— Wi-Fi sensing applications have achieved remarkable results over the last decade, offering accurate device-free localization and gesture recognition capabilities. Indeed, Wi-Fi sensing has quickly become a critical field of research for future communication systems under the paradigm known as joint communication and sensing. However, device-free wireless sensing can also be exploited for malign purposes against unaware victims, and the omnipresence of Wi-Fi transceivers poses a significant threat to people’s privacy. Therefore, it is essential to develop functional solutions that can effectively thwart wireless sensing. All the current attempts to hinder illegitimate wireless sensing rely on specialized hardware deployed in the environment, but their cost and complexity can undermine widespread deployment. In this paper, we explore the possibility of using native capabilities of Wi-Fi systems, namely beamforming, to thwart wireless sensing. To this end, we propose for the first time a solution that enables complete control over the beamforming in commercial Wi-Fi devices. On top of that, we build BeamDancer, which randomizes beamforming vectors to inhibit channel fingerprinting. We empirically demonstrate the effectiveness of the proposed solution against three different wireless sensing techniques, both data-driven and model-based, while preserving almost entirely the legitimate Wi-Fi traffic at the same time.

The high directionality of millimeter-wave (mmWave) communication systems has proven effective in reducing the attack surface against eavesdropping, thus improving the physical layer security. However, even with highly directional beams, the system is still exposed to eavesdropping against adversaries located within the main lobe. In this paper, we propose BeamSec, a solution to protect the users even from adversaries located in the main lobe. The key feature of BeamSec are: (i) Operating without the knowledge of eavesdropper's location/channel; (ii) Robustness against colluding eavesdropping attack and (iii) Standard compatibility, which we prove using experiments via our IEEE 802.11ad/ay-compatible 60 GHz phased-array testbed. Methodologically, BeamSec first identifies uncorrelated and diverse beampairs between the transmitter and receiver by analyzing signal characteristics available through standard-compliant procedures. Next, it encodes the information jointly over all selected beampairs to minimize information leakage. We study two methods for allocating transmission time among different beams, namely uniform allocation (no knowledge of the wireless channel) and optimal allocation for maximization of the secrecy rate (with partial knowledge of the wireless channel). Our experiments show that BeamSec outperforms the benchmark schemes against single and colluding eavesdroppers and enhances the secrecy rate by 79.8% over a random paths selection benchmark.

Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense self-backhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.

The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric (radio frequency) fingerprint. This fingerprint can be used as an additional means of authentication to enhance security. In this paper, we prove analytically and experimentally that these solutions are highly vulnerable to impersonation attacks. We also demonstrate that such a unique device-specific signature can be abused to track devices, thus violating privacy. We propose RF-Veil, a radiometric fingerprinting solution that is not only robust against impersonation attacks but also effective in protecting privacy by obfuscating the radiometric fingerprint of the transmitter for non-legitimate receivers. Specifically, we introduce a randomized pattern of phase errors to the transmitted signal such that only the intended receiver can extract the original fingerprint of the transmitter. In a series of experiments and analyses, we expose the vulnerability of adopting naive randomization to statistical attacks and introduce countermeasures. Finally, we show the efficacy of RF-Veil experimentally in protecting user privacy and enhancing security. More importantly, our proposed solution allows communicating with other devices, which do not employ RF-Veil.

IoT devices penetrate different aspects of our life including critical services, such as health monitoring, public safety, and autonomous driving. Such safety-critical IoT systems often consist of a large number of devices and need to withstand a vast range of known Denial-of-Service (DoS) network attacks to ensure a reliable operation while offering low-latency information dissemination. As the first solution to jointly achieve these goals, we propose LIDOR, a secure and lightweight multihop communication protocol designed to withstand all known variants of packet dropping attacks. Specifically, LIDOR relies on an end-to-end feedback mechanism to detect and react on unreliable links and draws solely on efficient symmetric-key cryptographic mechanisms to protect packets in transit. We show the overhead of LIDOR analytically and provide the proof of convergence for LIDOR which makes LIDOR resilient even to strong and hard-to-detect wormhole-supported grayhole attacks. In addition, we evaluate the performance via testbed experiments. The results indicate that LIDOR improves the reliability under DoS attacks by up to 91% and reduces network overhead by 32% compared to a state-of-the-art benchmark scheme.

The complexity of the mode selection and resource allocation (MSRA) problem has hampered the commercialization progress of Device-to-Device (D2D) communication in 5G networks. Furthermore, the combinatorial nature of MSRA has forced the majority of existing proposals to focus on constrained scenarios or offline solutions to contain the size of the problem. Given the real-time constraints in actual deployments, a reduction in computational complexity is necessary. Adaptability is another key requirement for mobile networks that are exposed to constant changes such as channel quality fluctuations and mobility. In this article, we propose an online learning technique (i.e., CBMoS) which leverages combinatorial multi-armed bandits (CMAB) to tackle the combinatorial nature of MSRA. Furthermore, our two-stage CMAB design results in a tight model, which eliminates the theoretically feasible but practicality invalid options from the solution space. We prototype the first SDR-based D2D testbed to verify the performance of CBMoS under real-world conditions. The simulations confirm that the fast learning speed of CBMoS leads to outperforming the benchmark schemes by up to 132%. In experiments, CBMoS exhibits even higher performance (up to 142%) than in the simulations. This stems from the adaptability/fast learning speed of CBMoS in presence of high channel dynamics which cannot be captured via statistical channel models used in the simulators.

Millimeter-wave (mmWave) backhauling is key to ultra-dense deployments in beyond-5G networks because providing every base station with a dedicated fiber-optic backhaul link to the core network is technically too complicated and economically too costly. Self-backhauling allows the operators to provide fiber connectivity only to a small subset of base stations (Fiber-BSs), whereas the rest of the base stations reach the core network via a (multi-hop) wireless link towards the Fiber-BS. Although a very attractive architecture, self-backhauling is proven to be an NP-hard route selection and resource allocation problem. The existing self-backhauling solutions lack practicality because: (i) they require solving a fairly complex combinatorial problem every time there is a change in the network (e.g., channel fluctuations), or (ii) they ignore the impact of network dynamics which are inherent to mobile networks. In this article, we propose SCAROS which is a semi-distributed learning algorithm that aims at minimizing the end-to-end latency as well as enhancing the robustness against network dynamics including load imbalance, channel variations, and link failures. We benchmark SCAROS against state-of-the-art approaches under a real-world deployment scenario in Manhattan and using realistic beam patterns obtained from off-the-shelf mmWave devices. The evaluation demonstrates that SCAROS achieves the lowest latency, at least 1.8 × higher throughput, and the highest flexibility against variability or link failures in the system.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose an online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely fast machine learning (FML), with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates the received data to learn from and adapt to its environment. Furthermore, we demonstrate the feasibility of a real-world implementation of FML by proposing a standard-compliant protocol based on the existing architecture of cellular networks and the forthcoming features of 5G. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 mins of deployment by learning from the available context. Moreover, FML remains within 5% of the optimal performance by swift adaptation to system changes (i.e., blockage, traffic).

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios, and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose the first online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely FML, with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates received data to learn from and adapt to its environment. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 minutes of deployment by learning from the available context. Moreover, FML remains within 5% of the optimal performance by swift adaptation to system changes such as blockage and traffic.

The directionality of millimeter-Wave (mm-Wave) communication results in challenging network dynamics and thus complex system design. A key problem with such networks is human blockage, which is highly detrimental since absorption at mm-Wave frequencies is extremely high. This poses a significant challenge for the state-of-the-art technologies in 5G networks such as Device-to-Device (D2D) communication. Essentially, the aforementioned dynamics hinder direct communication between devices. Existing protocols in the mm-Wave band such as IEEE 802.11ad address this problem using relays. However, the complexity relay discovery in these protocols grows linearly with the number of users, Hence, these approaches are infeasible for crowded areas such as malls or busy pedestrian streets. In this paper, we present a lightweight relaying mechanism called Opp-Relay that builds on the existing D2D features of the 3GPP standard to opportunistically discover an mm-Wave enabled relay. Specifically, we provide an algorithm to compute the optimal beamwidth for opportunistic discovery of a relay in dense and dynamic network environments. We validate our approach in practice using our experimental testbed operating in the 60 GHz band. Our experiments demonstrate that choosing a suitable beamwidth to discover and communicate with a relay node is crucial. Moreover, we show that our relaying mechanism significantly reduces the complexity of relay discovery.

Multi-hop Device-to-Device (D2D) communication has emerged as a key enabler for public safety applications in 5G networks. A failure of these applications would be catastrophic since they control vital human-in-the-loop services. As a result, it is of utmost importance to identify and mitigate security risks prior to commercial deployment. To this end, we devise SEMUD, the first secure multi-hop D2D solution for 5G mobile networks. It enables robust and secure communication even in the absence of supporting infrastructure. We design SEMUD to be compliant with 3GPP Proximity-based Services, the standard to implement D2D communications. We implement SEMUD in the ns-3 simulator and our testbed. Via extensive simulation, we assert its resilience against strong adversaries and attacks. Furthermore, we show the energy efficiency and achievable throughput of our proposal on real devices.

At vehicular speeds, the contact time during which a mobile node is in range of a fixed road side unit (RSU) is short. While this is not an issue if the RSU only needs to deliver textual information such as traffic updates, short contact times become problematic when transmitting a large amount of information. For instance, an RSU may need to deliver high volumes of local navigation data for an augmented reality application, or video material regarding tourist information of a nearby town. Millimeter-wave (mm-Wave) communication is highly promising for such scenarios since it provides order-of-magnitude larger throughput than the existing technologies operating at lower frequencies. However, the contact time in mm-Wave vehicular scenarios becomes even shorter due to the directional nature of the communication. This raises a fundamental question: can the high throughput of mm-Wave make up for the reduction in the contact time? In this paper, we analyze this trade-off and design a first-of-its-kind practical mm-Wave vehicular testbed to evaluate the resulting performance. Specifically, we consider alternative locations for the RSU other than at the side of the road, such as on top of a bridge or inside a roundabout. Moreover, we leverage that the road implicitly determines the direction in which the RSU expects a car to be located. This allows us to use fixed beam-steering at both the car and the RSU, thus avoiding costly beam-training. We validate our approach in real-world vehicular scenarios with actual traffic in a mid-sized town in Spain. The results show that our fixed beam-steering approach enables the RSU to transmit large amounts of data in a very short amount of time for a wide range of speeds. This allows us to provide detailed insights into the aforementioned fundamental question regarding the use of mm-Wave in vehicular scenarios.