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Author: Awan, Maaz AliHas files: false

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    An Overview of Challenges To Long-Term Sustainability and Scalability of Radio Frequency Fingerprinting
    (IEEE, 2024) Demiroglu, Harun Senol; Awan, Maaz Ali; Kara, Ali
    Internet of Things (IoT) technology has become ubiquitous with a broad spectrum of applications. This vast penetration entails formidable cyber-security for the stable operation of the associated systems. Most inexpensive IoT devices employ rudimentary cryptographic security mechanisms due to their resource-limited architecture. Radio frequency fingerprinting (RFF) is a physical layer security mechanism that leverages hardware impairments for authentication and device classification. To this end, its scope has been limited to academia owing to daunting challenges. In this work, an abridged overview of the state-of-the-art is provided, along with a summary of the challenges that hinder progress toward practical applications. The article culminates with a discussion on the intricacies of performance metrics in RFF and the direction for future research.
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    Citation - WoS: 2
    Citation - Scopus: 2
    Advancing Mmwave Altimetry for Unmanned Aerial Systems: a Signal Processing Framework for Optimized Waveform Design
    (Mdpi, 2024) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali; Derawi, Mohammad
    This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input-multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77-81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.
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    Citation - WoS: 1
    Citation - Scopus: 1
    Optimizing Radio Frequency Fingerprinting for Device Classification: a Study Towards Lightweight Dl Models
    (IEEE, 2024) Iyiparlakoglu, Raif; Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali
    As the Internet of Things (IoT) permeates diverse application domains, ensuring the security of wireless networks has become increasingly critical. However, the constraints of resource-limited IoT devices render complex encryption impractical. Consequently, Radio Frequency Fingerprinting (RFF) has emerged as a promising avenue, leveraging unique device characteristics resulting from manufacturing nonlinearities. RFF enhances physical layer security by enabling device classification and authentication at IoT gateways. While deep learning (DL) aided RFF systems offer exceptional classification accuracy, their deployment on edge devices remains challenging to this end. Accordingly, there is a gap in the literature for efficient model exploration and implementation. This study proposes a lightweight Convolutional Neural Network (CNN) model using 1D convolutional filters to reduce inference latency. The model was applied to an open-source dataset comprising 30 LoRa devices. An evaluation was conducted to compare classification accuracy and inference latency using Short Time Fourier Transform (STFT) and Fast Fourier Transform (FFT) for preprocessing. Additionally, the performance of the proposed model was compared against a CNN model utilizing 2D convolutional filters. The model exhibited a significant reduction in inference latency with miniscule degradation in classification accuracy, addressing the identified gap, and propelling the academic discourse towards RFF for edge devices.