This paper reviews the Internet of Things (IoT) in healthcare, also known as Healthcare-IoT (H-IoT), and its security and privacy challenges. Specifically, smartwatches, blood pressure sensors, and temperature sensors are discussed as IoT devices that pose vulnerabilities. Several risks may arise, such as data breaches, unauthorized access, and potential patient harm. This review explores data security challenges associated with machine learning and H-IoT. Additionally, it emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application layers. Detecting and responding to anomalies involve a wide range of cyber threats and attacks, as well as protocols such asWi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). Hence, to protect and mitigate H-IoT devices from the increasing cyber threat landscape, a robust authentication mechanism based on machine learning and deep learning techniques is required. Overall, this paper reviews the security and privacy challenges in healthcare IoT and discusses risk mitigation strategies for building cyber resilience in H-IoT.

A range of optical fibre-based sensors for the measurement of ethanol, primarily in aqueous solution, have been developed and are reviewed here. The sensing approaches can be classified into four groups according to the measurement techniques used, namely absorption (or absorbance), external interferometric, internal fibre grating and plasmonic sensing. The sensors within these groupings can be compared in terms of their characteristic performance indicators, which include sensitivity, resolution and measurement range. Here, particular attention is paid to the potential application areas of these sensors as ethanol production is globally viewed as an important industrial activity. Potential industrial applications are highlighted in the context of the emergence of the internet of things (IoT), which is driving widespread utilization of these sensors in the commercially significant industrial and medical sectors. The review concludes with a summary of the current status and future prospects of optical fibre ethanol sensors for industrial use.

We report a U-bend plastic optical fiber (POF) sensor for measuring ultralow concentration of ethanol corresponding to the initial bioethanol production rate by cyanobacteria, i.e., 0.1-0.5 gL⁻¹ day⁻¹. This production rate corresponds to 0.00499-0.0499 %wt of ethanol in total solution in terms of weight percent concentration. Refractive indices for these minute ethanol concentration values are not available in the literature hence mathematical estimation of the refractive indices for the ethanol-water solutions in this concentration range is demonstrated. The sensing principle is based on optical fiber-based evanescent wave absorption. Parameters affecting the response of an evanescent wave absorption sensor are analyzed for the intended concentration range of ethanol. Experimental results using the U-bend evanescent wave POF sensor are also presented for ethanol-water solutions having refractive indices corresponding to the bioethanol production rate. The excellent repeatability of the measurement is established and these real-time measurements show that sensor has a sensitivity of 817.76 O.D/RIU (O.D refers to optical density, unit of absorbance) with a 99.76% linearity. The limit of detection of the sensor is 9.2 x 10⁻⁷ RIU. It is also proved using refractive index calculations of ethanol-water solutions that the sensor exhibits a resolution of 10⁻⁷ RIU. The sensor of this investigation, therefore, represents a potential solution for online and real-time monitoring of the production of biofuels even at the very low-level initial concentration of product.

In this paper, the surface roughness characteristic of D-shaped optical fibre sensors with its effects on the sensitivity has been studied. The ULTRAPOL end and edge polishing system was used with some modifications to fabricate the D-shaped sensors with planar sensing zone from the single-mode optical fibres. The mean surface roughness of 343, 96, 25 and 9 nm was estimated at the sensing zone of the D-shaped sensors which were sequentially polished with 30, 9, 3 and 0.5 µm grit size polishing films, respectively. From the experimental results, it has been observed that surface roughness of the sensing zone does not exhibit the significant effects on the output signal strength, whereas the sensitivity of the D-shaped sensors nonlinearly related with the surface roughness of the sensing zone. The designed D-shaped optical fibre sensors have potential applications in biomedical and chemical industries.

Fibre optic sensors (FOSs) have developed as a transformative technology in healthcare, often offering unparalleled accuracy and sensitivity in monitoring various physiological and biochemical parameters. Their applications range from tracking vital signs to guiding minimally invasive surgeries, enabling advancements in medical diagnostics and treatment. However, the integration of FOSs into biomedical applications faces numerous challenges. This article describes some challenges for adopting FOSs for biomedical purposes, exploring technical and practical obstacles, and examining innovative solutions. Significant challenges include biocompatibility, miniaturization, addressing signal processing complexities, and meeting regulatory standards. By outlining solutions to the stated challenges, it is intended that this article provides a better understanding of FOS technologies in biomedical settings and their implementation. A broader appreciation of the technology, offered in this article, enhances patient care and improved medical outcomes.