The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts.

Diabetes is a common chronic metabolic disease with a wide range of clinical symptoms and consequences and one of the main causes of death. For the management of diabetes, painless and continuous interstitial fluid (ISF) glucose monitoring is ideal. Here, we demonstrate continuous diabetes monitoring using an integrated microneedle (MN) biosensor with an emergency alert system. MNs are a novel technique in the field of biomedical engineering because of their ability to analyze bioinformation with minimal invasion. In this work we developed a poly(methyl methacrylate) (PMMA) based MN glucose sensor. The device was produced by the 3D printing technique, microfabrication, electrodeposition, and enzyme immobilization step. The in vitro test for the glucose MN sensor showed a linear range from 1.5 to 14 mM with a sensitivity of 1.51 μA mM⁻¹, limit of detection (LOD) of 0.35 mM and good selectivity. Highly repeatable sensing is observed with good reproducibility. The interference-free detection of glucose in the presence of physiologically relevant concentrations of ascorbic acid, uric acid, and mannose is demonstrated, along with the operational stability of the array. After resolving the biofouling consequences linked to on-body sensing, this MN platform would be appealing for minimally invasive electrochemical glucose monitoring. An alert is sent to confidants via email or SMS when the values are abnormal. The application is also able to display the recorded values in the form of a graph to help determine the state of health of the user over a period of time. It can be concluded that continuous monitoring and an emergency alert system are important for keeping an eye on diabetic patients and can send alert in case of an abnormal situation of the patient.

Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover, conventional denoising methods struggle with the non-uniform noise distributions in LDCT images and often require complex projection data, which limits their effectiveness and generalizability. Herein, we propose an AI-based denoising approach using an Attention Residual U-Net (ARU-Net) architecture integrated into the CycleGAN framework named Attention Residual U-Net CycleGAN (ARUC-GAN). The proposed framework outperforms state-of-theart denoising models, such as RED-CNN, EDCNN, and CTformer, according to experimental evaluation on the Mayo Clinic abdominal CT dataset. The model exhibits a peak signal-to-noise ratio (PSNR) of 34.82 dB, a structural similarity index (SSIM) of 0.85, and an RMSE of 0.018. Furthermore, the model successfully maintains edge structures with an Edge Keeping Index (EKI) of 0.835. The visual examination validates ARUC-GAN’s superior texture and detail preservation. The results indicate that the suggested method has great promise as a smart health tool for improving diagnosis accuracy in LDCT scans.

The operation of the quartz crystal microbalance (QCM) in liquids is plagued by small flexural admixtures to the thickness-shear deformation. The resonator surface moves not only in the transverse direction, but also along the surface normal, thereby emitting compressional waves into the liquid. Using a simple analytical model and laser Doppler vibrometry, we show that the flexural admixtures are stronger on the fundamental mode than on the overtones. The normal amplitude of motion amounts to about 1% of the transverse motion on the fundamental mode. This ratio drops by a factor of two on the overtones. A similar dependence on overtone order is observed in experiments, where the resonator is immersed in a liquid and faces an opposite planar wall, the distance of which varies. Standing compressional waves occur at certain distances. The amplitudes of these are smaller on the overtones than on the fundamental mode. The findings can be rationalized with the tensor form of the small-load approximation.