rapid connection to the wired network from remote locations. A crucial issue for the WLL is the system design so that the number of subscribers can be maximized while providing the required quality of service. In this paper, we consider a broadband wireless local loop with fixed point users and we evaluate the subscriber capacity for a slotted CDMA scheme which accommodates integrated voice, data, and video traffic. The impact of the system parameters on the WLL performance is studied via simulation and provides a useful insight on how to efficiently design the network.

The coexistence of different wireless systems that share the same frequency band is becoming one of the most challenging issue due to the wide-spread popularity of WLANs and to the rapid development of short-range radio systems. In this paper we consider WLANs based on the IEEE 802.11 standard and a short-range radio system based on Bluetooth specifications, which operate in the 2.4 GHz ISM frequency band. We present a model of the interference that IEEE 802.11 WLANs may experience either because of a voice or a data Bluetooth link. We derive results showing that by applying simple traffic shaping techniques, interference can be significantly reduced. In the presence of Bluetooth data traffic, WLAN packet error probability can be decreased by 1996 at the expense of an additional average delay in Bluetooth packet transmission equal to 10 ms, or by 2946 at the expense of a Bluetooth average packet delay equal to 110 ms.

This paper presents a power management scheme that maximizes energy saving in wireless ad hoc networks while still meeting the required quality of service (QoS). We assume that battery-powered devices can be remotely activated by a waking-up signal using a simple circuit based on RF tag technology. In this way, devices that are not currently active may enter a sleep state and power up only when they have pending traffic. Radio devices select different time-out values, so called sleep pattern, to enter various sleep states depending on their battery status and quality of service. The performances of the proposed policy are derived by simulation for a simple ad hoc network scenario. Results show the achieved tradeoff between power saving and traffic delay.

This paper addresses the problem of radio resource allocation for packet switched wireless networks. The proposed approach is based on a FDMA/TDMA scheme with time slot partitioning and exploits the reuse factor concept applied to the time domain. This technique is used in combination with a specific time slot assignment in the MAC protocol, so that radio resources sharing among users within the same cell is controlled, while interference among users belonging to different cells is minimized. The scheme allows mobile users to make soft handovers while they move from one cell to contiguous ones. Adaptive time slot allocation algorithms can be envisioned to deal with hot spot traffic without requiring major frequency allocation replanning. Preliminary performance results that show that throughput benefits can be obtained are presented.

Management of terminal handovers is one of the challenges in mobile wireless ATM (W-ATM) systems due to the connection-oriented nature of ATM. When the ATM connection is re-established to follow the terminal roaming from one base station to another, seamless handover is necessary to guarantee the required quality of service (QoS). A promising procedure is based on the use of handover buffers at the (destination) base station. This paper proposes a technique to reduce the buffer requirements in seamless handovers by introducing the concept of buffer sharing at the base station (B/sup 2/S/sup 2/): connections requiring handover at the same time and towards the same base station share a common pool of buffers available at the base station. When compared to the standard dedicated buffer (DB) approach B/sup 2/S/sup 2/ achieves the required QoS with a reduced total buffer size, and consequently with a reduced cost of the base station. Simulation and numerical results are discussed to quantify this reduction.

The overall objective of this work is to explore ways in which the energy efficiency of communications can be enhanced through the use of communication protocols that exploit the charge recovery mechanism inherent to many secondary storage batteries.In the first part of this paper, we summarize the behavior of electrochemical energy cells.We compile results that pertain to the capacity of a cell and its dependence on the intensity of the discharge current.The phenomenon of charge recovery that takes place under bursty or pulsed discharge conditions is identified as a mechanism that can be exploited to enhance the capacity of a cell.The bursty nature of many data trafllc sources suggests that there may be a natural fit between the two.In the second part of this manuscript, we explore thii synergy by developing a stochastic model that tracks charge recovery in conjunction with bursty discharges due to transmissions driven by Bernoulli arrivals.We derive the resulting capacity advantage relative to constant discharge as a function of the burstiness of the arrival for two discharge scenarios.

This paper introduces a stochastic model of battery behavior, that emulates electrochemical mechanisms that are key to battery performance under pulsed discharge conditions. A pulsed discharge allows charge recovery during the idle periods. Recovery depends on the state of charge of the battery and on the duration of the rest time period. Using the postulated model, we derive the improvement to battery lifetime that results from pulsed current discharge driven by bursty stochastic transmissions. The results emphasize the role of traffic shaping in the quest to enhance battery behavior.