Radio Frequency Fingerprinting (RFF) is a physical (PHY) layer security technique enabling the identification of a radio transmitter without resorting to shared secrets while combining deep learning and signal processing techniques. State-of-the-art scientific contributions focus on improving identification performance in different scenarios and configurations while considering the RFF methodology as a black-box. In this work, we apply eXplainable Artificial Intelligence (XAI) techniques to expose the discriminative features allowing a neural network model to identify radio transmitters at the PHY layer. Our analysis considers image-based RFF classifiers, where received signals (in the form of IQ symbols) are processed into images, and proves that symbols contribute unequally to the identification of the transmitter. In particular, our results demonstrate that image pre-processing can be leveraged to significantly reduce the overhead of training (up to 50%) and testing (up to 70%) without affecting the classifier's final accuracy.

Today, an unprecedented number of researchers and companies are interested in exploring advanced and optimized protocols and algorithms making the 5G New Radio technology (and of course its evolutions) able to support different services under heterogeneous scenarios. In most cases, their studies leverage computer simulations and simple datasets describing isolated functionalities of the overall complex mobile communication system. Accordingly, despite all these valuable and available tools, there remains a lack of comprehensive, high-quality datasets that can support in-depth analysis, modeling, and testing of the 5G New Radio in real-world deployments. To bridge this gap, this paper presents a 5G dataset for Quality of Experience assessment in the new RAdio (5G-QoERA). Focusing the attention on a video streaming use case, the goal of the dataset is to indicate the level of quality experienced by mobile end users in terms of Mean Opinion Score, while jointly considering several influencing aspects such as real user mobility traces, real-world base station deployments and related radio map environments, variable scheduling decisions, and application-level details. After detailing the generation process of the dataset, a preliminary analysis of its features is conducted to underline the possible research activities that can take advantage of its usage.

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.

In an era dominated by digitalization in corporate strategies, the effectiveness of network infrastructures constitutes the foundation upon which enterprises build their businesses. Within this context, Intent-Based Networking (IBN) emerges as a networking paradigm that addresses the need for autonomous and intelligent systems, shifting network management from static to dynamic and automated processes. Despite its significant advancements in network automation, IBN introduces new complexities and challenges, particularly concerning the assessment of reliability or trustworthiness. Formulating intent without thorough investigation of users’ intentions could potentially lead to deliberate network sabotage, denial of services, data breaches, and privilege escalations, thereby posing significant risks to enterprise networks. To bridge this gap, this study proposes a novel Malicious Intent Detection (MID) module within the IBN framework to construct a security knowledge base for accurately classifying enterprise users’ intentions. Specifically, it detects malicious expressions directly during the intent processing stage, thereby preventing the formulation of disruptive network configurations or policies derived from malicious intents. The obtained results demonstrate the effectiveness of the proposed solution, with the ability to detect 94% of malicious intentions and achieve an accuracy of up to 93%.

In the era of pervasive connectivity, the effective provisioning of multimedia services surely represents a cornerstone challenge for both infrastructure and service providers. Given the recent evolution of Beyond 5G/6G networks, it is conventionally accepted that a Network Operator (infrastructure provider) shares its resources with tenants (service providers) by giving them the possibility to autonomously and efficiently configure the application services they provide to end users. To this end, this contribution proposes a cutting-edge framework for multimedia service provisioning, that harnesses the power of Artificial Intelligence (AI) in conjunction with Digital Twin Networks (DTNs). Specifically, it considers the real network features collected from the physical real network through Software-Defined Networking Controllers to build the DTN, whose purpose is to provide present and future physical, network, and application statistics through AI for efficient and proactive resource allocation. The DTN exposes to the tenant a concise set of monitoring parameters related to actual network-specific statistics and Quality of Service metrics and also future ones thanks to Deep Learning, without sharing in-depth details of the underlying network through Machine Learning (clustering). This information will be exploited by the service orchestrator for optimal resource allocation and redistribution, which will be deeply investigated in future works.

Law Enforcement Agencies (LEAs) heavily rely on Lawful Interception (LI) tools to investigate criminal and terrorist activities. The growing frequency of cybercrime, terrorism-related offenses, and illegal trades in the European Union (EU) has driven LEAs to explore novel LI techniques that align with the developing 5G and Beyond 5G network architectures. Moreover, the emergence of extremely dynamic and distributed networks, the increased usage of end-to-end encryption applications, and privacy protections present limitations for traditional LI approaches. In order to provide a technological solution capable of extending the 3GPP LI standard, this paper presents a novel LI framework designed on top of the standardized 3GPP LI architecture, leveraging an inspection-friendly end-to-end cryptography mechanism (e.g., a Key Escrow algorithm) at the application layer. Moreover, the proposed Lawful Interception (LI) framework enables authorized LEAs to decrypt intercepted end-to-end encrypted data within the core network. Firstly, a security proof validates the security of the proposed LI framework under two attack scenarios. Subsequently, a proof-of-concept workstation implementation that emulates a 5G network for end-to-end data exchange and cloud-based deployment validates the suggested LI framework by affirming the LEA capabilities in decrypting intercepted data. Additionally, the system performance has been studied through experimental tests, ensuring the scalability of the conceived solution and revealing the possibility of intercepting data with mainly real-time latency without affecting the Quality of Service (QoS) experienced by the user.

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.

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.

Attribute-based Searchable Encryption is emerging as a promising cryptographic technique supporting data protection, flexible access control, and keyword search over encrypted data. The current scientific literature already investigated its adoption in cloud-based services and additionally explored the usage of edge computing to implement some of the cryptographic tasks in scenarios with limited computational capabilities (such as Internet of Things). In the majority of the available solutions, however, the remote cloud is still responsible for data storage, keyword search over encrypted data, and delivery tasks. Indeed, the heavy computational load generated in scenarios with multiple data producers and data consumers (never studied yet) and large communication latencies can inevitably compromise the overall system performance. To bridge this gap, this work proposes a decentralized service architecture offering privacy-preserving data dissemination, by jointly leveraging attribute-based Searchable Encryption techniques, publish–subscribe communication model, and edge computing capabilities. Here, customized Edge Servers are deployed at the network edge to (i) collect subscription requests encoded via Searchable Encryption Trapdoors, (ii) receive data publications, encrypted via Attribute-based Searchable Encryption scheme, (iii) implement keyword search over encrypted data, and (iv) deliver encrypted data only to authorized requesters. Experimental tests explored the impact of network configuration and traffic load on both communication latency and energy consumption. Obtained results demonstrated the unique ability of the proposed solution to achieve shorter data delivery delays as well as less energy consumption with respect to cloud-based alternatives.

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.

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.

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.

The adoption of the Narrowband-Internet of Things technology in satellite communications intends to boost Internet of Things services beyond the boundaries imposed by the current terrestrial infrastructures. Apart from link-level studies in the scientific literature and preliminary 3GPP technical reports, the overall debate is still open. To provide a further step forward in this direction, the work presented herein pursues a novel service-oriented methodology to design an effective solution, meticulously stitched around application requirements and technological constraints. To this end, it conducts link-level and system-level investigations to tune physical transmissions, satellite constellation, and protocol architecture, while ensuring the expected system behavior. To offer a real smart agriculture service operating in Europe, the resulting solution exploits 24 low-Earth orbit satellites, grouped into eight different orbits, moving at an altitude of 500 km. The configured protocol stack supports the transmission of tens of bytes generated at the application layer, by also counteracting the issues introduced by the satellite link. Since each satellite has the whole protocol stack onboard, terminals can transmit data without the need for the feeder link. This ensures communication latencies ranging from 16 to 75 min, depending on the served number of terminals and the physical transmission settings. Moreover, the usage of the early data transmission scheme reduces communication latencies up to 40%. These results pave the way toward the deployment of an effective proof of concept, which drastically reduces the time to market imposed by the current state of the art.

In the Social Internet of Things paradigm, the Trust Management System computes trust values of involved social objects, identifies trusted relationships, and selects the most suitable object able to provide a target service. State-of-the-art mechanisms conceived to address these tasks generally avoid considering the actual availability of social objects and demand the implementation of complex algorithms to constrained nodes. This work presents a novel multi-tiered and fog-based Social Internet of Things architecture to solve these open issues, ensuring fast service provisioning, high scalability, fault tolerance, and security. On the one hand, the Trust Management System hosted at the first fog layer of the architecture jointly addresses the trustworthiness of service providers and monitors the re-source availability exposed by social objects, thus simplifying the forwarding of service requests to trusted and unloaded nodes. From another hand, to securely implement advanced services at a large scale, a second fog layer exploits a Blockchain-based storage for sharing services, relationships, and trust values across organizations and service domains. Computer simulations demonstrate the effectiveness of the proposed architecture in a realistic Social Internet of Things while showing the performance gain obtained against a baseline approach.

Satellite Communication is expected to play a leading role in 5G & Beyond networks. Current studies, however, focus the attention on physical and link-level aspects, while ignoring to evaluate system-level performance. This paper presents a novel simulation tool, conceived as a new module for the open-source 5G-air-simulator, modeling NB-IoT satellite-based communication systems. Specifically, it implements several link-to-system abstraction models, the cell selection procedure, and a configurable satellite constellation. Furthermore, these essential features are successfully integrated within the rest of the 5G-air-simulator, thus offering the opportunity to test flexible network deployments (e.g., by varying the number and the distribution of users) under different application statistics. To demonstrate the actual effectiveness of the developed tool, this work also presents a preliminary performance assessment of an NB-IoT satellite-based communication system enabling reference monitoring scenarios.

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.

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.

Multi-access Edge Computing represents a key enabling technology for emerging mobile networks. It offers intensive computational resources very close to the end-users, useful for task offloading purposes. Many scientific contributions already proposed approaches for optimally allocating these resources over time. However, most of them fail to take advantage of the prediction of both users’ mobility and service demands over a look-ahead temporal horizon. To bridge this gap, this paper formulates a novel methodology for anticipatorily allocating communication and computational resources at the network edge, based on the prediction of spatio-temporal dynamics of mobile users. The conceived architecture exploits a Software-Defined Networking approach to monitor users’ mobility, a Convolutional Long Short-Term Memory to predict over different look-ahead horizons the number of users within a given number of cells and their related service demands, and Dynamic Programming to optimally allocate users’ requests among available Multi-access Edge Computing servers. Computer simulations investigate the effectiveness of the proposed approach in a realistic autonomous driving use case and compare its behavior against a baseline solution. Obtained results demonstrate its unique ability to dynamically and fairly distribute users’ requests among the resources available at the network edge, while ensuring the targeted quality of service level.

The new generation of digital services are natively conceived as an ordered set of Virtual Network Functions, deployed across boundaries and organizations. In this context, security threats, variable network conditions, computational and memory capabilities and software vulnerabilities may significantly weaken the whole service chain, thus making very difficult to combat the newest kinds of attacks. It is thus extremely important to conceive a flexible (and standard-compliant) framework able to attest the trustworthiness and the reliability of each single function of a Service Function Chain. At the time of this writing, and to the best of authors knowledge, the scientific literature addressed all of these problems almost separately. To bridge this gap, this paper proposes a novel methodology, properly tailored within the ETSI-NFV framework. From one side, Software-Defined Controllers continuously monitor the properties and the performance indicators taken from networking domains of each single Virtual Network Function available in the architecture. From another side, a high-level orchestrator combines, on demand, the suitable Virtual Network Functions into a Service Function Chain, based on the user requests, targeted security requirements, and measured reliability levels. The paper concludes by further explaining the functionalities of the proposed architecture through a use case.

The Fifth Generation of mobile networks is emerging as a key enabler for Ultra Reliable and Low Latency Communications. However, to effectively design and provide safety-critical applications through mobile systems, many research issues still need to be deeply investigated. The most important ones include: (1) the dynamic and flexible management of radio resources of a new Radio Access Network jointly used by many virtual mobile operators, (2) the optimized and realtime configuration of network slices, and (3) the harmonious integration of Multi-access Edge Computing services. Starting from the efficient methodologies and solutions available in the current state of the art, this position paper sheds some important basis for the design of a comprehensive architecture enabling Radio Access Network slicing for Ultra Reliable and Low Latency Communications, including design criteria, system components and their baseline interactions, and critical open issues to be investigated in future research initiatives.

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.

NarrowBand Internet of Things (NB-IoT) is a novel radio interface proposed by the Third Generation Partnership Project for addressing the challenging requirements of Machine-Type Communications through a mobile network architecture. Although it natively promises an impressive battery lifetime to constrained devices, advanced methodologies that further reduce energy consumptions are still required. To this aim, this work investigates an extended NB-IoT architecture, embracing the cooperative relaying paradigm. It formulates an optimal relay selection algorithm that minimizes the overall amount of energy consumed in a NB-IoT cell. In addition, it proposes a greedy algorithm able to reach the same goal with a lower computational complexity. System level simulations clearly demonstrate that the adoption of the proposed cooperative relaying paradigm brings to an energy saving up to 30%. Moreover, the greedy approach registers energy consumptions which are only 10% higher than the optimal strategy.

While enabling brand new services and opportunities, the federation of vertical Internet of Things platforms presents new challenges in terms of secure and controlled access to heterogeneous resources, especially when authorization permissions must be regulated by multiple decentralized authorities. The work presented herein designs, develops, and experimentally validates a flexible and effective attribute-based access control framework, properly devised to operate in a federated and cloud-assisted cyber-physical system (CPS). Our main novelty stems in the original way we turn a policy-based encryption scheme, customarily used for accessing data, into a cyber-physical resource access control protocol. The proposed design approach is able to address several security issues characterizing the emerging use cases in this context, including the decoupling between authentication and authorization, fine-grained, offline, and time-limited authorization, protection against collusion attacks, access rights revocation, and user privacy. A security analysis and a performance evaluation executed through experimental tests clearly demonstrate the viability of the proposed approach in realistic cloud-assisted CPSs, as well as its ability to overcome the lacks affecting competitive approaches without introducing huge communication and computational requirements.

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.

We tackle the problem of identifying the trajectory of a moving radio source from Time of Arrival (TOA) measurements collected by a set of cooperating receivers. The considered system is completely asynchronous: nodes clocks are affected by unknown time and frequency offsets, and no control is exerted over packet transmission times. In the proposed solution, the receiver clock offset terms are estimated from TOA measurements on packets originated by non-cooperative reference transmitters, possibly but not necessarily coincidental with reference receivers. Transmission time ambiguity is resolved by exploiting the redundancy associated to the reception of the same packet at multiple receivers. A distinguishing feature of the proposed solution is that it seeks to identify the parameters of the trajectory as a whole, rather than the individual points of transmission as done in traditional point-based approaches. This allows the effective exploitation of TOA measurements collected in lossy scenarios, where the generic packet is received by a smaller subset of the available receivers (at least two). For the problem at hand, we provide distinct estimators based on TOA and Time-Difference of Arrival (TDOA) and prove their equivalence. Numerical results from simulations and from a real WiFi testbed are provided to validate the effectiveness of the proposed method.

While the Internet of Things is breaking into the market, the controlled access to constrained resources still remains a blocking concern. Unfortunately, conventional solutions already accepted for both web and cloud applications cannot be directly used in this context. In fact, they generally require high computational and bandwidth capabilities (that are impossible to reach with constrained devices) and offer poor interoperability against standardized communication protocols for the Internet of Things. To solve this issue, this contribution presents a flexible authentication and authorization framework for the Internet of Things, namely OAuth-IoT. It leverages and properly harmonizes existing open-standards (including the OAuth 2.0 authorization framework, different token formats, and the protocol suite for the Internet of Things tailored by the Internet Engineering Task Force), while carefully taking into account the limited capabilities of constrained devices. Functionalities and benefits offered by OAuth-IoT are pragmatically shown by means of an experimental testbed, and further demonstrated with a very preliminary performance assessment.

Self-adaptive systems are modern applications in which the running software system should be able to react on its own, by dynamically adapting its behavior, for sustaining a required set of qualities of service, and dynamic changes in the context or in user requirements. They are typically involved in Future Internet development such as the Internet of Things where interoperability, flexibility, and adaptability are key requirements. Convergence of contents, services, things and networks seems to be the cornerstone to fullfil these requirements. We propose a reflective approach to provide a common abstraction for automating the deployment of component based applications in the Internet of Things environment. The proposed framework allows the design of heterogeneous, distributed, and adaptive applications built on the component based software engineering paradigm. The framework considers a metamodel instantiated in a Rest middleware properly modified for allowing different implementations by using reflective design patterns. We are currently working to refine the framework metamodel and to validate it in several implementation domains.

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%).

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.

In the recent past, we witnessed to a dramatic growth of networks with a huge number of interconnected wireless nodes exchanging large amounts of information. This has brought to the need of ad-hoc, energy-aware protocols suitable for low-power and lossy networks. Among these, Routing Protocol for Low Power and Lossy Networks (RPL) is surely one of the most interesting ones. It chooses the optimal routes from a source to a destination node, based on specific metrics. In this work we present a RPL compliant composite metric that considers both reliability and energy. Its aim is twofold. First, it realizes energy consumption balancing, so that each node tends to consume the same amount of energy of all the others, thus prolonging the overall network lifetime. Second, it takes into account data reliability along the paths, expressed by the well known Expected Transmission Count (ETX). The effectiveness of the proposed metric is confirmed by some interesting simulation results, that strongly encourage a deeper investigation on this issue.

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.

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.

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.

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.

Recent advances in information and communication technologies have made multimedia-on-demand services technically and economically feasible. Important aspects of such systems are the resource sharing techniques, which allow the simultaneous service of a large number of users with considerable savings in terms of network bandwidth and server resources. In this paper, we report the results of a study which analyzes batching and buffering techniques, which involves serving all video requests issued during a short interval of time with a single stream. The mathematical model, based on queueing networks, allows the evaluation of the main system performance (average and probability distribution of the number of streams, percentage reduction of resources, and so on) as a function of load and batching interval duration. Simulation experiments confirm the analytical model in the whole range of considered conditions.

The main aspect of several multimedia applications is the transmission of variable bit rate (VBR) video streams requiring high bandwidth with stringent quality of service (QoS) guarantee. In such systems, a statistical bandwidth estimation is relevant for network infrastructure planning and admission control. In this paper, an original algorithm for aggregate bandwidth estimation, needed by a given number of correlated video streams, is proposed and analyzed. The obtained analytical results are validated by simulation and compared with other numerical results already present in the literature, discussing the effectiveness of the proposed solution.

An actual trend in the telecommunication world is the proposal of a universal telecommunication system deriving from the convergence between mobile telephony and the Internet. Such a system will presumably exploit both wired and wireless parts, a network platform totally based on a TCP/IP architecture. However, the encapsulation process of TCP/IP layers produces packets whose payload size, for particular services, is a small percentage of the whole packet size (high overhead-services), so that, a significant part of radio channel bandwidth (the most expensive and limited resource of the whole wireless system) is used for header transmission. For these reasons it is of primary importance for the adoption of a header compression scheme to be able to reduce the protocol overhead with the aim to make economically feasible and physically realizable the implementation of high overhead-services. In this paper a new header compression scheme for TCP streams, as a specific header compression profile within the ROHC platform, is proposed and analyzed. This new header compression scheme is based on the distinct management of data and acknowledgment streams associated to the TCP stream. To reduce error propagation, a robust encoding technique (W-LSB) and the synergy between three repair mechanisms for damaged compressed packets have been suggested. Thus, headers up to 40-60 bytes can be compressed to 4-6 bytes with great benefits on cost and quality of service (QoS).

Future cellular mobile communication networks will exploit microcellular architectures and dynamic channel allocation in order to meet the rapidly increasing traffic demand. In this paper, an analytical model has been developed in order to evaluate the performance of maximum packing, a dynamic channel allocation scheme for cellular communication networks. Specifically, a finite number of users has been assumed, moving in a geographical region, covered by a finite set of cells. The considered users are characterized by a variable degree of mobility, which allows both variable sized cells and different user speeds to be analyzed. The model, based on queueing networks, allows the evaluation of the main system performance parameters in terms of blocking probability of new calls, handoff blocking probability, forced termination probability, unsuccessful probability, and throughput. Performance predictions are confirmed by simulation in a wide range of load conditions and user mobility.

Future telecommunication systems will integrate "wireline" and "wireless" networks in a single advanced infrastructure, and it is expected that such systems will exploit asynchronous transfer mode (ATM) technology. This paper considers some aspects of this integrated telecommunication infrastructure. In particular, we propose and analyze an improvement, for wired networks, of the virtual connection tree (VCT) concept, while for the wireless part, a new model for the analysis of a dynamic channel allocation scheme is presented. The obtained analytical results, confirmed by simulation in a wide range of load conditions and user mobility, allow us to evaluate the main system performance parameters, in terms of blocking probability of new calls, handoff blocking probability and forced termination probability.