Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System.

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution (e.g., a good trade-off between cost, accuracy, resilience and computational efficiency) that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case.

Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms such as edge computing, although they have a huge potential for the digital transition towards sustainability, they are not yet contributing to the sustainable development of the IoT sector itself. In fact, such a sector has a significant carbon footprint due to the use of scarce raw materials and its energy consumption in manufacturing, operating, and recycling processes. To tackle these issues, the Green IoT (G-IoT) paradigm has emerged as a research area to reduce such carbon footprint; however, its sustainable vision collides directly with the advent of Edge Artificial Intelligence (Edge AI), which imposes the consumption of additional energy. This article deals with this problem by exploring the different aspects that impact the design and development of Edge-AI G-IoT systems. Moreover, it presents a practical Industry 5.0 use case that illustrates the different concepts analyzed throughout the article. Specifically, the proposed scenario consists in an Industry 5.0 smart workshop that looks for improving operator safety and operation tracking. Such an application case makes use of a mist computing architecture composed of AI-enabled IoT nodes. After describing the application case, it is evaluated its energy consumption and it is analyzed the impact on the carbon footprint that it may have on different countries. Overall, this article provides guidelines that will help future developers to face the challenges that will arise when creating the next generation of Edge-AI G-IoT systems.

Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review.

Wearable and Internet of Things (IoT) technologies in sports open a new era in athlete's training, not only for performance monitoring and evaluation but also for fitness assessment. These technologies rely on sensor systems that collect, process and transmit relevant data, such as biomarkers and/or other performance indicators that are crucial to evaluate the evolution of the athlete's condition, and therefore potentiate their performance. This work aims to identify and summarize recent studies that have used wearables and IoT technologies and discuss its applicability for fitness assessment. A systematic review of electronic databases (WOS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO, IEEEXplore, PubMed, SPORTDiscus, Cochrane and Web of Science) was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 280 studies initially identified, 20 were fully examined in terms of hardware and software and their applicability for fitness assessment. Results have shown that wearable and IoT technologies have been used in sports not only for fitness assessment but also for monitoring the athlete's internal and external workloads, employing physiological status monitoring and activity recognition and tracking techniques. However, the maturity level of such technologies is still low, particularly with the need for the acquisition of more-and more effective-biomarkers regarding the athlete's internal workload, which limits its wider adoption by the sports community.

RSSI-based LoRaWAN dataset collected in a dynamic and harsh industrial environment with high humidity.

Enabling precise device localization is a critical requirement for the future of the industry. Leveraging signal features for location determination has emerged as a leading approach and a good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long-range coverage area. LoRaWAN (a low power and wide area networking protocol built on top of the LoRa radio modulation technique) is one of the most common communication networks that can provide coverage with low implementation cost and power consumption [1]. Among various signal features that can be used for localization, Received Signal Strength (RSS) gets more attention because of its low-cost deployment. However, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic setting. To evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRa gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via GPS and transmits it as the LoRa message. In addition, to provide more insight into the effect of the dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages at fixed time intervals, including their unique IDs. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, along with timestamps. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in a dynamic and harsh environment.

Selective Laser Melting and Spark Plasma Sintering: A Perspective on Functional Biomaterials.

Achieving lightweight, high-strength, and biocompatible composites is a crucial objective in the field of tissue engineering. Intricate porous metallic structures, such as lattices, scaffolds, or triply periodic minimal surfaces (TPMSs), created via the selective laser melting (SLM) technique, are utilized as load-bearing matrices for filled ceramics. The primary metal alloys in this category are titanium-based Ti6Al4V and iron-based 316L, which can have either a uniform cell or a gradient structure. Well-known ceramics used in biomaterial applications include titanium dioxide (TiO2), zirconium dioxide (ZrO2), aluminum oxide (Al2O3), hydroxyapatite (HA), wollastonite (W), and tricalcium phosphate (TCP). To fill the structures fabricated by SLM, an appropriate ceramic is employed through the spark plasma sintering (SPS) method, making them suitable for in vitro or in vivo applications following minor post-processing. The combined SLM-SPS approach offers advantages, such as rapid design and prototyping, as well as assured densification and consolidation, although challenges persist in terms of large-scale structure and molding design. The individual or combined application of SLM and SPS processes can be implemented based on the specific requirements for fabricated sample size, shape complexity, densification, and mass productivity. This flexibility is a notable advantage offered by the combined processes of SLM and SPS. The present article provides an overview of metal-ceramic composites produced through SLM-SPS techniques. Mg-W-HA demonstrates promise for load-bearing biomedical applications, while Cu-TiO2-Ag exhibits potential for virucidal activities. Moreover, a functionally graded lattice (FGL) structure, either in radial or longitudinal directions, offers enhanced advantages by allowing adjustability and control over porosity, roughness, strength, and material proportions within the composite.

Impacts of Indoor Radon on Health: A Comprehensive Review on Causes, Assessment and Remediation Strategies.

Indoor radon exposure is raising concerns due to its impact on health, namely its known relationship with lung cancer. Consequently, there is an urgent need to understand the risk factors associated with radon exposure, and how this can be harmful to the health of exposed populations. This article presents a comprehensive review of studies indicating a correlation between indoor radon exposure and the higher probability of occurrence of health problems in exposed populations. The analyzed studies statistically justify this correlation between exposure to indoor radon and the incidence of lung diseases in regions where concentrations are particularly high. However, some studies also showed that even in situations where indoor radon concentrations are lower, can be found a tendency, albeit smaller, for the occurrence of negative impacts on lung cancer incidence. Lastly, regarding risk remediation, an analysis has been conducted and presented in two core perspectives: (i) focusing on the identification and application of corrective measures in pre-existing buildings, and (ii) focusing on the implementation of preventive measures during the project design and before construction, both focusing on mitigating negative impacts of indoor radon exposure on the health of populations.

Designing an Indoor Radon Risk Exposure Indicator (IRREI): An Evaluation Tool for Risk Management and Communication in the IoT Age.

The explosive data growth in the current information age requires consistent new methodologies harmonized with the new IoT era for data analysis in a space-time context. Moreover, intuitive data visualization is a central feature in exploring, interpreting, and extracting specific insights for subsequent numerical data representation. This integrated process is normally based on the definition of relevant metrics and specific performance indicators, both computed upon continuous real-time data, considering the specificities of a particular application case for data validation. This article presents an IoT-oriented evaluation tool for Radon Risk Management (RRM), based on the design of a simple and intuitive Indoor Radon Risk Exposure Indicator (IRREI), specifically tailored to be used as a decision-making aid tool for building owners, building designers, and buildings managers, or simply as an alert flag for the problem awareness of ordinary citizens. The proposed methodology was designed for graphic representation aligned with the requirements of the current IoT age, i.e., the methodology is robust enough for continuous data collection with specific Spatio-temporal attributes and, therefore, a set of adequate Radon risk-related metrics can be extracted and proposed. Metrics are summarized considering the application case, taken as a case study for data validation, by including relevant variables to frame the study, such as the regulatory International Commission on Radiological Protection (ICRP) dosimetric limits, building occupancy (spatial dimension), and occupants' exposure periods (temporal dimension). This work has the following main contributions: (1) providing a historical perspective regarding RRM indicator evolution along time; (2) outlining both the formulation and the validation of the proposed IRREI indicator; (3) implementing an IoT-oriented methodology for an RRM indicator; and (4) a discussion on Radon risk public perception, undertaken based on the results obtained after assessment of the IRREI indicator by applying a screening questionnaire with a total of 873 valid answers.