With the huge advancement of nanotechnology over the past years, the devices are shrinking into micro-scale, even nano-scale. Additionally, the Internet of nano-things (IoNTs) are generally regarded as the ultimate formation of the current sensor networks and the development of nanonetworks would be of great help to its fulfilment, which would be ubiquitous with numerous applications in all domains of life. However, the communication between the devices in such nanonetworks is still an open problem. Body-centric nanonetworks are believed to play an essential role in the practical application of IoNTs. BCNNs are also considered as domain specific like wireless sensor networks and always deployed on purpose to support a particular application. In these networks, electromagnetic and molecular communications are widely considered as two main promising paradigms and both follow their own development process. In this survey, the recent developments of these two paradigms are first illustrated in the aspects of applications, network structures, modulation techniques, coding techniques and security to then investigate the potential of hybrid communication paradigms. Meanwhile, the enabling technologies have been presented to apprehend the state-of-art with the discussion on the possibility of the hybrid technologies. Additionally, the inter-connectivity of electromagnetic and molecular body-centric nanonetworks is discussed. Afterwards, the related security issues of the proposed networks are discussed. Finally, the challenges and open research directions are presented.

This letter presents a novel hybrid method that leverages deep learning to exploit the multiresolution analysis capability of the wavelets, in order to denoise a photoplethysmography (PPG) signal. Under the proposed method, a noisy PPG sequence of length $N$ is first decomposed into $L$ detailed coefficients using the fast wavelet transform (FWT). Then, the clean PPG sequence is reconstructed with the help of a custom feedforward neural network (FFNN) that provides the binary weights for each of the wavelet subsignals outputted by the inverse-FWT block. This way, all those subsignals which correspond to noise or artefacts are discarded during reconstruction. The FFNN is trained on the Beth Israel Deaconess Medical Center dataset and a custom video-PPG dataset, whereby we compute the mean squared-error (MSE) between the denoised sequence and the reference clean PPG signal, and compute the gradient of the MSE for the back-propagation. Simulation results reveal that our proposed method reduces the MSE of the PPG signal significantly (compared to the MSE of the original noisy PPG signal): by 56.40% for Gaussian noise, by 64.01% for Poisson noise, 46.02% for uniform noise, and by 72.36% for salt-and-pepper noise (with “db10” mother wavelet).

This paper considers a line-of-sight underwater acoustic (UWA) sensor network consisting of $M$ underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared UWA reporting channel in a time-division multiple-access fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this paper first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-feature-based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum-likelihood (ML) hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a ML distance estimator as well as the corresponding Cramer-Rao bound. We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with the colored noise and frequency-dependent pathloss.

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. Quality-of-Service (QoS) optimization during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes is the main purpose of this research. This article contributes in four distinct ways. First, to propose a novel lazy video transmission algorithm (LVTA). Second, a novel video transmission rate control algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators, i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission), and standard deviation, is investigated while comparing LVTA, VTRCA, and baseline approaches. The experimental results demonstrate that the reduction in encoding (32% and 35.4%) and transmission (37% and 39%) energy drains, PMR (5 and 4), and standard deviation (3 and 4 dB) for VTRCA and LVTA, respectively, is greater than that obtained by baseline during video streaming through WMMD.

This paper presents a study on physical layer authentication problem for in vivo nano networks at terahertz (THz) frequencies. A system model based on envisioned nano network for in vivo body-centric nano communication is considered and distance-dependent pathloss based authentication is performed. Experimental data collected from THz time-domain spectroscopy setup shows that pathloss can indeed be used as a device fingerprint. Furthermore, simulation results clearly show that given a maximum tolerable false alarm rate, detection rate up to any desired level can be achieved within the feasible region of the proposed method. It is anticipated that this paper will pave a new paradigm for secured, authenticated nano network for future applications, e.g., drug delivery and Internet of nano-things-based intelligent office.

Congenital heart disease (CHD) is the most common type of congenital anomaly, with an estimated prevalence of 8–12 per 1,000 live births. CHD results in heart murmurs, which once listened to, provide valuable information about mechanical activity of the heart and aid in diagnosis of CHD and other heart valve diseases. This work does automatic and accurate heart murmur detection from phonocardiogram (PCG) recordings. Two publicly available PCG datasets from Physionet online database are utilized to train and test various custom neural networks. We first do pre-processing which includes the following key steps: denoising, segmentation, re-labeling of noise-only segments, data normalization, and time-frequency analysis of the PCG segments using wavelet scattering transform, mel-frequency cepstral coefficients, short-time Fourier transform. We then conduct four experiments, first three (E1-E3) using first dataset, and fourth (E4) using second dataset. It turns out that our custom 1D convolutional neural network (CNN) with wavelet scattering outperforms all other models. Further, the vanilla 1D-CNN model (with wavelet scattering) outperforms the related work in terms of weighted accuracy, precision, and specificity, for experiment E3. As for experiment E1, our model performs quite close to top performing work in terms of weighted accuracy, outperforms related works in terms of precision, and is on par with the related works in terms of F1-score.