The integration of Unmanned Aerial Vehicles (UAVs) into next-generation mobile networks is widely recognized as a key enabler of disruptive applications, where aerial platforms may function either as network nodes or as advanced network users supporting a variety of services. Unfortunately, experimental testbeds in which UAVs perform tasks while communicating with ground infrastructure over Fifth-Generation (5 G) networks remain scarce, primarily due to the challenges posed by legal restrictions on Beyond Line-of-Sight (BLoS) and autonomous operations. Motivated by this need, this work presents the design, implementation, and evaluation of a novel indoor experimental testbed for assessing the performance of UAV-based systems operating over 5 G networks. The testbed features an autonomously controlled quadcopter equipped with a 5G modem, connected to a private 5 G network implemented using SoftwareDefined Radio (SDR) technology and the OpenAirInterface (OAI) framework. To ensure a controlled environment, a motion capture system is used to provide absolute indoor positioning data, emulating Global Navigation Satellite System (GNSS) coordinates without relying on external satellites. A preliminary experimental campaign is conducted to evaluate the proposed system in terms of 5 G network performance, radio link characteristics, and UAV platform energy consumption.

As well known, the integration of Unmanned Aerial Vehicles (UAVs) and Intelligent Reflecting Surfaces (IRS) can enhance the reliability, coverage, robustness, and efficiency of next-generation wireless and mobile networks. Nevertheless, the performance of UAV-mounted Passive Metasurfaces (PMs), referred to as Aerial PMs (APMs), is significantly influenced by system configurations, electromagnetic signal propagation, UAV mobility, and wobbling. While most state-of-the-art studies rely on analytical models and simulations to assess these factors, this work presents an experimental investigation of an APM in a realistic operating environment. To this end, we developed an advanced experimental testbed that combines a precision tracking system with real-time electromagnetic measurements, enabling a detailed characterization of the metasurface response in an indoor scenario. Additionally, we compared the feasibility of APMs against a static setup where the metasurface is mounted on a fixed structure. Experimental results demonstrate that APMs can effectively maintain signal quality and spectral efficiency. However, they also reveal the impact of positioning, orientation, and in-flight stability on overall system performance. Our observations provide interesting insights into optimizing Aerial IRS-assisted networks, contributing to the design of more robust solutions for the Beyond 5th and 6th Generation networks.

Viewport in immersive media corresponds to the field of view (FoV), playing a critical role in both data transmission volume and user experience. However, instantaneous and highly dynamic interactions often conflict with segment-based transmission modes, resulting in substantial redundant data transmission and wastage of valuable resources. In this paper, we analyze data from an open-source dataset and our self-collected records to investigate the interactive characteristics of viewers in immersive scenes, including focus time, viewing area scope, movement direction, and tile access probability. Based on empirical statistical inference, we innovatively introduce the concept of an irregular, expandable, and directional extended field of view (EoV) to describe the dynamically variable area mimicking human visual motion. Furthermore, we propose a motion-aware tile-based adaptive control scheme for viewing areas, named VAAC-IM, designed to enable flexible transmission of immersive media. Specifically, we developed an FoV prediction model based on ConvLSTM, leveraging spatiotemporal features from historical viewing records to provide advanced predictions of visual motion preferences. Subsequently, we model the viewing area control process as a constrained submodular minimization problem, dynamically managing irregular EoV area using marginal effects. Finally, we perform a comprehensive validation. The results demonstrate that VAAC-IM significantly enhances performance in terms of reducing black edge coverage, minimizing data volume, lowering latency, and improving overall user experience.

As Deep Neural Networks (DNNs) are evolving in complexity to meet the demands of novel applications, a single device becomes insufficient for training, leading to the emergence of distributed DNN training. However, this evolution exposes a gap in research surrounding security vulnerabilities on model poisoning attacks, especially in model parallel setups, an area that has been scarcely studied. To bridge this gap, we introduce Patronus, an approach that counters model poisoning attacks in distributed DNN training, accommodating both data and model parallelism. With the employment of Loss-aware Credit Evaluation, Patronus scores each participating client. Based on the continuously updated credit, malicious clients are isolated and detected after multiple epochs by Shuffling-based Isolation Mechanism. Additionally, the training system is reinforced by Byzantine Fault-tolerant Aggregation to minimize malicious client impacts. Comprehensive experiments confirm Patronus's superior reliable and efficient performance over the existing methods under attack scenarios.

As the telecommunications industry embarks on the transition to Sixth-Generation (6G) networks, this paper examines the integration of Non-Terrestrial Networks (NTN), and in particular satellite backhauling, in the context of Fifth-Generation (5G) systems. The Integrated Access and Backhaul (IAB) technology, conceived as a wireless terrestrial backhauling system in the Next Generation Radio Access Network (NG-RAN), has been identified as a possible enabler for the integration of satellite nodes. Despite the work already done in this direction, the combination of IAB architectures with satellite nodes operating in both the access and backhaul side requires further evaluations on feasibility and limitations for networks integrating Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites. To this end, this work contributes providing insights on background technologies, as well as a detailed analysis of the issues and challenges arising from such integration and a definition of use cases to support narrow-band and broadband services. Furthermore, the design and implementation of a simulation tool is proposed for a performance evaluation in terms of registration time, link capacity, single-hop and end-to-end delay. Results show that the integration turns out to be feasible, even if with strong constraints coming from the satellite system rather than the IAB usage itself. Indeed, the earth-satellite link in LEO systems has a significant impact on the packet delivery time due to the discontinuous coverage. In case of GEO satellite instead, a non-terrestrial backhaul link could limit the performance of the whole system, especially at lower elevation angles.

This paper describes the integration of Terrestrial and Non-Terrestrial Networks, wherein space-based network entities collaborate with traditional and emerging terrestrial communication frameworks to furnish pervasive, resilient, and three-dimensional wireless connectivity worldwide toward the 6th Generation of communication networks. This integration supports heterogeneous services, such as enhancing coverage, user experience, system capacity, service reliability, and availability, while also providing high-speed connectivity in remote or disaster-affected areas, improving existing 5th Generation technologies. Various Use Cases are detailed, highlighting the pivotal roles that Non-Terrestrial Networks play in distinguishing between urban/suburban and rural environments, with particular emphasis on transportation ecosystems. Through this analysis, Key Performance Indicators and requirements are delineated to characterize the requisite service quality for these diverse Use Cases. The paper further presents an overview of potential and standards-compliant integrated Terrestrial/Non-Terrestrial architectures, delineating their roles both in backhauling and access across different layers of Non-Terrestrial systems and elements. These insights are derived from studies conducted within the Integrated Terrestrial And Non-Terrestrial Networks (ITA-NTN) project, part of the European Union initiative defined as the Italian National Recovery and Resilience Plan (NRRP) RESTART Research Program.

Unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRSs) represent a cutting-edge technology for improving the channel capacity of wireless communications, by capitalizing on UAVs’ 3-D mobility coupled with the IRSs’ smart radio capabilities. This work envisions a scenario in which a swarm of UAVs equipped with IRSs serves multiple Internet of Things (IoT) ground nodes (GNs) concurrently transmitting to a single base station (BS) via OFDMA. The huge number of passive elements composing the IRSs introduces a significant complexity in the mission design. Therefore, each IRS is divided into patches that can be simultaneously used to serve different nodes. Considering general Rician fading, a comprehensive channel model for IRS-assisted UAV-aided networks is derived. Then, a multiobjective mixed-integer nonlinear programming problem is conceived to maximize the sum-rate of the GNs and, at the same time, minimize the difference among the users’ data rates, by jointly optimizing the trajectories and the phase shift matrices. This nonconvex problem, reformulated in terms of scheduling (i.e., patch-GN assignment), is challenging to solve. Hence, it is rearranged as a Markov Decision Process and a quasi-optimal solution is obtained via Deep Reinforcement Learning. Extensive simulation analysis is performed to validate the results and the accuracy of the proposed model.

Quantum Internet (QI) is a system of interconnected quantum computers able to exchange information encoded in the so called quantum bits (qubits). Differently from the classical counterpart, qubits benefit from a manifold properties guaranteed by quantum mechanics, such as superposition and entanglement. Despite the fact that quantum networks bring significant advantages, several phenomena can negatively impact the overall system, potentially hindering communication. In order to evaluate the network performance, a comprehensive probability expression is derived in this work to ultimately determine how many qubits are expected to be successfully received by nodes. On this basis, a Mixed-Integer Non-Linear Programming (MINLP) problem is formulated to fairly maximize the qubits exchanged between node pairs and jointly optimize (i) the position of the quantum source, and (ii) the entanglement distribution plan. To cope with the non-convexity of the problem, an iterative optimization algorithm, leveraging Block Coordinate Descendent (BCD) and Successive Convex Approximation (SCA) techniques, is proposed. A thorough simulation campaign is conducted to corroborate the theoretical findings. Numerical results demonstrates, under different parameter setups, that the proposed algorithm provides superior performance with respect to a baseline approach.

Non-Terrestrial Networks represent a valuable solution for providing connectivity to Internet of Things (IoT) devices in remote areas, where classical infrastructure is unavailable. Due to the low-power nature of IoT devices, an Unmanned Aerial Vehicle (UAV) can prevent the energy depletion of these Ground Nodes (GNs) by employing Wireless Power Transfer through an array antenna. Starting from the mathematical modeling of such a scenario, two Mixed-Integer Non-Linear Programming problems are formulated to fairly maximize (i) the energy distribution and (ii) the total amount of data transmitted to a Low Earth Orbit CubeSat. Therefore, it is necessary to optimize the drone kinematics, the transmission scheduling plan, and the beamforming vectors of the array antenna. To cope with their non-convexity, both problems are mathematically manipulated to reach a tractable form, for which two optimization algorithms are proposed and their complexity analyzed. To prove the effectiveness of the overall solution, a comprehensive simulation campaign is conducted under several parameter settings, such as number of GNs and UAV antenna elements with different transmission power levels. Finally, the proposal is compared with a baseline, which confirms the superiority of the proposal up to 7 times in terms of total transmitted data.

The Internet of Drones (IoD) is a networking architecture that stems from the interplay between Unmanned Aerial Vehicles (UAVs) and wireless communication technologies. Networked drones can unleash disruptive scenarios in many application domains. At the same time, to really capitalize their potential, accurate modeling techniques are required to catch the fine details that characterize the features and limitations of UAVs, wireless communications, and networking protocols. To this end, the present contribution proposes the Internet of Drones Simulator (IoD-Sim), a comprehensive and versatile open source tool that addresses the many facets of the IoD. IoD-Sim is a Network Simulator 3 (ns-3)-based simulator organized in a 3-layer stack, composed by (i) the Underlying Platform, which provides the telecommunication primitives for different standardized protocol stacks, (ii) the Core, that implements all the fundamental features of an IoD scenario, and (iii) the Simulation Development Platform, mainly composed by a set of tools that speeds up the graphical design for every possible use-case. In order to prove the huge potential of this proposal, three different scenarios are presented and analyzed from both a software perspective and a telecommunication standpoint. The peculiarities of this open-source tool are of interest for researchers in academia, as they will be able to extend to model upcoming specifications, including, but not limited to, mobile networks and satellite communications. Still, it will certainly be of relevance in industry to accelerate the design phase, thus improving the time-to-market of IoD-based services.

Kubernetes (K8s) has become a core technology for cloud-native applications. However, a design flaw of the external IP in K8s leads to the service-oriented man-in-the-middle attack. Existing solutions (e.g., script monitor) attempt to address it passively, which allows attackers enough analysis time to bypass these static rule reviews. Differently, we propose a mutation-enabled proactive defense mechanism, aiming to change the asymmetry between attackers and defenders. It involves the address mutation (i.e., network identification) module and the connection ID (i.e., communication identification) mutation module. In the former module, we analyze mutation constraints and prove the corresponding mutation grouping problem to be NP-hard. Then, a maximally coloring-driven mutation grouping algorithm is developed. Since the address allocation time grows linearly with the service size, we design a prefetched address allocation algorithm. After designing the interaction flow between modules, we present a randomized algorithm in the latter module. Thus our mechanism does not affect methods oriented to other attacks. Eventually, it can continuously interrupt the attack and keep the service connection by incrementally updating K8s and the transport layer protocol. Experiments in the Alibaba cloud demonstrate that it can effectively defend against the attack with an acceptable performance loss.

Network function virtualization (NFV) supports the rapid development of service function chain (SFC), which efficiently connects a sequence of network virtual function instances (VNFIs) placed into physical infrastructures. Current SFC migration mechanisms usually keep static SFC deployment after finishing certain objectives, and deployment methods mostly provide static resource allocation for VNFIs. Therefore, the adversary has enough time to plan for devastating attacks for in-service SFCs. Fortunately, moving target defense (MTD) was proposed as a game-changing solution to dynamically adjust network configurations. However, existing MTD methods mostly depend on attack-defense models, and lack adaptive mutation period. In this article, we propose an Intelligence-Driven Service Function Chain Migration (ID-SFCM) scheme. First, we model a Markov decision process (MDP) to formulate the dynamic arrival or departure of SFCs. To remove infeasible actions from the action space of MDP, we formalize the SFC deployment as a constrained satisfaction problem. Then, we design a deep reinforcement learning (DRL) algorithm named model-based adaptive proximal policy optimization (MA-PPO) to enable attack-resistant migration decisions and adaptive migration period. Finally, we evaluate the defense performance by multiple attack strategies and two realistic datasets called CICIDS-2017 and LYCOS-IDS2017 respectively. Simulation results highlight the effectiveness of ID-SFCM compared with representative solutions.

With the explosive growth of Internet traffic, large sensitive and valuable information is at risk of cyber attacks, which are mostly preceded by network reconnaissance. A moving target defense technique called host address mutation (HAM) helps facing network reconnaissance. However, there still exist several fundamental problems in HAM: 1) current approaches cannot be self-adaptive to adversarial strategies; 2) network state is time-varying because each host decides whether to mutate IP address; and 3) most methods mainly focus on enhancing security, but ignore the survivability of existing connections. In this paper, an Intelligence-Driven Host Address Mutation (ID-HAM) scheme is proposed to address aforementioned challenges. We firstly model a Markov decision process (MDP) to describe the mutation process, and design a seamless mutation mechanism. Secondly, to remove infeasible actions from the action space of MDP, we formulate address-to-host assignments as a constrained satisfaction problem. Thirdly, we design an advantage actor-critic algorithm for HAM, which aims to learn from scanning behaviors. Finally, security analysis and extensive simulations highlight the effectiveness of ID-HAM. Compared with state-of-the-art solutions, ID-HAM can decrease maximum 25% times of scanning hits while only influencing communication slightly. We also implemented a proof-of-concept prototype system to conduct experiments with multiple scanning tools.

In the upcoming Sixth-Generation (6G) of mobile communication systems, space network entities will cooperate with conventional terrestrial networks to provide three-dimensional wireless connectivity around the World. By considering the resulting massive amount of data to be managed into non-terrestrial segments, it will be necessary to dynamically configure functionalities across space network entities. Preliminary contributions in this context focus on quasi-static scenarios, while ignoring the challenging issues (i.e., intermittent visibility, dynamic network configuration, and communication delays) introduced by satellites’ movement and communication protocols enabling the integration of terrestrial and non-terrestrial networks. To bridge this gap, this work presents a network architecture with novel orchestration capabilities of services into non-terrestrial segments. In the proposed approach, the interaction between terrestrial and non-terrestrial entities and the cloud has been detailed across service request, configuration, and provisioning phases. Then, starting from a system model describing the network configuration and the resulting deployment delays of services, an optimization problem has been formulated to dynamically allocate Virtual Network Functions (VNFs) among LEO CubeSats over a looking-ahead horizon, based on service requests, computational capabilities of the involved CubeSats, visibility matrices, and expected deployment delay bounds. Finally, the proposed optimization problem has been solved through three heuristic strategies. Computer simulations have been carried out to demonstrate the ability of the developed strategies to achieve results close to the optimal solution and to ensure better performance against a benchmark scheme.

In the emerging 6G satellite deployments, the interaction between Non-Terrestrial Network terminals and satellite constellations will generate a large surface of attack, which requires the design of novel security architectures. The scientific literature suggests to implement security services as Virtual Network Functions, installed onboard the satellites. The dynamic orchestration of these services, however, still represents a challenging and open research issue. To bridge this gap, this paper presents a novel approach willing to allocate security Virtual Network Functions across satellites, in a dynamic and optimal way. To this end, an optimization problem is formulated by deeply considering the intermittent connectivity between terminals on the Earth and the satellite constellation, the limited computational capabilities of satellites, and the need to provide secure Virtual Network Functions before a given time deadline. Then, the Tabu Search algorithm is used to solve the optimization problem and achieve preliminary results in realistic scenarios. The study illustrates the feasibility of the proposed approach and highlights the issues to address in future research activities.

Intel software guard extensions (SGX) technology allows cloud vendors to provide customers with an independent and trusted execution environment (TEE). It protects critical data confidentiality and integrity from malicious software. However, more and more SGX side-channel attacks have appeared, which seriously undermine the confidence of tenants in cloud security. The related research focuses on system hardware and SGX compiler solutions for specific attacks, which also has difficulties in deployment. Differently, we propose an intelligent-driven proactive defense strategy, which is based on live migration. To the best of our knowledge, this is the first proactive defense against SGX side-channel attacks. We adopt the Markov decision process to solve the migration programming problem. The innovative deep reinforcement learning (DRL) solves problems of the unknown state transition probability and large machine load states, which is called self-checking proximal policy optimization (SPPO). It changes the reward pattern, improving the convergence speed and stability of DRL. In prototype experiments, we deploy the strategy in the OpenStack platform agilely to prove the defense performance and low virtual machine costs.

Digital services and digital service chains are the heart beating of the modern economy. Their composition involves several players, i.e., processes, software, devices, and many kinds of data exchanged among them. In such a scenario, it is important to guarantee data confidentiality, integrity, as well as authentication and authorization procedures between the communicating parties of a service chain. Cyber-security frameworks are explicitly designed for this purpose. They rely on the integration of different software modules, mutually interfaced to accomplish complex security tasks. Nevertheless, it is important to guarantee a high level of protection during data exchange among the modules. Currently, standardized authentication and authorization mechanisms are implemented through proprietary “As-a-Service” products, but the deployment of a mature on-premise solution is still missing. To bridge this gap, this contribution proposes an authentication and authorization module that automatically protects the information flowing among the modules of cyber-security frameworks. It guarantees resource availability only to authenticated subjects. Thus, their operations are confined in what actions they are authorized for. The proposed module has been implemented and tested in a real cyber-security framework under development into the H2020 GUARD project. Experimental tests show that the proposed module enables authentication and authorization procedure delegation among GUARD modules, which eases their implementation, while maximizing the flexibility of the set of access control policies and an efficient protection of the services.

This contribution proposes a novel task offloading scheme for robotics applications in Information-Centric Networks. The reference scenario includes heterogeneous agents connected to various wireless Network Attachment Points, asking for computational services to Multi-access Edge Computing servers deployed at the network edge. It also uses Information-Centric Networking facilities to implement resource announcement, resource discovery, and service provisioning. The effectiveness of the conceived solution has been tested through experimental tests in realistic scenarios.

The Social Internet of Things is gaining momentum thanks to its unique capability of i) autonomously building social relationships among smart objects and ii) supporting the novel services within the Social Network. During the service provisioning, the Trust Management System is in charge of selecting suitable objects able to accomplish the requested services. In this context, available solutions present two relevant issues to be solved. First, they generally assume to select social objects that only achieve higher trustworthiness without considering their actual computing capabilities. Indeed, it would significantly compromise the Quality of Experience in the Social Network of objects. Second, they assume to implement this task directly within constrained devices, becoming unpractical considering their limited computational and storage capabilities. To improve and speed up service provisioning, this paper proposes a novel Trust Management System scheme that fairly distributes service requests by jointly considering the trustworthiness and resource capabilities of available objects. This Trust Management System has been designed to exploit fog computing to efficiently handle the whole process of service provisioning in real-world deployments while relieving constrained devices from all processing and storage efforts. Its behavior is investigated through computer simulations. Obtained results demonstrated that the conceived approach outperforms baseline solutions in terms of latency, fairness in services distribution, and responsiveness in malicious nodes' detection.

A new class of poisoning attacks has recently emerged targeting the client-side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current DNS defense architecture does not include DNS clients. Although relative encryption solutions can mitigate this attack, they require the cooperation of multiple parties, and the deployment speed is slow. Therefore, we propose an intelligent-driven proactive defense strategy. First, we model the offensive and defensive process as a stochastic game based on moving target defense. Second, we adopt and optimize Proximal Policy Optimization (PPO), a deep reinforcement learning method, to solve problems caused by uncertain attack strategies and unknown state transition probability. Third, we design a self-checking component in PPO to solve the uncertainty of action space caused by game state constraints based on our previous work. Thus the convergence speed and stability of PPO are improved. Finally, to the best of our knowledge, we are the first to game with intelligent attackers besides three conventional ones. Our strategy does not require any modifications to the DNS architecture. Through an extensive experimental campaign, the prototype system is proved to be effective against multiple attack modes. Its success rate is 98.5% approximately, and network round-trip time is about 55 ms. Even for random attackers, our method can achieve the theoretical maximum defensive success rate.

Vehicular Ad hoc Networks (VANETs) are prone to packet drop attacks because of their inherent distributed architecture and dynamic topology. Existing security schemes mainly focus on multi-path and trust-based routing. Unfortunately, the former causes high energy consumption and the latter requires trust assessment, which is not easy to implement in practice. Route mutation (RM) is emerging as an active defense technology that changes routes periodically. Traditional RM is conceived for fixed network topologies, and needs a centralized controller, so that it cannot be applied to VANETs. Therefore, the present contribution investigates RM in VANETs by proposing a Grid-based extended Joint Action Learning approach (Grid-eJAL). To the best of our knowledge, this is the first contribution that designs an online and adaptive multi-agent reinforcement learning (MARL) for RM to mitigate attacks in VANETs. Differently from existing MARL schemes, Grid-eJAL allows vehicles to share parameters to accelerate the convergence speed of learning. In Grid-eJAL, the area of interest is split in equally sized grids and, when a vehicle transmits packets, the next hop with the minimum angle of mobility is selected within the grid considered as optimal by the learning policy. The convergence of Grid-eJAL is proved theoretically. Finally, extensive simulation results highlight the effectiveness of Grid-eJAL compared to representative state-of-the-art solutions.

Multi-path transmission control protocol (MPTCP) is an extension of TCP that enables the concurrent transmission of information through different network interfaces (e.g., Cellular, Wi-Fi, 802.11p, and so on) available at terminal side. It is well known that MPTCP can provide significant advantages in bandwidth aggregation and transmission stability. Unfortunately, path diversity can limit bandwidth aggregation efficiency and incur higher delays. These issues become critical when in presence of emerging mobile AR and VR applications, which are bandwidth hungry, time-sensitive and exhibit abrupt variations of the bitrate. To address these issues, we propose theReinforcementLearning-based mobile AR/VR multipath transmission with streamingPowerSpectrumDensity analysis (RL-PSD). RL-PSD analyses the Power Spectrum Density (PSD) of the AR/VR input stream to extract its features. Then, both the input stream and network features are considered to model the MPTCP congestion control as an reinforcement learning process. Finally, a two-stage reinforcement algorithm is proposed to optimize transmission performance. RL-PSD has been tested in both single-terminal and multi-terminal scenarios: results show that it outperforms the other advanced solutions conceived to support the multipath transmission of AR/VR streams.

Named-Data Networking is a prominent solution for offering flexible communications in Metropolitan Area Networks. Its features were recently extended with SDN and MEC capabilities for better supporting consumer mobility and improving packet forwarding functionalities. However, the scientific literature lacks a thorough investigation on the impact on the energy consumption of these protocols. Focusing the attention on the novel approach presented by the same authors of this paper, this contribution analyzes the energy saving in Information-Centric Metropolitan Area Networks. Results demonstrate the efficacy of the investigated approach by evaluating the different components of energy consumption in the considered scenario.

Piezoelectric nanogenerators recently emerged as a turning point in the design of energy-aware and energy harvesting transmission scheme at the nanoscale. This work considers their adoption in diffusion-based molecular communications, proposing a power control strategy based on feedback control theory. In particular, the transmission power of an electrochemical nanodevice fed by a piezoelectric nanogenerator is dynamically set proportionally to the available energy budget, by using a closed-loop control scheme. The resulting system is analytically modeled with a discrete-time nonlinear state equation. The range of acceptable values of the proportional gain is theoretically derived by studying technological constraints and global asymptotic stability. The impact of the proportional gain on both output variance and time constant of the linearized system around the equilibrium point is also investigated. Computer simulations validate the theoretical analysis under different parameter settings. The comparison against state of the art transmission scheme also demonstrates the unique ability of the conceived approach to ensure, at the equilibrium, the targeted performance level.

Nowadays, amateur broadcasters can massively generate video contents and stream them across the Internet. For this reason, crowdsourced livecast services (CLS) are attracting millions of users around the world. To provide a smooth and high-quality playback experience to viewers with diversified device configurations in dynamic network conditions, CLS providers have to find a way to deploy cost-effective transcoding operations by distributing the computation-intensive workload among Cloud, Edge, and Crowd. In addition, it is necessary to control transcoded streams from million broadcasters to worldwide viewers. To address these challenges, we propose a novel stochastic approach that jointly optimizes the usage of transmission resources (e.g., bandwidth), and transcoding resources (e.g., CPU) in CLS systems that leverage the cooperation of Cloud, Edge, and Crowd technologies. In particular, we first design an augmented queue structure that can jointly capture the dynamic features of data transmission and online transcoding, based on the virtual queue technology. Then, we formulate a joint resource allocation problem, using stochastic optimization arguments, and devise an Accelerated Gradient Optimization (AGO) algorithm to solve the optimization problem in a scalable way. Moreover, we provide four main theoretical results that characterize the algorithm’s steady-state queue-length, optimality, and fast-convergence. By conducting both numerical simulations and system-level evaluations based on our prototype, we demonstrate that our solution provides lower system costs and higher QoE performance against state-of-the-art solutions.

Thanks to its inherent capabilities (such as fairly long radio coverage with extremely low power consumption), long-range wide area network (LoRaWAN) can support a wide spectrum of low-rate use-cases in Industry 4.0. In this article, both plain and energy harvesting (EH) industrial environments are considered to study the performance of LoRa radios for industrial automation. In the first instance, a model is presented to investigate LoRaWAN in Industry 4.0 in terms of battery life, battery replacement cost, and damage penalty. Then, the EH potential, available within an Industry 4.0, is highlighted to demonstrate the impact of harvested energy on the battery life and sensing interval of LoRa motes deployed across a production facility. The key outcome of these investigations is the cost trade-off analysis between battery replacement and damage penalty along different sensing intervals which demonstrates a linear increase in aggregate cost up to £1500 in case of 5 min sensing interval in the plain (nonenergy harvesting) industrial environment while it tends to decrease after a certain interval up to five times lower in EH scenarios. In addition, the carbon emissions due to the presence of LoRa motes and the annual CO2emission savings per node have been recorded up to 3 kg/kWh when fed through renewable energy sources. The analysis presented herein could be of great significance toward a green industry with cost and energy efficiency optimization.

Internet of Things (IoT) is shaping the agricultural industry to enhance process control, boost business efficiency, and improve product quality. Digital agriculture has the potential to fulfill the climate change adaptation with optimized natural resources use and achieve economic benefits through increased agricultural productivity. Field monitoring and agricultural processes automation can lead to alternative options for managing natural resources and environment. In addition, social and cultural benefits can be fostered by means of enhanced communication infrastructure. The agriculture domain usually requires long-range communications, extended battery lifetime, and high reliability of sensors devices. Narrowband IoT (NB-IoT) is growing as a key Low-Power Wide-Area Network (LPWAN) technology for IoT applications. Smart Agriculture proves a natural use of NB-IoT, as a typical industrial IoT application. In this paper, a NB-IoT agricultural field test is reported, within a real system architecture, comparing its network performances with General Packet Radio Service (GPRS) standard. The experimental campaign exposes gains and challenges of the technology highlighting the most attractive aspects for the farming context.

The Internet of Drones paradigm is considered as a key enabler for several cutting edge verticals, including surveillance, planetary exploration, protection, loads transportation, and aerology. The main limitations to its wide-scale adoption arise from the constraints on the resources available onboard of drones: this concerns energy, computational and storage capabilities. Unfortunately, current literature mainly focuses on energy limitations, leaving unexplored the interplay with other constraints. To bridge this gap, the present contribution also encompasses the limitations on the memory onboard, which can be critical when drones have to acquire high resolution multimedia signals for ambient awareness services. In particular, an iterative stochastic approach is conceived hereby to tune data flows from/to drones subject to energy and memory constraints in order to fulfill an Out-of-Service probability below a given threshold. Stemming from the proposed approach, two algorithms have been also designed that seek a different complexity-performance tradeoff. The first one is less complex and more conservative, since it plans the mission once at the beginning. The second, instead, is slightly more complex and aggressive but it allows the drone to gather and upload a higher volume of data and shorten the gap with respect to the ideal case.

A new Domain Name System (DNS) cache poisoning attack aiming at clients has emerged recently. It induced cloud users to visit fake web sites and thus reveal information such as account passwords. However, the design of current DNS defense architecture does not formally consider the protection of clients. Although the DNS traffic encryption technology can alleviate this new attack, its deployment is as slow as the new DNS architecture. Thus we propose a lightweight adaptive intelligent defense strategy, which only needs to be deployed on the client without any configuration support of DNS. Firstly, we model the attack and defense process as a static stochastic game with incomplete information under bounded rationality conditions. Secondly, to solve the problem caused by uncertain attack strategies and large quantities of game states, we adopt a deep reinforcement learning (DRL) with guaranteed monotonic improvement. Finally, through the prototype system experiment in Alibaba Cloud, the effectiveness of our method is proved against multiple attack modes with a success rate of 97.5% approximately.

In a quite short time after its envisioning, Information Centric Networking paradigm seems to be a standing reality: name-driven primitives have been defined to support networking functionalities and a few architectures have been implemented. In fact, a content consumer can now search for a data item by name and completely disregard its IP address. But the question to raise is: is this searching mechanism really information-centric or is it rather name-centric only? This paper defines the requirements enabling the transition to a paradigm for content exchange which is exclusively based on information. A running example, based on the Web of Data, has been illustrated to describe the key functionalities of the proposed approach. The envisioned solution grounds on a mechanism to embed content in names which, notably, is architecture-agnostic and can run on top of all existing ICN flavors designed so far. This mechanism supports a semantic-based retrieval of content, that returns a list of contents potentially satisfying the consumer, incrementing retrieval precision and recall. The enhancement in terms of recall comes at the cost of a communication overhead, which has been analytically modeled and estimated in this paper.

Detecting disruptive events, such as earthquakes, using environmental monitoring systems is a particularly promising, but rather challenging, opportunity. The Internet of Things (IoT) can play a significant role in characterizing and predicting seismic events. The present contribution introduces QuakeSense, an open-source earthquake and weather monitoring system. The implemented IoT system is configured as a Long Range (LoRa)based star topology with a fully energy-autonomous sensor node. The system leverages some of the most useful features of two emerging IoT technologies, e.g., LoRa and Message Queue Telemetry Transport (MQTT), and enables the near real-time monitoring of seismic events through a web-based interface. An experimental campaign has been carried out to verify the current consumption and, therefore, the battery lifetime of the sensor node. Moreover, LoRa parameters have been extensively tested as to evaluate performances in several configurations. The obtained results in terms of latency and Packet Delivery Ratio (PDR) demonstrated the reliability of the proposal.

The many fold capacity magnification promised by 5G vehicular networks will likely provide massive multimedia services, including infotainment, augmented reality, location services, etc. However, the large-scale and stochastic characteristic of these burgeoning multimedia applications will lead to an exponential increase of traffic in vehicular networks. Meanwhile, the diversified requirements introduced by the coexistence with traditional services will also bring new challenges to the efficient usage of resources. To cope with the above challenges, we propose a novel Stochastic Optimization framework for Pricing-aware Multimedia Services (SOPMS) in this paper, which targets the maximization of utility with the constraints of system stability and traffic pricing policy. Specifically, we leverage the Lyapunov function to address this optimization objective, which is decomposed into three tractable subproblems. For each problem, a distinct algorithm is conceived, i.e. Quality of Experience (QoE) based utility maximization, cooperative resource allocation and pricing-based transmission control. Finally, validated by the simulations, our proposed SOPMS preserves the optimality and significantly improves the queue stability and service utility, in comparison with other state-of-the-art solutions.

The absence of proper infrastructure and rapid change in topological formation makes it challenging to adapt the appropriate clustering scheme for choosing a stable cluster head. In this paper, we propose an Adaptive REsilience Algorithm (AREA) for cluster reformation in VANET that takes into account multiple interrelated metrics including the direction, relative velocity, and inter-nodal distance of the moving vehicles yielding a stable clustering with better life time. Simulation results clearly exhibit the robustness and stability of the proposed algorithm.

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

The manyfold capacity magnification promised by dense 5G networks will make possible the provisioning of broadband multimedia services, including virtual reality, augmented reality, and mobile immersive video, to name a few. These new applications will coexist with classic ones and contribute to the exponential growth of multimedia services in mobile networks. At the same time, the different requirements of past and old services pose new challenges to the effective usage of 5G resources. In response to these challenges, a novel Stochastic Optimization framework for Green Multimedia Services named SOGMS is proposed herein that targets the maximization of system throughput and the minimization of energy consumption in data delivery. In particular, Lyapunov optimization is leveraged to face this optimization objective, which is formulated and decomposed into three tractable subproblems. For each subproblem, a distinct algorithm is conceived, namely quality of experience--based admission control, cooperative resource allocation, and multimedia services scheduling. Finally, extensive simulations are carried out to evaluate the proposed method against state-of-art solutions in dense 5G networks.

Time Slotted Channel Hopping (TSCH) is a recent enhancement of the IEEE 802.15.4 standard aimed at the industrial sector. To complement its unique features, the IPv6 over the TSCH mode of IEEE 802.15.4e (6tisch) Working Group released a set of specifications that define the way Time Slotted Channel Hopping could be integrated in embedded IPv6 networks based on the IPv6 over Low Power WPAN. TheI 6tisch standard is now available under several hardware/software platforms so that a cross-comparison of the different implementations deserves particular attention in order to properly identify performance bottlenecks and scavenge pros and cons of the 6tisch technology. The present contribution provides an experimental cross-comparison of protocol stack implementations running on top of different devices. In particular, experimental tests were conducted to characterize the behavior of OpenWSN and Contiki in Time Slotted Channel Hopping- based scenarios made up by TelosB, Zolertia Z1, and OpenMote motes. By considering star and chain topologies and different traffic load conditions, many performance indices were measured, including memory footprint, energy consumption, synchronization time, duty- cycle, end-to-end packet delays, and Packet Loss Ratio. Obtained results highlight strengths and weaknesses of each hardware/software platform, while providing trade-off considerations about flexibility, accuracy, energy-efficiency, and Quality of Service.

This paper focuses on Long Range Wide Area Networks (LoRaWAN) and sheds some light on the feasibility of fully cable-less Internet of Things deployments, where dual-radio gateways are fed by a photovoltaic plant and equipped with a wireless backhaul. As a first step, the power needs of a dual-radio gateway, serving a mix of realistic machine-to-machine applications and leveraging different combinations of front-end chipsets and backhaul wireless technologies, are investigated. Then, the achieved results are properly employed to size the PV plant, as well as to estimate its installation costs and land acquisition. Finally, cost-saving and carbon footprints analysis is presented to demonstrate the socio-economic benefits arising out of these cable-less deployments for LoRaWANs. The conducted study clearly exhibits that network operators can achieve their break-even point during the early stages after the deployment, while adopting environment-friendly approaches because of carbon emission savings achieved by renewable energy.

Information-centric networking (ICN), as a novel network paradigm, is expected to natively support mobility, multicast, and multihoming in vehicular ad hoc networks (VANETs). In this paper, the adoption of ICN principles for multimedia streaming in multihomed VANETs is investigated, with a major emphasis on the tradeoff between the quality of experience and energy efficiency (EnE). To formalize this problem, a cost optimization model is first proposed, based on queueing theory arguments. Then, a novel green information-centric multimedia streaming (GrIMS) framework is designed to drive the system toward optimal working points in practical settings. GrIMS consists of three enhanced mechanisms for on-demand cloud-based processing, adaptive multipath transmission, and cooperative in-network caching. Finally, a massive simulation campaign has been carried out, demonstrating that, thanks to its core components, the GrIMS enables flexible multimedia service provisioning and achieves an improved performance in terms of start-up delay, playbacks continuity, and EnE with respect to state-of-the-art solutions.

Device-to-Device (D2D) communications are a prominent feature of 5G systems, introduced to provide a native support to distributed services in mobile environments. D2D technologies enable straight interactions between mobile terminals without a compulsory involvement of base stations. In this manuscript, we study and propose an optimized caching strategy to content distribution on top of D2D technology, based on Information Centric Networking (ICN) principles. The rationale is that ICN architectures can provide seamless support to mobile services and decouple contents from node identifiers, thus providing a promising match with D2D requirements. To this end, a novel fluid-based model in proposed hereby that catches the interplay between ICN functionalities, D2D requirements, and 5G specifications. Then, based on this model, an optimal content replication problem is formulated, encompassing caching overhead and system load. Additionally, this problem is thoroughly analyzed to prove that it has an optimal solution with time threshold form. A practical algorithm ς*-OCP is further proposed in order to implement the optimal caching control in realistic environments. Finally, a massive simulation campaign is carried out to test the proposed algorithm in comparison to state-of-the-art solutions.

Internet of Things (IoT) is widely recognized as a key enabling driver in environmental monitoring systems, thanks to its inherent capabilities to provide sensing and actuation functionalities on a distributed basis. At the same time, the majority of IoT applications are Information Centric by design: they target specific actions that are not related to any network node but can be provided by multiple resources within the system. In this perspective, the Information-Centric Networking (ICN) paradigm, formulated to answer to Future Internet requirements, can natively and seamlessly support IoT technologies as a service bus. Moving to IoT-enabled environmental monitoring systems, the publish-subscribe data exchange model is the pivotal functionality that ICN architectures have to support. In fact, an environmental monitoring system can be easily set up by letting one or more collecting stations to subscribe to the events or sensed values, generated (i.e., published) by IoT nodes scattered in the scenario of interest. Unfortunately, the support for publish-subscribe functionalities is not uniform across the main ICN architectures designed so far. To shed some light on this issue, the present contribution analyzes to what extent ICN architectures are able to support publish-subscribe and highlights the missing building blocks to purse this objective.

Information-Centric Networking (ICN) focuses on data dissemination instead of host communication, and it is a promising solution for video streaming in Vehicular Ad-hoc Networks (VANETs). In Information-Based VANETs, content copies can be cached by arbitrary mobile nodes in the networks. Thus, it is required to optimize the routing of content requests (referred to as Interest packets) in order to quickly locate potential content providers as soon as possible. This article proposes a Preference-aware Fast Interest Forwarding for video streaming in ICN-based VANETs (PaFF). In PaFF, each mobile user creates a Highly Preferred Content Table (HPCT) to maintain the content caching status of nodes who have similar mobility patterns and video playback behavior. Based on HPCT, a preference-aware forwarder selection mechanism is proposed to select the next hop of Interest packets in order to minimize latencies and maximize reliability. Simulation results show that PaFF achieves a considerable improvement in terms of start-up delay and cache hit ratio while incurring almost the same overhead with respect to state-of-art solutions.

Intelligent Transportation Systems (ITSs) are built considering numerous legal regulations, organisational conditions, and user needs. For this reason, they cannot be sold as plug & play products and their implementation requires a clear definition of functional and technical requirements. The primary objective of ITSs is to improve the efficiency and safety of road traffic. In fact, nowadays, the number of motorways all over Europe is increasing. The road freight transport is also growing. These are one of some reasons of caused accidents. Solution to this problem may be implementation of ITS systems. But, a reliable work of them is possible when their reliability is at a very high level. The aim of this paper is to present a new model of reliability and operation analysis of the selected ITS system which include continuous provision of power to its elements. The article also contains a logical, functional and physical viewpoint of selected ITS system.

Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks), which frequently affect Wireless Sensor Networks (WSNs). Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network’s resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM) for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe), which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes’ classification, attacks’ identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

Due to the on-demand and dynamic nature of Cloud, there is an increasing interest for automated management of adaptation and (possibly) re-deployment of cloud applications to realize quality requirements and evolution needs autonomously at run-time. This paper proposes a fast and automated approach for adapting and redeploying a cloud application at run-time as dictated by evolution needs and sudden changes in the operating environment conditions. The proposed approach exploits a graph-based model and an algorithm that extracts a sub-graph identifying the adaptation processes to be executed according to evolution changes. The approach is general enough to be implemented by any cloud application management framework. A TOSCA-based description of the structure and management aspects of the cloud application may be updated according to the above mentioned sub-graph. Then, this description may be processed by a TOSCA-compliant runtime environment to effectively adapt and possibly re-deploy the cloud application in an automated manner. The paper also illustrates the instantiation of this generic approach for adapting an e-commerce cloud application.

With reference to the IEEE 802.15.4 standard, many solutions have been formulated to face the different facets of layer-2 security. Unfortunately, the opportunities and subtleties arising from their joint adoption has been not investigated, due to the lack of an integrating framework. To this end, hereby a novel standard compatible framework is proposed, which is able to orchestrate several layer-2 security mechanisms with a limited computational footprint. Conceived as a distributed scheme, it covers the following key features: (i) multiple security configurations in homogeneous and heterogeneous scenarios; (ii) adaption to dynamic networks; (iii) lean and scalable initialization functionalities; (iv) lightweight Key Management Protocol; and (v) resilience to several attacks. The robustness against security attacks have been evaluated through a well-known automatic cryptographic protocol verifier, namely ProVerif. Moreover, to further demonstrate its effectiveness, the proposed framework has been implemented within the emerging OpenWSN protocol stack, experimentally evaluated, and compared with respect to the ZigBee IP security architecture, which integrates the Symmetric Key - Key Establishment protocol (SKKE). Results clearly show that, although security features in constrained nodes incur not negligible computational costs (which impair latencies and energy efficiency), the proposed approach always guarantees better performances with respect to the ZigBee IP security architecture. In fact, it speeds up the configuration of security services (up to 120%), while ensuring relevant energy savings (larger than 50%).

The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology, which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.

Methodologies for correct by construction reconfigurations can efficiently solve consistency issues in dynamic software architecture. Graph-based models are appropriate for designing such architectures and methods. At the same time, they may be unfit to characterize a system from a non functional perspective. This stems from efficiency and applicability limitations in handling time-varying characteristics and their related dependencies. In order to lift these restrictions, an extension to graph rewriting systems is proposed herein. The suitability of this approach, as well as the restraints of currently available ones, are illustrated, analysed and experimentally evaluated with reference to a concrete example. This investigation demonstrates that the conceived solution can: (i) express any kind of algebraic dependencies between evolving requirements and properties; (ii) significantly ameliorate the efficiency and scalability of system modifications with respect to classic methodologies; (iii) provide an efficient access to attribute values; (iv) be fruitfully exploited in software management systems; (v) guarantee theoretical properties of a grammar, like its termination.

This paper capitalizes on two emerging trends, i.e., the growing use of wireless at the edge of industrial control networks and the growing interest to integrate IP into said networks. This is facilitated by recent design contributions from the IEEE and the IETF, where the former developed a highly efficient deterministic time-frequency scheduled medium access control protocol in the form of IEEE 802.15.4e timeslotted channel hopping (TSCH) and the latter IPv6 networking paradigms in the form of 6LoWPAN/ROLL, and scheduling approaches in the form of 6TiSCH. The focus of the present work is on advancing the state-of-the-art of deterministic 6TiSCH schedules toward more flexible but equally reliable distributed approaches. In addition, this paper aims to introduce the first implementation of 6TiSCH networks for factory automation environments: it outlines the challenges faced to overcome the scalability issues inherent to multihop dense low-power networks; the experimental results confirm that the naturally unreliable radio medium can support time-critical and reliable applications. These developments pave the way for wireless industry-grade monitoring approaches.

Content-Centric Networking (CCN) is an entirely novel networking paradigm, in which packet forwarding relies upon lookup operations on content names directly instead of fixed-length host addresses. The unique features of CCN names, i.e., variable length, huge cardinality, and hierarchical structure, introduce new challenges that could hinder the deployment of such a new architecture at the Internet scale. In this paper, we make an in-depth study of characteristics of large-scale CCN names, and propose a simple yet efficient CCN-customized name lookup engine (named by TB2F), which capitalizes the strengths of Tree-Bitmap (TB) and Bloom-Filter (BF) mechanisms, while counteracts their main limitations. To this end, TB2F splits CCN prefix into a constant size T-segment and a variable length B-segment with a relative short length, which are treated using TB and BF, respectively. Furthermore, an optimal length of the T-segment is found to improve the lookup efficiency. Experimental comparisons with respect to the reference Name Prefix-Trie and Bloom-Hash have been also carried out. The results show that TB2F properly configured has good scalability and efficiency by (i) speeding up lookup operations and reducing the false positive rate with respect to Bloom-Hash; (ii) requiring less memory than Name Prefix-Trie; (iii) achieving a low overhead in updating operations in the large scale case.

Information-centric networking (ICN) leverages content demand redundancy and proposes in-network caching to reduce network and servers load and to improve quality of experience. In this paper, we study the interaction between in-network caching of ICN and Additive Increase Multiplicative Decrease (AIMD) end-to-end congestion control with a focus on how bandwidth is shared, as a function of content popularity and cache provisioning. As caching shortens AIMD feedback loop, the download rate of AIMD is impacted. Supported by an analytical model based on Discriminatory Processor Sharing and real experiments, we observe that popular contents benefit from caching and realize a shorter download time at the expense of unpopular contents, which see their download time inflated by a factor bounded by 1/1-ρ where ρ is the network load. This bias can be removed by redefining congestion control to be delay independent or by over-provisioning link capacity at the edge so that to compensate for the greediness of popular contents.

While numerous Information Centric Networking (ICN) architectures have been proposed over the last years, the community has so far only timidly attempted at a quantitative assessment of the relative quality of service level that users are expected to enjoy in each of them. This paper starts a journey toward the cross comparison of ICN alternatives, making several contributions along this road. Specifically, a census of 20 ICN software tools reveals that about 10 are dedicated to a specific architecture, about half of which are simulators. Second, we survey ICN research papers using simulation to gather information concerning the used simulator, finding that a large fraction either uses custom proprietary and unavailable software, or even plainly fails to mention any information on this regard, which is deceiving. Third, we cross-compare some of the available simulators, finding that they achieve consistent results, which is instead encouraging. Fourth, we propose a methodology to increase and promote cross-comparison, which is within reach but requires community-wide agreement, promotion and enforcement.

High-quality multimedia streaming services in Vehicular Ad-hoc Networks (VANETs) are severely hindered by intermittent host connectivity issues. The Information Centric Networking (ICN) paradigm could help solving this issue thanks to its new networking primitives driven by content names rather than host addresses. This unique feature, in fact, enables native support to mobility, in-network caching, nomadic networking, multicast, and efficient content dissemination. In this paper, we focus on exploring the potential social cooperation among vehicles in highways. An ICN-based COoperative Caching solution, namely ICoC, is proposed to improve the quality of experience (QoE) of multimedia streaming services. In particular, ICoC leverages two novel social cooperation schemes, namely partner-assisted and courier-assisted, to enhance information-centric caching. To validate its effectiveness, extensive ns-3 simulations have been executed, showing that ICoC achieves a considerable improvement in terms of start-up delay and playback freezing with respect to a state-of-the-art solution based on probabilistic caching.

Secure data aggregation still represents a very challenging topic in wireless sensor networks' research. In fact, only few solutions exists to face, simultaneously, confidentiality, integrity, adaptive aggregation, and privacy issues. Furthermore, proposals available in literature mainly assume flat network architectures, without leveraging the peculiarities of clustered wireless sensor networks, which are very common in real life deployments. This work tries to bridge the gap by proposing a solution, namely SETA, tailored to a hybrid architecture composed of wireless sensor nodes and wireless mesh routers as cluster heads. In SETA, to lower the processing workload of sensor nodes, only cluster heads are allowed to perform integrity verification checks and message merging operations to face possible network congestions. To prove its effectiveness, it has been compared, using simulations, with respect to DyDAP in several realistic settings. Results have shown that it can provide a slight improvement of the robustness to malicious nodes and of the sensing accuracy, while increasing the overall energy efficiency and decreasing the signaling overhead in the network.

The emerging IEEE802.15.4e standard and IETF RPL routing protocol are core to the organization of multi-hop Low-power and Lossy Networks. They provide key functionalities useful for a really viable Internet of Things. However, several open issues still remain and require research efforts to be solved. Among others, the design of effective scheduling schemes in such systems is one of the major problems; in fact, there are no specifications about how schedules should be realized. Trying to fill this gap, this paper presents a new Decentralized Traffic-Aware Scheduling algorithm, which is able to construct optimum multi-hop schedules in a distributed fashion. Its effectiveness has been proved by using simulation results and comparing it with a centralized scheme. The reported performance results encourage the use of the developed scheduling technique, since it allows a very efficient queue management, and thus it minimizes packet discards due to buffer overflows, while at the same time minimizing the network duty cycle.

The Time Synchronized Channel Hopping (TSCH) protocol is part of the newly defined IEEE 802.15.4e standard and represents the latest generation of highly reliable low-power MAC protocols. With implementation details left open, we conceive here a novel Traffic Aware Scheduling Algorithm (TASA) by extending the theoretically well-established graph theory methods of matching and coloring by means of an innovative approach based on network topology and traffic load. TASA is able to support emerging industrial applications requiring low latency at low duty cycle and power consumption. Preliminary simulation results have also been reported to highlight the effectiveness of the proposed algorithm.

A Selective Dropping Congestion Control (SDCC) algorithm is proposed in this work, for wireless multimedia sensor networks based on scalable multimedia encoding. SDCC, which has been designed by means of linear discrete time control theory arguments, can be applied in a fully distributed way. It periodically evaluates at each node the amount of data to drop, in order to control the level of the transmission queue, and then selects the packets to discard based on their relevance to the image quality. SDCC effectiveness has been evaluated with reference to a video-surveillance problem in several scenarios, using the Castalia simulator. Results have shown that SDCC is able to significantly improve the network performance in terms of image delivery ratio and delay.

Packet sampling can greatly reduce traffic measurement overhead in high-speed networks. At the same time, this operation introduces estimation errors that have to be carefully handled to ensure a reasonable measurement accuracy. Recently, a frequency-based approach has been proposed to catch the impact of such errors in bitrate estimation of IP flows at various time-scales. In particular, a closed-form expression for the signal-to-noise ratio has been derived as a function of the packet sampling probability, the bin size, and some basic information about the flow (i.e., first and second order moments of the packet size, and long term average packet-rate). In this work, we adopt this model to design a real-time algorithm that properly tunes the temporal resolution of bitrate estimates available at a router, with the goal of assuring a target measurement accuracy. The effectiveness of the proposed approach has been demonstrated by computer simulations, carried out using real packet traces.

Long-term evolution represents an emerging technology that promises a broadband and ubiquitous Internet access. But several aspects have to be considered for providing effective multimedia services to mobile users. In particular, in this work, we consider the design of a quality-of-service (QoS) aware packet scheduler for real-time downlink communications. To this aim, a novel two-level scheduling algorithm is conceived. The upper level exploits an innovative approach based on discrete-time linear control theory. Instead, at the lower level, a proportional fair scheduler has been properly tailored to our purposes. The performance and the complexity of the proposed scheme have been evaluated both theoretically and by using simulations. A comparison with recently proposed scheduling strategies has been also presented, considering several network conditions and real-time multimedia flows. Particular attention has been devoted to the evaluation of the quality-of-experience (QoE) provided to end users. Results have clearly shown that the proposed approach is able to greatly outperform the existing ones especially in the presence of real-time video flows.

In network measurement systems, packet sampling techniques are usually adopted to reduce the overall amount of data to collect and process. Being based on a subset of packets, they introduce estimation errors that have to be properly counteracted by using a fine tuning of the sampling strategy and sophisticated inversion methods. This problem has been deeply investigated in the literature with particular attention to the statistical properties of packet sampling and to the recovery of the original network measurements. Herein, we propose a novel approach to predict the energy of the sampling error in the real time estimation of traffic bitrate, based on spectral analysis in the frequency domain. We start by demonstrating that the error introduced by packet sampling can be modeled as an aliasing effect in the frequency domain. Then, we derive closed-form expressions for the Signal-to-Noise Ratio (SNR) to predict the distortion of traffic bitrate estimates over time. The accuracy of the proposed SNR metric is validated by means of real packet traces. Furthermore, a comparison with respect to an analogous SNR expression derived using classic stochastic tools is proposed, showing that the frequency domain approach grants for a higher accuracy when traffic rate measurements are carried out at fine time granularity.

In network measurement systems, packet sampling techniques are usually adopted to reduce the overall amount of data to collect and process. Being based on a subset of packets, they hence introduce estimation errors that have to be properly counteracted by a fine tuning of the sampling strategy and sophisticated inversion methods. This problem has been deeply investigated in the literature with particular attention to the statistical properties of packet sampling and the recovery of the original network measurements. Herein, we propose a novel approach to predict the energy of the sampling error on the real time traffic volume estimation, based on a spectral analysis in the frequency domain. We start by demonstrating that errors due to packet sampling can be modeled as an aliasing effect in the frequency domain. Then, we exploit this theoretical finding to derive closed-form expressions for the Signal-to-Noise Ratio (SNR), able to predict the distortion of traffic volume estimates over time. The accuracy of the proposed SNR metric is validated by means of real packet traces.

Abstract The 802.15.3 standard is a reference point for high data‐rate wireless personal area networks (WPANs), being able to support also multimedia applications with specific quality of service (QoS) requirements. In a 802.15.3 piconet, a single piconet co‐ordinator (PNC) distributes channel time allocations (CTAs) to devices (DEVs) associated to it, in order to provide the expected QoS. The 802.15.3 standard does not specify how this allocation should be done. To bridge this gap, we propose a dynamic CTA scheduling scheme that provides bounded average delays to multimedia applications. The algorithm is based on a control theoretic approach and acts in two steps: (1) each single DEV computes its transmission needs by applying a control algorithm that targets empty transmission queues; (2) the PNC collects requests of DEVs and allocates CTAs by taking into account the capacity constraints of the wireless channel. The main properties and the tuning rules of the algorithm have been theoretically investigated. Moreover, in order to provide a comprehensive performance evaluation of the proposed approach, realistic scenarios with video, voice, and FTP flows have been simulated using ns‐2. Results confirm that our scheduler is able to satisfy the expected delay bounds in a wide range of traffic loads. Copyright © 2009 John Wiley & Sons, Ltd.

A wireless networked control system exploits a shared wireless network to logically link its sub-systems. Due to the peculiarities of the wireless channel, it is important to understand in which measure the quality of service provided by the communication infrastructure affects the quality of control. The aim of this work is to analyze the performance of a wireless networked control system based on the IEEE 802.11 MAC. For that purpose, a model of such a system has been built using the ns-2 simulator and the impact of MAC parameters on the system dynamics has been evaluated. Simulation results have highlighted strengths and limitations of this kind of wireless networked control system.

In Internet multimedia streaming, a raw quality base layer is adaptively enriched with one or more enhancement layers to fully use the available bandwidth. In this work, using a control theoretic approach, we design and implement an innovative scheduling strategy for properly distributing the network available bandwidth among base and enhancement layers, in order to avoid playout interruptions due to base layer underflows at the decoder side. The proposed solution has been casted in a H.264/AVC SVC video streaming architecture. Experimental results, collected using a single bottleneck testbed and three different benchmarking video sequences, have demonstrated that the proposed scheduling strategy is able to avoid or strongly limit the number of playout interruptions under moderate or high traffic conditions, respectively.

Packet sampling techniques introduce measurement errors that should be carefully handled in order to correctly characterize the network behavior. In the literature several works have studied the statistical properties of packet sampling and the way it should be inverted to recover the original network measurements. Here we take the new direction of studying the spectral properties of packet sampled traffic. A novel technique to model the impact of packet sampling is proposed based on a theoretical analysis of network traffic in the frequency domain. Moreover, a real-time algorithm is also presented to detect the spectrum portion of the network traffic that can be restored once packet sampling has been applied. Preliminary experimental results are reported to validate the proposed approach.

The recent interest for wireless networked control systems has boosted new research activities on real-time 802.11-based communications. In fact, due to their versatility, wireless LANs can be exploited as infrastructure in this kind of control systems, but changes to channel access techniques are required. To this aim, we propose a novel experimental implementation of a TDMA scheme over 802.11 MAC, based on the hard real-time networking framework RTnet and on the Xenomai real-time task scheduler. Preliminary results have shown that, using the proposed approach, transmission delays smaller than 10 ms and packet loss rates smaller than 0.5% can be achieved.

CF-MAC is an energy efficient collision-free MAC protocol for wireless ad-hoc networks, based on the theory of Balanced Incomplete Block Design (BIBD). It has been originally designed considering energy saving as primary aim. In this paper, using computer simulations, its ability to handle voice calls has been evaluated. Specifically, the Mean Opinion Score and the Cumulative Distribution Function of the one way packet delay have been analyzed, for various protocol parameters and network scenarios. Moreover, a comparison between CF-MAC and classical 802.11 DCF access function has been proposed. Simulation results have shown that by properly selecting protocol parameters, CF-MAC can effectively support VoIP calls.

In order to provide service differentiation in 802.11 wireless local area networks, the 802.11e working group has recently proposed the hybrid coordination function (HCF) along with a HCF controlled channel access (HCCA). However, the 802.11e proposal does not specify an effective bandwidth allocation algorithm. Recently, to overcome this limitation, a control theoretic framework has been proposed in literature. Within such a framework, this paper proposes a new allocation algorithm based on a proportional-integral-derivative (PID) controller. Moreover, a performance comparison among the simple Scheduler introduced by 802.11e standard and three bandwidth allocations algorithms, designed using different dynamic regulators, has been carried out. Ns-2 simulation results showed that the best trade-off between packet delays and number of admitted flows is achieved by a properly tuned PID regulator.

Modeling TCP is fundamental for understanding Internet behavior. The reason is that TCP is responsible for carrying a huge quota of the Internet traffic. During last decade many analytical models have attempted to capture dynamics and steady-state behavior of standard TCP congestion control algorithms. In particular, models proposed in literature have been mainly focused on finding relationships among the throughput achieved by a TCP flow, the segment loss probability, and the round trip time (RTT) of the connection, which the flow goes through. Recently, Westwood+ TCP algorithm has been proposed to improve the performance of classic New Reno TCP, especially over paths characterized by high bandwidth-delay products. In this paper, we develop an analytic model for the throughput achieved by Westwood+ TCP congestion control algorithm when in the presence of paths with time-varying RTT. The proposed model has been validated by using the ns-2 simulator and Internet-like scenarios. Validation results have shown that this model provides relative prediction errors smaller than 10%. Moreover, it has been shown that a similar accuracy is achieved by analogous models proposed for New Reno TCP.

IEEE 802.11e Working Group has recently proposed an Enhanced Distributed Coordination Access (EDCA) scheme to improve the standard 802.11 Distributed Coordination Function (DCF) in order to provide service differentiation capabilities to WLANs. However, it has been widely demonstrated that EDCA does not provide an effective usage of the channel capacity, especially at high network loads.This paper proposes the Extended EDCA scheme as a distributed dynamic bandwidth allocation algorithm, providing delay guarantees to real-time media flows. Using the ns-2 simulator, we have shown that the proposed algorithm is able to provide a bounded delay service to multimedia flows in a wide range of network loads and frame loss ratios.

The additive increase/multiplicative decrease probing paradigm is at the core of TCP congestion control. To improve the classic Reno/New Reno congestion control algorithms, the recent Westwood+ TCP proposes to substitute the multiplicative decrease phase with an adaptive decrease phase, which takes into account an end-to-end estimate of the available bandwidth obtained by filtering the stream of returning ACKs. This paper aims at evaluating the performance of Westwood+ TCP over the real Internet. For that purpose, a Linux 2.4.19 implementation of Westwood+ TCP has been developed and compared with an implementation of New Reno. More than 4000 files, with different sizes, have been uploaded via ftp from a host at the Politecnico of Bari (South of Italy) to three remote servers, which are located at Parma (North of Italy), Uppsala University (Sweden) and University of California Los Angeles (UCLA, California). Experimental results indicate that Westwood+ TCP improves the goodput with respect to New Reno TCP over paths with a bandwidth delay product larger than few segments. In particular, goodput improvements up to 40-50% have been measured when transmitting data from Politecnico of Bari to Uppsala or UCLA servers. Currently, Westwood+ TCP support is available in the official Linux kernel. It was included both in the kernel 2.4.x from version 2.4.26-prel on and in the kernel 2.6.x from version 2.6.3-rcl on.

In this paper we propose to exploit cellular neural networks (CNNs) as a computational tool to obtain real-time compression of video sequences. In particular, we present a CNN-based architecture, which combines object-oriented CNN algorithms and basic coding/decoding MPEG capabilities. The proposed real-time compression architecture has been tested using standard benchmarking video sequences. Simulation results, in terms of compression ratio and peak to signal noise ratio, show that the proposed approach enables CNN-based real-time coding systems with satisfying compression ratios and good visual appearance.

Link layer error control is widely adopted in wireless LANs (WLANs) to hide the unreliability of the wireless channel to higher level protocols. This allows classic end-to-end congestion control algorithms to achieve acceptable throughput even in the presence of radio links. The present work investigates the performance of Westwood+ and NewReno TCP over a noisy wireless LAN channel. In particular, the effect of local error control persistency on the end-to-end performances of Westwood+ and NewReno TCP is addressed. The investigation has been carried out by using ns-2 computer simulation. Simulation results show that (1) in the presence of uniformly distributed packet losses a well tuned local error control leads both Westwood+ and NewReno TCP to full network utilization; (2) in the presence of bursty losses Westwood+ TCP improves the good-put with respect to NewReno TCP also in the presence of local error control; in particular, Westwood+ TCP requires a smaller number of retransmissions at the link layer than New Reno to achieve full utilization of the wireless channel.

Westwood+ TCP is a sender-side only modification of the classic TCP that is based on end-to-end estimation of the bandwidth available to the connection in order to shrink adaptively the TCP congestion window and the slow start threshold after congestion. We report measurements obtained using Linux implementations of Westwood+, Westwood and Reno to FTP data over Internet connections spanning continental and intercontinental distances. In particular, we show that the bandwidth estimation algorithm employed by Westwood+ nicely tracks the available bandwidth, whereas the previous bandwidth estimation algorithm used by TCP Westwood fails to work in the real Internet due to ACK compression. Collected live Internet measurements also show that Westwood+ improves the goodput, from 10% to 46%, with respect to TCP Reno.

Due to the fundamental end-to-end design principle of the TCP/IP for which the network cannot supply any explicit feedback, today the TCP congestion control algorithm implements an additive increase multiplicative decrease (AIMD) algorithm. It is widely recognized that the AIMD mechanism is at the core of the stability of end-to-end congestion control. In this paper we describe a new mechanism we call additive increase adaptive decrease (AIAD). The key concept of the adaptive decrease mechanism is to adapt congestion window reductions to the bandwidth available at the time the congestion is experienced. We propose Westwood++ TCP as an implementation of the AIAD paradigm, and we consider Reno TCP as an example of the AIMD mechanism for comparison. We derive a mathematical model of the throughput of the AIAD mechanism that shows that Westwood++ is stable, is friendly to Reno and increases the fairness in bandwidth utilization. To confirm the validity of the theoretical model Internet measurements are reported.