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.

The textile and clothing industry sector has today a big environmental impact, not only due to the consumption of water and the use of toxic chemicals but also due to the increasing levels of textile waste. One way to reduce the problem is to circularise the, currently linear, textile and clothing value chain, by using discarded clothes as raw material for the production of new clothes, transforming it into a model of circular economy. This way, while reducing the need to produce new raw materials (e.g. cotton), the problem of textile waste produced is also reduced, thus contributing to a more sustainable industry. In this article, we review the current approaches for traceability in the textile and clothing value chain, and study a set of technologies we deem essential for promoting the circular economy in this value chain – namely, the blockchain technology – for registering activities on traceable items through the value chain, and the Internet of Things (IoT) technology, for easily identifying the traceable items’ digital twins.

Internet of Things (IoT) has been developing to become a free exchange of useful information between multiple real-world devices. Already spread all over the world in the most varied forms and applications, IoT devices need to overcome a series of challenges to respond to the new requirements and demands. The main focus of this manuscript is to establish good practices for the design of IoT devices (i.e., smart devices) with a focus on two main design challenges: power and connectivity. It groups IoT devices in passive, semi-passive, and active, giving details on multiple research topics. Backscatter communication, Wireless Power Transfer (WPT), Energy Harvesting (EH), chipless devices, Simultaneous Wireless Information and Power Transfer (SWIPT), and Wake-Up Radio (WUR) are some examples of the technologies that will be explored in this work.

Humans spend the majority of their time indoors, where they are potentially exposed to hazardous pollutants. Within this context, over the past few years, there has been an upsurge of low-cost sensors (LCS) for the measurement of indoor air pollutants, motivated both by recent technological advances and by increased awareness of indoor air quality (IAQ) and its potential negative health impacts. Although not meeting the performance requirements for reference regulatory-equivalent monitoring indoors, LCS can provide informative measurements, offering an opportunity for high-resolution monitoring, emission source identification, exposure mitigation and managing IAQ and energy efficiency, among others. This article discusses the strengths and limitations that LCS offer for applications in the field of IAQ monitoring; it provides an overview of existing sensor technologies and gives recommendations for different indoor applications, considering their performance in the complex indoor environment and discussing future trends.

This review study examines the relationship between radon and geology, including its origins, transmission, and accumulation, as well as its impact on human health and mitigation strategies. The decay of uranium and thorium produces radon, a radioactive gas. Its concentration in the environment fluctuates based on local geology, soil permeability, climate, and terrain, as well as regional, seasonal, and daily meteorological conditions. Exposure to radon indoors is associated with an increased risk of lung cancer, making it a significant public health concern. Measuring radon concentrations in indoor environments is essential for identifying high-risk areas and devising effective mitigation strategies, such as ventilation, fissure sealing, and exhaust installation. The need for greater knowledge of regional and seasonal fluctuations in radon concentration, as well as the development of more effective and cost-effective measuring and mitigation strategies, has implications for future research. To influence public health policies and practices, more research on the long-term health effects of radon exposure is required. The focus of public health policy should be on increasing awareness of the dangers associated with radon exposure, supporting regular measurement and monitoring of radon concentrations in indoor areas, and encouraging the adoption of effective mitigation techniques. When selecting construction sites, urban planning regulations and building codes should consider the local geology and radon levels in the soil.

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.