Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey.

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end-users as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacy-related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures by incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities for FL in IoMT. In the end, we conclude this survey by discussing open research issues and challenges while using FL in future smart healthcare systems.

Secure lightweight cryptosystem for IoT and pervasive computing.

Large volumes of sensitive data are being transferred among devices as the Internet of Things (IoT) grows in popularity. As a result, security measures must be implemented to ensure that unauthorized parties do not obtain access to the data. It is well acknowledged that IoT devices have restricted resources, such as limited battery life, memory, and hence reaction time. Classical encryption approaches and methods become inefficient for IoT devices due to memory limits. Large volumes of sensitive data are being transferred between devices as the Internet of Things (IoT) grows in popularity. This involves the implementation of security safeguards to ensure that unauthorized parties do not obtain access to the data. IoT devices are notorious for having limited resources, such as battery life, memory, and hence response time. Classical encryption approaches and methods become inefficient for IoT devices due to memory limits. As a result, a Lightweight cryptosystem that fits the needs of Lightweight devices and ubiquitous computing systems has emerged. The goal of this study is to present a Lightweight cryptosystem (LWC) that may be used as a plugin to secure data transfers in IoT devices and pervasive computing. To that goal, the researchers employ several simple measuring techniques. The suggested system was then implemented on a field-programmable gate array (FPGA) board using the Verilog programming language to demonstrate its appropriateness for actual security applications. FPGA is also utilized in hardware applications to assess the system's resource usage and performance. Finally, a comparison of the proposed system with previous lightweight cryptography systems is performed to reinforce the major goal of this work, which is to present a new lightweight cryptosystem.

Design and Analysis of Multi-User Faster-Than-Nyquist-DCSK Communication Systems over Multi-Path Fading Channels.

In this paper, we present a new multi-user chaos-based communication system using Faster-than-Nyquist sampling to achieve higher data rates and lower energy consumption. The newly designed system, designated Multi-user Faster Than Nyquist Differential Chaos Shift Keying (MU-FTN-DCSK), uses the traditional structure of Differential Chaos Shift Keying (DCSK) communication systems in combination with a filtering system that goes below the Nyquist limit for data sampling. The system is designed to simultaneously enable transmissions from multiple users through multiple sampling rates resulting in semi-orthogonal transmissions. The design, performance analysis, and experimental results of the MU-FTN-DCSK system are presented to demonstrate the utility of the newly proposed system in enabling multi-user communications and enhancing the spectral efficiency of the basic DCSK design without the addition of new blocks. The MU-FTN-DCSK system presented in this paper demonstrates spectral gains for one user of up to 23% and a combined gain of 25% for four (U=4) users. In this paper, we present a proof of concept demonstrating a new degree of freedom in the design of Chaos-based communication systems and their improvement in providing wireless transmissions without complicated signal processing tools or advanced hardware designs.

Design of a SIMO Deep Learning-Based Chaos Shift Keying (DLCSK) Communication System.

This paper brings forward a Deep Learning (DL)-based Chaos Shift Keying (DLCSK) demodulation scheme to promote the capabilities of existing chaos-based wireless communication systems. In coherent Chaos Shift Keying (CSK) schemes, we need synchronization of chaotic sequences, which is still practically impossible in a disturbing environment. Moreover, the conventional Differential Chaos Shift Keying (DCSK) scheme has a drawback, that for each bit, half of the bit duration is spent sending non-information bearing reference samples. To deal with this drawback, a Long Short-Term Memory (LSTM)-based receiver is trained offline, using chaotic maps through a finite number of channel realizations, and then used for classifying online modulated signals. We presented that the proposed receiver can learn different chaotic maps and estimate channels implicitly, and then retrieves the transmitted messages without any need for chaos synchronization or reference signal transmissions. Simulation results for both the AWGN and Rayleigh fading channels show a remarkable BER performance improvement compared to the conventional DCSK scheme. The proposed DLCSK system will provide opportunities for a new class of receivers by leveraging the advantages of DL, such as effective serial and parallel connectivity. A Single Input Multiple Output (SIMO) architecture of the DLCSK receiver with excellent reliability is introduced to show its capabilities. The SIMO DLCSK benefits from a DL-based channel estimation approach, which makes this architecture simpler and more efficient for applications where channel estimation is problematic, such as massive MIMO, mmWave, and cloud-based communication systems.

Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation.

Non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows for multiplexing several users over limited time-frequency resources. Among existing NOMA methods, sparse code multiple access (SCMA) is especially attractive; not only for its coding gain using suitable codebook design methodologies, but also for the guarantee of optimal detection using message passing algorithm (MPA). Despite SCMA's benefits, the bit error rate (BER) performance of SCMA systems is known to degrade due to nonlinear power amplifiers at the transmitter. To mitigate this degradation, two types of detectors have recently emerged, namely, the Bussgang-based approaches and the reproducing kernel Hilbert space (RKHS)-based approaches. This paper presents analytical results on the error-floor of the Bussgang-based MPA, and compares it with a universally optimal RKHS-based MPA using random Fourier features (RFF). Although the Bussgang-based MPA is computationally simpler, it attains a higher BER floor compared to its RKHS-based counterpart. This error floor and the BER's performance gap are quantified analytically and validated via computer simulations.

An overview of generic tools for information-theoretic secrecy performance analysis over wiretap fading channels.

Physical layer security (PLS) has been proposed to afford an extra layer of security on top of the conventional cryptographic techniques. Unlike the conventional complexity-based cryptographic techniques at the upper layers, physical layer security exploits the characteristics of wireless channels, e.g., fading, noise, interference, etc., to enhance wireless security. It is proved that secure transmission can benefit from fading channels. Accordingly, numerous researchers have explored what fading can offer for physical layer security, especially the investigation of physical layer security over wiretap fading channels. Therefore, this paper aims at reviewing the existing and ongoing research works on this topic. More specifically, we present a classification of research works in terms of the four categories of fading models: (i) small-scale, (ii) large-scale, (iii) composite, and (iv) cascaded. To elaborate these fading models with a generic and flexible tool, three promising candidates, including the mixture gamma (MG), mixture of Gaussian (MoG), and Fox's H-function distributions, are comprehensively examined and compared. Their advantages and limitations are further demonstrated via security performance metrics, which are designed as vivid indicators to measure how perfect secrecy is ensured. Two clusters of secrecy metrics, namely (i) secrecy outage probability (SOP), and the lower bound of SOP; and (ii) the probability of nonzero secrecy capacity (PNZ), the intercept probability, average secrecy capacity (ASC), and ergodic secrecy capacity, are displayed and, respectively, deployed in passive and active eavesdropping scenarios. Apart from those, revisiting the secrecy enhancement techniques based on Wyner's wiretap model, the on-off transmission scheme, jamming approach, antenna selection, and security region are discussed.

ANN Assisted-IoT Enabled COVID-19 Patient Monitoring.

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

Ergodic Capacity Analysis of Full Duplex Relaying in the Presence of Co-Channel Interference in V2V Communications.

We analyze the ergodic capacity of a dual-hop full duplex amplify-and-forward (AF) vehicle-to-vehicle (V2V) cooperative relaying system over Nakagami-m fading channels. In this context, the impacts of self-interference (SI) at the relay and co-channel interference (CCI) at the destination are taken into account in this analysis. Precisely, based on the analysis of the moment generating function (MGF) of the signal-to-interference-plus-noise ratio (SINR), new exact and lower bound expressions for the ergodic capacity are derived. The ergodic capacity upper bound is also derived based on the asymptotic outage probability of the approximated SINR. Monte-Carlo simulation results are presented to corroborate the derived analytical results. Our results show the significant impact of the considered interferences on the system performance. It is shown that the ergodic capacity is degraded when the average SI at the relay and/or the average CCI at the destination is increased. This highlights the importance of taking these phenomena into account in the performance evaluation in order to assess the practical limit of full duplex relaying (FDR) cooperative wireless communications. Interestingly, it is also observed that FDR with SI and CCI still shows a higher ergodic capacity than the interference-free half duplex relaying, especially at medium to high signal-to-noise ratios (SNRs).

Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors.

Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video surveillance, the conventional method of HBLP monitoring, suffers from various limitations, such as subject's privacy, and field-of-view obstruction. We propose an autonomous method for classifying the four state-of-the-art HBLPs in healthy adults subjects: supine, prone, left and right lateral, with no sensors or cables attached on the body and no constraints imposed on the subject. Experiments have been conducted on 12 healthy adults (age 27.35 ± 5.39 years) using a collection of textile pressure sensors embedded in a cover placed under the bed sheet. Histogram of oriented gradients and local binary patterns were extracted and fed to a supervised artificial neural network classification model. The model was trained based on the scaled conjugate gradient backpropagation. A nested cross validation with an exhaustive outer validation loop was performed to validate the classification's generalization performance. A high testing prediction accuracy of 97.9% with a Cohen's Kappa coefficient of 97.2% has been interestingly obtained. Prone and supine postures were successfully separated in the classification, in contrast to the majority of previous similar works. We found that using the information of body weight distribution along with the shape and edges contributes to a better classification performance and the ability to separate supine and prone postures. The results are satisfactorily promising toward unobtrusively monitoring posture for ulcer prevention. The method can be used in sleep studies, post-surgical procedures, or applications requiring HBLP identification.

Ground-to-air FSO communications: when high data rate communication meets efficient energy harvesting with simple designs.

The capability of free-space optical (FSO) communications in delivering very high data rates and the agility of unmanned aerial vehicle (UAV) flying platforms render FSO-UAV-based solutions attractive for delivering 5G wireless communication services. In parallel, research on simultaneous information and power transfer, whether in the context of radio frequency (RF) networks or indoor optical wireless communication (OWC) networks, is on the rise. Even though the operation of a UAV is limited by its battery lifetime, the concept of energy harvesting (EH) from the information-carrying FSO signals was not deeply investigated in the literature. This paper highlights the inherent EH capabilities in FSO transmissions and investigates novel signal design methodologies for boosting the EH efficiency. We focus on the ground-to-air communications where the ground-based FSO transmitter is connected to the power grid and, hence, is governed by a peak-power constraint and not an average-power constraint. For this setup, simulations carried out under different weather conditions demonstrate that high data rates can be associated with significant amounts of harvested energy using simple transceiver architectures.