The increasing adoption of the Industrial Internet of Things (IIoT) has led to significant improvements in operational efficiency but has also brought new challenges for cybersecurity. To address these challenges, a number of standards have been introduced over the years. One of the best-known series of standards for this purpose is ISA/IEC 62443. This paper examines the applicability of the ISA/IEC 62443 series of standards, traditionally used for securing industrial automation and control systems, to the IIoT environment. For each requirement described in the ISA/IEC 62443 standards, relevant research on that subject is reviewed and presented in a table-like manner. Based on this table, areas for future research are identified, including system hardening, asset inventory, safety instrumented system isolation, risk assessment methodologies, change management systems, data storage security, and incident response procedures. The focus on future improvement is performed for the area of system hardening, for which research and guidelines already exist but not for the specific area of IIoT environments.

The paper presents an accurate and simple method for the approximation of the nonlinear B-H curves using expansion into complex exponential series. The least-squares fit of the model is obtained by the application of the Moore-Penrose pseudoinverse. Due to the completeness of the orthogonal basis of the approximation, any desired accuracy can be achieved with increasing the number of terms. Using the measurements acquired by an automated system based on the PXI digitizer boards, the feasibility of the method is proved.

Fitness and activity trackers are hugely popular wearable devices that monitor various health-related metrics, such as step count, heartbeat rate, or even oxygen saturation. Utilizing personal health information obtained by users’ personal trackers provides promising results in the fields of telemedicine and personal well-being. However, we face challenges such as data quality, privacy and compliance with standards and regulations. This paper addresses such challenges, with the focus on the last one. Semantic constraints for healthcare datatypes are defined to ensure compliance with standards, making the information medically valid and relevant. A process of semantic verification and Schematron-based validation is proposed. The validation process suggested in this paper will enable the data to be transferred and incorporated into a formal Electronic Health Record. The process is then verified using datasets containing various health-related data types. The aim is to integrate personal health data into Electronic Health Record, which forms a part of Central Health Information System. This would provide personalized medical services to patients and help physicians to make more informed decisions.

This paper presents the noise reduction of power quality measurement with time-frequency (T-F) depth analysis. Noise reduction is achieved with wavelet transformation by decomposition, thresholding and lossless reconstruction of signal. Three main problems with T-F noise reduction with wavelet transformation are: defining thresholding levels, level of decomposition and number of wavelet vanishing moment. In this analysis decomposition level and number of vanishing moments are defined via simulation for pure sinusoid signal, these values are used for signals with perturbations and they provide reasonable results. Analysis is conducted by simulating various change of parameters and then approved by laboratory measurement with calibrators and precision measurement equipment. The paper describes a method for noise reduction of signal without prior knowledge of noise level or signal amplitude. Proposed method is able to separate noise without adding phase shift for diverse signal conditions, harmonics, interharmonics, dips, swells and dynamic variations.