Challenges of Radio Frequency Fingerprinting: From Data Collection to Deployment

Radio Frequency Fingerprinting (RFF) techniques promise to authenticate wireless devices at the physical layer based on inherent hardware imperfections introduced during manufacturing. Such RF transmitter imperfections are reflected into over-the-air signals, allowing receivers to accurately identify the RF transmitting source. Recent advances in Machine Learning, particularly in Deep Learning (DL), have improved the ability of RFF systems to extract and learn complex features that make up the device-specific fingerprint. However, integrating DL techniques with RFF and operating the system in real-world scenarios presents numerous challenges. This article identifies and analyzes these challenges while considering the three reference phases of any DL-based RFF system: (i) data collection and preprocessing, (ii) training, and finally, (iii) deployment. Our investigation points out the current open problems that prevent real deployment of RFF while discussing promising future directions, thus paving the way for further research in the area.Comment: 7 pages, 1 table, and 4 figure

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation

Doctor of Philosophy

dissertationCross layer system design represents a paradigm shift that breaks the traditional layer-boundaries in a network stack to enhance a wireless network in a number of di erent ways. Existing work has used the cross layer approach to optimize a wireless network in terms of packet scheduling, error correction, multimedia quality, power consumption, selection of modulation/coding and user experience, etc. We explore the use of new cross layer opportunities to achieve secrecy and e ciency of data transmission in wireless networks. In the rst part of this dissertation, we build secret key establishment methods for private communication between wireless devices using the spatio-temporal variations of symmetric-wireless channel measurements. We evaluate our methods on a variety of wireless devices, including laptops, telosB sensor nodes, and Android smartphones, with diverse wireless capabilities. We perform extensive measurements in real-world environments and show that our methods generate high entropy secret bits at a signi cantly faster rate in comparison to existing approaches. While the rst part of this dissertation focuses on achieving secrecy in wireless networks, the second part of this dissertation examines the use of special pulse shaping lters of the lterbank multicarrier (FBMC) physical layer in reliably transmitting data packets at a very high rate. We rst analyze the mutual interference power across subcarriers used by di erent transmitters. Next, to understand the impact of FBMC beyond the physical layer, we devise a distributed and adaptive medium access control protocol that coordinates data packet tra c among the di erent nodes in the network in a best e ort manner. Using extensive simulations, we show that FBMC consistently achieves an order-of-magnitude performance improvement over orthogonal frequency division multiplexing (OFDM) in several aspects, including packet transmission delays, channel access delays, and e ective data transmission rate available to each node in static indoor settings as well as in vehicular networks

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research