374 research outputs found

    Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)

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    Intelligent transportation systems contribute to improved traffic safety by facilitating real time communication between vehicles. By using wireless channels for communication, vehicular networks are susceptible to a wide range of attacks, such as impersonation, modification, and replay. In this context, securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything (V2X) communication, constitutes a technological challenge that needs to be addressed. Hence, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. In this challenging scenario, physical (PHY)-layer authentication has gained popularity, which involves leveraging the inherent characteristics of wireless channels and the hardware imperfections to discriminate between wireless devices. However, PHY-layerbased authentication cannot be an alternative to crypto-based methods as the initial legitimacy detection must be conducted using cryptographic methods to extract the communicating terminal secret features. Nevertheless, it can be a promising complementary solution for the reauthentication problem in VANETs, introducing what is known as “cross-layer authentication.” This thesis focuses on designing efficient cross-layer authentication schemes for VANETs, reducing the communication and computation overheads associated with transmitting and verifying a crypto-based signature for each transmission. The following provides an overview of the proposed methodologies employed in various contributions presented in this thesis. 1. The first cross-layer authentication scheme: A four-step process represents this approach: initial crypto-based authentication, shared key extraction, re-authentication via a PHY challenge-response algorithm, and adaptive adjustments based on channel conditions. Simulation results validate its efficacy, especially in low signal-to-noise ratio (SNR) scenarios while proving its resilience against active and passive attacks. 2. The second cross-layer authentication scheme: Leveraging the spatially and temporally correlated wireless channel features, this scheme extracts high entropy shared keys that can be used to create dynamic PHY-layer signatures for authentication. A 3-Dimensional (3D) scattering Doppler emulator is designed to investigate the scheme’s performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the scheme’s capability to support high detection probability for an acceptable false alarm value ≤ 0.1 at SNR ≥ 0 dB and speed ≤ 45 m/s. 3. The third proposal: Reconfigurable intelligent surfaces (RIS) integration for improved authentication: Focusing on enhancing PHY-layer re-authentication, this proposal explores integrating RIS technology to improve SNR directed at designated vehicles. Theoretical analysis and practical implementation of the proposed scheme are conducted using a 1-bit RIS, consisting of 64 × 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = − 6 dB for multicarrier communications. 4. The fourth proposal: RIS-enhanced vehicular communication security: Tailored for challenging SNR in non-line-of-sight (NLoS) scenarios, this proposal optimises key extraction and defends against denial-of-service (DoS) attacks through selective signal strengthening. Hardware implementation studies prove its effectiveness, showcasing improved key extraction performance and resilience against potential threats. 5. The fifth cross-layer authentication scheme: Integrating PKI-based initial legitimacy detection and blockchain-based reconciliation techniques, this scheme ensures secure data exchange. Rigorous security analyses and performance evaluations using network simulators and computation metrics showcase its effectiveness, ensuring its resistance against common attacks and time efficiency in message verification. 6. The final proposal: Group key distribution: Employing smart contract-based blockchain technology alongside PKI-based authentication, this proposal distributes group session keys securely. Its lightweight symmetric key cryptography-based method maintains privacy in VANETs, validated via Ethereum’s main network (MainNet) and comprehensive computation and communication evaluations. The analysis shows that the proposed methods yield a noteworthy reduction, approximately ranging from 70% to 99%, in both computation and communication overheads, as compared to the conventional approaches. This reduction pertains to the verification and transmission of 1000 messages in total

    IoT Transmission Technologies for Distributed Measurement Systems in Critical Environments

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    Distributed measurement systems are spread in the most diverse application scenarios, and Internet of Things (IoT) transmission equipment is usually the enabling technologies for such measurement systems that need to feature wireless connectivity to ensure pervasiveness. Because wireless measurement systems have been deployed for the last years even in critical environments, assessing transmission technologies performances in such contexts is fundamental. Indeed, they are the most challenging ones for wireless data transmission due to their intrinsic attenuation capabilities. Several scenarios in which measurement systems can be deployed are analysed. Firstly, marine contexts are treated by considering above-the-sea wireless links. Such setting can be experienced in whichever application requiring remote monitoring of facilities and assets that are offshore installed. Some instances are offshore sea farming plants, or remote video monitoring systems installed on seamark buoys. Secondly, wireless communications taking place from the underground to the aboveground are covered. This scenario is typical of precision agriculture applications, where the accurate measurement of underground physical parameters is needed to be remotely sent to optimise crops reducing the wastefulness of fundamental resources (e.g., irrigation water). Thirdly, wireless communications occurring from the underwater to the abovewater are addressed. Such situation is inevitable for all those infrastructures monitoring conservation status of underwater species like algae, seaweeds and reef. Then, wireless links happening traversing metal surfaces and structures are tackled. Such context is commonly encountered in asset tracking and monitoring (e.g., containers), or in smart metering applications (e.g., utility meters). Lastly, sundry harsh environments that are typical of industrial monitoring (e.g., vibrating machineries, harsh temperature and humidity rooms, corrosive atmospheres) are tested to validate pervasive measurement infrastructures even in such contexts that are usually experienced in Industrial Internet of Things (IIoT) applications. The performances of wireless measurement systems in such scenarios are tested by sorting out ad-hoc measurement campaigns. Finally, IoT measurement infrastructures respectively deployed in above-the-sea and underground-to-aboveground settings are described to provide real applications in which such facilities can be effectively installed. Nonetheless, the aforementioned application scenarios are only some amid their sundry variety. Indeed, nowadays distributed pervasive measurement systems have to be thought in a broad way, resulting in countless instances: predictive maintenance, smart healthcare, smart cities, industrial monitoring, or smart agriculture, etc. This Thesis aims at showing distributed measurement systems in critical environments to set up pervasive monitoring infrastructures that are enabled by IoT transmission technologies. At first, they are presented, and then the harsh environments are introduced, along with the relative theoretical analysis modelling path loss in such conditions. It must be underlined that this Thesis aims neither at finding better path loss models with respect to the existing ones, nor at improving them. Indeed, path loss models are exploited as they are, in order to derive estimates of losses to understand the effectiveness of the deployed infrastructure. In fact, some transmission tests in those contexts are described, along with providing examples of these types of applications in the field, showing the measurement infrastructures and the relative critical environments serving as deployment sites. The scientific relevance of this Thesis is evident since, at the moment, the literature lacks a comparative study like this, showing both transmission performances in critical environments, and the deployment of real IoT distributed wireless measurement systems in such contexts

    PHY Layer Anonymous Precoding: Sender Detection Performance and Diversity-Multiplexing Tradeoff

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    Departing from traditional data security-oriented designs, the aim of anonymity is to conceal the transmitters’ identities during communications to all possible receivers. In this work, joint anonymous transceiver design at the physical (PHY) layer is investigated. We first present sender detection error rate (DER) performance analysis, where closed-form expression of DER is derived for a generic precoding scheme applied at the transmitter side. Based on the tight DER expression, a fully DER-tunable anonymous transceiver design is demonstrated. An alias channel-based combiner is first proposed, which helps the receiver find a Euclidean space that is close to the propagation channel of the received signal for high quality reception, but does not rely on the recognition of the real sender’s channel. Then, two novel anonymous precoders are proposed under a given DER requirement, one being able to provide full multiplexing performance, and the other flexibly adjusting the number of multiplexing streams with further consideration of the receive-reliability. Simulation demonstrates that the proposed joint transceiver design can always guarantee the subscribed DER performance, while well striking the trade-off among the multiplexing, diversity and anonymity performance

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    BLE-based Indoor Localization and Contact Tracing Approaches

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    Internet of Things (IoT) has penetrated different aspects of modern life with smart sensors being prevalent within our surrounding indoor environments. Furthermore, dependence on IoT-based Contact Tracing (CT) models has significantly increased mainly due to the COVID-19 pandemic. There is, therefore, an urgent quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions leveraging accurate indoor localization/tracking approaches. In this context, the first objective of this Ph.D. thesis is to enhance accuracy of Bluetooth Low Energy (BLE)-based indoor localization. BLE-based localization is typically performed based on the Received Signal Strength Indicator (RSSI). Extreme fluctuations of the RSSI occurring due to different factors such as multi-path effects and noise, however, prevent the BLE technology to be a reliable solution with acceptable accuracy for dynamic tracking/localization in indoor environments. In this regard, first, an IoT dataset is constructed based on multiple thoroughly separated indoor environments to incorporate the effects of various interferences faced in different spaces. The constructed dataset is then used to develop a Reinforcement Learning (RL)-based information fusion strategy to form a multiple-model implementation consisting of RSSI, Pedestrian dead reckoning (PDR), and Angle-of-Arrival (AoA)-based models. In the second part of the thesis, the focus is devoted to application of multi-agent Deep Neural Networks (DNN) models for indoor tracking. DNN-based approaches are, however, prone to overfitting and high sensitivity to parameter selection, which results in sample inefficiency. Moreover, data labelling is a time-consuming and costly procedure. To address these issues, we leverage Successor Representations (SR)-based techniques, which can learn the expected discounted future state occupancy, and the immediate reward of each state. A Deep Multi-Agent Successor Representation framework is proposed that can adapt quickly to the changes in a multi-agent environment faster than the Model-Free (MF) RL methods and with a lower computational cost compared to Model-Based (MB) RL algorithms. In the third part of the thesis, the developed indoor localization techniques are utilized to design a novel indoor CT solution, referred to as the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) approach coupled with a Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms

    Pilot assignment and power control in secure UAV-enabled cell-free massive MIMO networks

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    This paper investigates the pilot assignment and power control problems for secure UAV communications in cell-free massive MIMO network with the user-centric scheme, where numerous distributed access points (APs) simultaneously serve multiple UAVs and terminal users. Meanwhile, there exists one UAV acting as an eavesdropper which can perform pilot spoofing attack. Considering a mixture of Rayleigh and Ricean fading channels, the APs respectively perform MMSE estimation and distributed conjugate beamforming for uplink training and downlink data transmission. Using random matrix theory, the closed-form expression for a tight lower bound on the achievable secrecy rate is derived, which enables the impact analysis of key parameters, such as power, antenna configuration, UAV height, etc. Taking into account both performance and complexity, a novel pilot assignment scheme is proposed by combining weighted graphic framework and genetic algorithm, which can actualize global search with limited iterations. The max-min power control with security constraints is then studied in parallel, which can not only enhance the network fairness but also ensure the security. Accordingly, successive convex approximation and fractional optimization are jointly utilized to solve this non-convex problem. Simulation results numerically verify the analytical results and indicate the superiority of the proposed pilot assignment and power control schemes

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    D4.2 Intelligent D-Band wireless systems and networks initial designs

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    This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

    Future Wireless Networks: Towards Learning-driven Sixth-generation Wireless Communications

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    The evolution of wireless communication networks, from present to the emerging fifth-generation (5G) new radio (NR), and sixth-generation (6G) is inevitable, yet propitious. The thesis evolves around application of machine learning and optimization techniques to problems in spectrum management, internet-of-things (IoT), physical layer security, and intelligent reflecting surface (IRS). The first problem explores License Assisted Access (LAA), which leverages unlicensed resource sharing with the Wi-Fi network as a promising technique to address the spectrum scarcity issue in wireless networks. An optimal communication policy is devised which maximizes the throughput performance of LAA network while guaranteeing a proportionally fair performance among LAA stations and a fair share for Wi-Fi stations. The numerical results demonstrate more than 75 % improvement in the LAA throughput and a notable gain of 8-9 % in the fairness index. Next, we investigate the unlicensed spectrum sharing for bandwidth hungry diverse IoT networks in 5G NR. An efficient coexistence mechanism based on the idea of adaptive initial sensing duration (ISD) is proposed to enhance the diverse IoT-NR network performance while keeping the primary Wi-Fi network's performance to a bearable threshold. A Q-learning (QL) based algorithm is devised to maximize the normalized sum throughput of the coexistence Wi-Fi/IoT-NR network. The results confirm a maximum throughput gain of 51 % and ensure that the Wi-Fi network's performance remains intact. Finally, advanced levels of network security are critical to maintain due to severe signal attenuation at higher frequencies of 6G wireless communication. Thus, an IRS-based model is proposed to address the issue of network security under trusted-untrusted device diversity, where the untrusted devices may potentially eavesdrop on the trusted devices. A deep deterministic policy gradient (DDPG) algorithm is devised to jointly optimize the active and passive beamforming matrices. The results confirm a maximum gain of 2-2.5 times in the sum secrecy rate of trusted devices and ensure Quality-of-Service (QoS) for all the devices. In conclusion, the thesis has led towards efficient, secure, and smart communication and build foundation to address similar complex wireless networks
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