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Given the growth of domotics and home automation, there is a need to use smart devices that integrate energy management systems and enable the automation of the environment. Considering the need to study the relationship between the environmental parameters in which the equipment is located and the energy parameters, an Environmental Awareness smart Plug (EnAPlug) is proposed with the application of machine learning (Tiny ML).This article presents a demonstration of EnAPlug applied to a refrigerator for predictions on internal humidity and activation motor for 5 min-ahead prediction on its operation, i.e., turning on or off. The two models for forecasting humidity presented Root Mean Squared Error (RMSE) results of 0.055 and 0.058 and a Coefficient of determination (r2 score) of 0.97 and 0.99, respectively. For the motor activation prediction, the results obtained were an accuracy of 94.74% and 94.84%, an F1 score of 0.97 for OFF, 0.94 for ON for Forecast 1 and 0.97 for OFF and 0.93 for ON for Forecast 2. Although the prototype does not have commercial purposes, what differs from existing smart plugs is the option to store data locally. The results are promising, as it allows for better energy management with implementation of machine learning.

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This paper describes a data collection experiment focused on researching indoor positioning systems using Bluetooth Low Energy (BLE) devices. The study was conducted in a real-world scenario with 150 test points and collected signals from 11 mobile devices. The dataset contains RSSI values from the mobile devices in relation to 15 fixed anchor nodes in the experimentation scenario. The dataset includes data on device identification, labels and coordinates of test points, and the room where the data was collected. The data is organized as CSV files and offers valuable information for researchers developing and assessing location models. By sharing this dataset, we aim to support the creation of robust and precise indoor localization models.

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The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

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Studies analyzing indoor thermal environments comprising temperature and humidity may be insufficient when obtaining data from sensors, which may be susceptible to inaccurate or failed information from internal and external factors. Therefore, this study proposes an intelligent climate monitoring using a supervised learning method for virtual hygrothermal measurement in enclosed buildings used to predict temperature and relative humidity when a sensor failure is detected. The methodology comprises the data collection from a wireless sensor network, the building of the learning model for predicting the dynamics of environmental variables, and the implementation of a sensor failure detection model. We use an artificial hydrocarbon network as the learning model for their simplicity and effectiveness under uncertain and noisy data. The experiments use data acquired in two settings: (1) a laboratory office and (2) a museum storage room. The first scenario has multiple workstations, and the staff turns on or off the air conditioning depending on the feeling of comfort, generating an uncontrolled environment for the variables of interest. The second scenario has controlled temperature and humidity to ensure the conservation conditions of the museum pieces. Both scenarios used 12 sensors that acquired data for one month, providing an average of 58,300 values for each variable. Results of the proposed methodology provide 95% of accuracy in terms of sensor failure detection and identification, and less than 0.22% of tolerance variability in temperature and humidity after sensor accommodation in both scenarios.

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In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

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This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

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Data from the Internet of Things (IoT) enables the design of new business models and services that improve user experience and satisfaction. These data serve as important information sources for many domains, including disaster management, biosurveillance, smart cities, and smart health, among others. However, this scenario involves the collection of personal data, raising new challenges related to data privacy protection. Therefore, we aim to provide state-of-the-art information regarding privacy issues in the context of IoT, with a particular focus on findings that utilize the Personal Data Store (PDS) as a viable solution for these concerns. To achieve this, we conduct a systematic mapping review to identify, evaluate, and interpret the relevant literature on privacy issues and PDS-based solutions in the IoT context. Our analysis is guided by three well-defined research questions, and we systematically selected 49 studies published until 2023 from an initial pool of 176 papers. We analyze and discuss the most common privacy issues highlighted by the authors and position the role of PDS technologies as a solution to privacy issues in the IoT context. As a result, our findings reveal that only a small number of works (approximately 20%) were dedicated to presenting solutions for privacy issues. Most works (almost 82%) were published between 2018 and 2023, demonstrating an increased interest in the theme in recent years. Additionally, only two works used PDS-based solutions to deal with privacy issues in the IoT context.

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The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

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The increasing urbanization in a poorly planned way accentuates the imbalance between the population's needs and the organized development in urban spaces. The present study is based on the development of a situational diagnosis in the scope of a smart city, for the contextualization of potential opportunities for actions and innovation strategies in urban spaces. This article presents a literature overview covering the innovative actions developed in the scope of smart cities in scientific publications. Furthermore, the scope of the study is identifying innovation initiatives in the performance of actions and solutions for urban spaces. A literature review was developed supported by mappings, couplings, and diagrams, through the use of VOSViewer and SciMat software, and 115 articles were selected and analyzed, considering the articles based on the criterion of the coefficient of the number of citations concerning the year of publication. In the literature overview developed, it was found that the research within the scope of smart cities has been deepened over the years, with the evolution of the number of words related to the theme in the period from 2014 to 2021, as the advance in the number of publications from 2018 is noticeable, which highlights the increase in popularity regarding the topic, as well as its current relevance. The study identified thematic axes with an emphasis on technology and innovation, environment, urbanism, energy, governance, mobility, and accessibility. The results contributed by assembling innovative smart city actions and practices in an interrelated way with technology, innovation, and market-oriented constructs aimed to reach urban demands, as well as the development of innovative solutions between public institutions and business organizations to integrate urban spaces.

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3D printing has revolutionized the manufacturing process of microanalytical devices by enabling the automated production of customized objects. This technology promises to become a fundamental tool, accelerating investigations in critical areas of health, food, and environmental sciences. This microfabrication technology can be easily disseminated among users to produce further and provide analytical data to an interconnected network towards the Internet of Things, as 3D printers enable automated, reproducible, low-cost, and easy fabrication of microanalytical devices in a single step. New functional materials are being investigated for one-step fabrication of highly complex 3D printed parts using photocurable resins. However, they are not yet widely used to fabricate microfluidic devices. This is likely the critical step towards easy and automated fabrication of sophisticated, complex, and functional 3D-printed microchips. Accordingly, this review covers recent advances in the development of 3D-printed microfluidic devices for point-of-care (POC) or bioanalytical applications such as nucleic acid amplification assays, immunoassays, cell and biomarker analysis and organs-on-a-chip. Finally, we discuss the future implications of this technology and highlight the challenges in researching and developing appropriate materials and manufacturing techniques to enable the production of 3D-printed microfluidic analytical devices in a single step.

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Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics. To meet the growing demand, there is a need for automation and accuracy of processes, at a lower cost. Convolutional neural networks (CNNs) are a viable alternative for automation, reducing human intervention, work time, errors, and production costs. Therefore, the objective of this work is to evaluate the efficacy of convolutional neural networks (CNNs) in counting round fish fingerlings (Serrasalmidae) at different densities using 390 color photographs in an illuminated environment. The photographs were submitted to two convolutional neural networks for object detection: one model was adapted from a pre-trained CNN and the other was an online platform based on AutoML. The metrics used for performance evaluation were precision (P), recall (R), accuracy (A), and F1-Score. In conclusion, convolutional neural networks (CNNs) are effective tools for detecting and counting fish. The pre-trained CNN demonstrated outstanding performance in identifying fish fingerlings, achieving accuracy, precision, and recall rates of 99% or higher, regardless of fish density. On the other hand, the AutoML exhibited reduced accuracy and recall rates as the number of fish increased.

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Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications are both the Wireless Sensor Nodes (WSn) and the Wireless Sensor Networks. It is essential to state that designing an application demands a particular design of a WSn, which represents an important time consumption during the process. In line with this observation, our work describes the development of a modular WSn (MWSn) built with digital processing, wireless communication, and power supply subsystems. Then, we reduce the WSn-implementing process into the design of its modular sensing subsystem. This would allow the development and launching processes of IoT applications across different fields to become faster and easier. Our proposal presents a versatile communication between the sensing modules and the MWSn using one- or two-wired communication protocols, such as I2C. To validate the efficiency and versatility of our proposal, we present two IoT-based remote monitoring applications.

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PURPOSE: Assistive technologies based on IoT can contribute to improve quality of living of patients with severe motor difficulties by providing partial or total independence. The aim of this work was to analyse the usability and performance of an assistive system based on the IoT when is evaluated by a child patient with spinal muscular atrophy type 1 (SMA-I). MATERIALS AND METHODS: The study involved a child with SMA-I and his caregiver. The materials used include an M5Stack Core2 kit, a mobile app, and a smart switch based on the ESP-01S card. The patient sends requests to the caregiver from the app installed on the M5Stack Core2 to a mobile app, and controls smart switches located in the rooms. The system was tested by the participants for a period of 30 days to later evaluate its usability and performance. RESULTS: The results show that the control function of smart switches is the most used and there is no decrease in interactions over the days for the system in general. In addition, the scores obtained from both usability tests (patient and caregiver) were 87.5% and 90%, respectively. The average performance of the entire system was 93.33%. CONCLUSION: The application of assistive technologies based on the IoT allows obtaining a practical solution that improves the development of daily activities in a patient with SMA-I.

A low-cost device can contribute to improve the quality of living of spinal muscular atrophy patients by favouring partial or total independence.IoT-based assistive technologies allow obtaining practical solutions that improve the development of daily activities.

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The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios.

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Context: The technological advancement of the Internet of Things (IoT) creates opportunities in various social sectors. Patients in clinics or home care have their comfort and safety enhanced with remote monitoring, sensors and applications that control and transfer patient data. These applications must be trustworthy, since they deal with sensitive data. Purpose: The purpose of this work is to identify gaps in trustworthiness, availability, effectiveness, security and other attributes. Also, to highlight challenges and opportunities for research and give guidance on choosing the right technology or application based on the resources available to support patients and doctors, protocol of communication and maturity level of these technologies. Methodology: This work presents a systematic review of the literature following four steps: Definition of the Research Questions, Conduct Search, Screening of Papers, and Data Extraction and Mapping Process. Results: Based on the articles studied, it was possible to answer important questions about eHealth applications. The results highlight how eHealth applications can enhance patient care by monitoring health data and supporting doctors' decision-making with a reasonable level of trustworthiness. Additionally, the results demonstrate how applications can notify external caregivers in emergencies and assist in diagnosis and treatment of illnesses. However, these applications still face problems related to sensor lifetime, medical data sharing, interoperability and lack of standardization. Finally, we suggest a literature mapping to support the choice of technologies based on resources available, communication protocol and technological maturity. Conclusion: This work carries out a systematic literature review to discuss state-of-the-art eHealth applications and gather new information of current research. In this process it was possible to show how these applications work, map out their main technological characteristics to assist the decision-making process for future works and uncover eHealth applications' strengths, future perspectives and challenges, specifically related to the high level of trustworthiness necessary.

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Currently, Brazil is one of the world's largest grain producers and exporters. Agriculture has already entered its 4.0 version (2017), also known as digital agriculture, when the industry has entered the 4.0 era (2011). This new paradigm uses Internet of Things (IoT) techniques, sensors installed in the field, network of interconnected sensors in the plot, drones for crop monitoring, multispectral cameras, storage and processing of data in Cloud Computing, and Big Data techniques to process the large volumes of generated data. One of the practical options for implementing precision agriculture is the segmentation of the plot into management zones, aiming at maximizing profits according to the productive potential of each zone, being economically viable even for small producers. Considering that climate factors directly influence yield, this study describes the development of a sensor network for climate monitoring of management zones (microclimates), allowing the identification of climate factors that influence yield at each of its stages.•Application of the internet of things to assist in decision making in the agricultural production system.•AgDataBox (ADB-IoT) web platform has an Application Programming Interface (API).•An agrometeorological station capable of monitoring all meteorological parameters was developed (Kate 3.0).

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The expectation for communication systems beyond 5G/6G is to provide high reliability, high throughput, low latency, and high energy efficiency services. The integration between systems based on radio frequency (RF) and visible light communication (VLC) promises the design of hybrid systems capable of addressing and largely satisfying these requirements. Hybrid network design enables complementary cooperation without interference between the two technologies, thereby increasing the overall system data rate, improving load balancing, and reducing non-coverage areas. VLC/RF hybrid networks can offer reliable and efficient communication solutions for Internet of Things (IoT) applications such as smart lighting, location-based services, home automation, smart healthcare, and industrial IoT. Therefore, hybrid VLC/RF networks are key technologies for next-generation communication systems. In this paper, a comprehensive state-of-the-art study of hybrid VLC/RF networks is carried out, divided into four areas. First, indoor scenarios are studied considering lighting requirements, hybrid channel models, load balancing, resource allocation, and hybrid network topologies. Second, the characteristics and implementation of these hybrid networks in outdoor scenarios with adverse conditions are analyzed. Third, we address the main applications of hybrid VLC/RF networks in technological, economic, and socio-environmental domains. Finally, we outline the main challenges and future research lines of hybrid VLC/RF networks.

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BACKGROUND: Wheelchair positioning systems can prevent postural deficits and pressure injuries. However, a more effective professional follow-up is needed to assess and monitor positioning according to the specificities and clinical conditions of each user. OBJECTIVE: This study aims to present the concept of an electronic system embedded in a motorized wheelchair, based on the Internet of Things (IoT), for automated positioning as part of a study on wheelchairs and telemonitoring. METHODS: We conducted a mixed methods study with a user-centered design approach, interviews with 16 wheelchair users and 66 professionals for the development of system functions, and a formative assessment of 5 participants with descriptive analysis to design system concepts. RESULTS: We presented a new wheelchair system with hardware and software components developed based on coparticipation with singular components in an IoT architecture. In an IoT solution, the incorporation of sensors from the inertial measurement unit was crucial. These sensors were vital for offering alternative methods to monitor and control the tilt and recline functions of a wheelchair. This monitoring and control could be achieved autonomously through a smartphone app. In addition, this capability addressed the requirements of real users. CONCLUSIONS: The technologies presented in this system can benefit telemonitoring and favor real feedback, allowing quality provision of health services to wheelchair users. User-centered development favored development with specific functions to meet the real demands of users. We emphasize the importance of future studies on the correlation between diagnoses and the use of the system in a real environment to help professionals in treatment.

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The rapid development of the Internet of Things (IoT) has brought about the processing and storage of sensitive information on resource-constrained devices, which are susceptible to various hardware attacks. Fault injection attacks (FIAs) stand out as one of the most widespread. Particularly, voltage-based FIAs (V-FIAs) have gained popularity due to their non-invasive nature and high effectiveness in inducing faults by pushing the IoT hardware to its operational limits. Improving the security of devices and gaining a comprehensive understanding of their vulnerabilities is of utmost importance. In this study, we present a novel fault injection method and employ it to target an 8-bit AVR microcontroller. We identify the optimal attack parameters by analyzing the detected failures and their trends. A case study is conducted to validate the efficacy of this new method in a more realistic scenario, focusing on a simple authentication method using the determined optimal parameters. This analysis not only demonstrates the feasibility of the V-FIA but also elucidates the primary characteristics of the resulting failures and their propagation in resource-constrained devices. Additionally, we devise a hardware/software countermeasure that can be integrated into any resource-constrained device to thwart such attacks in IoT scenarios.

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Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.