Paraoxonase enzymes serve as an important physiological redox system that participates in the protection against cellular injury caused by oxidative stress. The PON enzymes family consists of three members (PON-1, PON-2, and PON-3) that share a similar structure and location as a cluster on human chromosome 7. These enzymes exhibit anti-inflammatory and antioxidant properties with well-described roles in preventing cardiovascular disease. Perturbations in PON enzyme levels and their activity have also been linked with the development and progression of many neurological disorders and neurodegenerative diseases. The current review summarizes the available evidence on the role of PONs in these diseases and their ability to modify risk factors for neurological disorders. We present the current findings on the role of PONs in Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative and neurological diseases.

The establishment of convenient and reliable cryptographic keys is not always feasible, especially in low-powered wireless devices where a complex key management infrastructure is unaffordable. Physical layer-based key generation approaches, on the other hand, allow two legitimate users to establish a common secret key by exploiting the parameters of the wireless channel, such as the underlying impulse response. In this paper, a fundamental bound on the maximum achievable key generation rate (KGR) over wireless fading channels with asymmetric additive white Gaussian noise (AWGN) is derived. A Nakagamim fading channel is considered, and the effect of non-reciprocity on the forward and reverse channels correlation is demonstrated. We validate the theoretical platform through simulations using a key generation protocol based on the level crossing rate (LCR) of the fading process. The proposed LCR protocol models the Nakagami channel by utilizing the sum-of-sinusoids approach, where Doppler shift, fading severity, and number-of-paths are taken into account. The proposed protocol also incorporates a two-level quantizer to extract keys, where the channel estimates between a wireless transmitter and receiver can be used as the basis. Besides providing an upper bound on the KGR, the analytical and simulation results intertwine the effect of channel reciprocity with the key generation process.

In this work, we propose a a physical layer security (PLS) scheme for orthogonal frequency-division multiplexing (OFDM) based transceivers, termed as link-signature-based discriminatory channel estimation (LS-DCE). The proposed scheme exploits the space-time uniqueness of the channel responses (i.e. link-signatures) at different transceiver pairs to facilitate a reliable signal reception at legitimate receivers, while equivocating reception at non-legitimate adversaries. While most PLS studies focus on transmitted data to provide such an equivocation, LS-DCE focuses on channel estimations. Since the quality of estimates impacts the overall performance, LS-DCE facilitates distinctive detection error probabilities by enforcing a poor channel estimation at non-legitimate receivers as compared to legitimate ones. To validate the effectiveness of the proposed scheme, analytical, simulated, and experimental evaluations are performed. First, a theoretical analysis is established in terms of the probability of bit error (PBE) at the legitimate and non-legitimate receivers. Then, indoor/outdoor stochastic and deterministic channel models are adopted using correlated Nakagami-m simulation model and software defined radio (SDR) testbed, respectively. With —10dB signal-to-noise ratio (SNR), it is found that a spatial correlation ≤0.1 between the legitimate and non-legitimate channels results in an average PBE of 0.48, 0.48, and 0.49 for BPSK, QPSK, and 16-QAM, respectively, at the non-legitimate receiver; and 0.45, 0.46, and 0.48 with 50 dB SNR.

The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter–receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers.