Passive UHF RFID-based real-time intravenous fluid level sensor.

Ultra high frequency (UHF) passive radio frequency identification (RFID) tag-based sensors are proposed for intravenous (IV) fluid level monitoring in medical Internet of Things (IoT) applications. Two versions of the sensor are proposed: a binary sensor (i.e., full vs. empty state sensing) and a real-time (i.e., continuous level) sensor. The operating principle is demonstrated using full-wave electromagnetic simulation at 910 MHz and validated with experimental results. Generalized Additive Model (GAM) and random forest algorithms are employed for each interrogation dataset. Real-time sensing is accomplished with small deviations across the models. A minimum of 72% and a maximum of 97% of cases are within a 20% error for the GAM model and 62% to 98% for the random forest model. The proposed sensor is battery-free, lightweight, low-cost, and highly reliable. The read range of the proposed sensor is 4.6 m.

Smart Wireless Transducer Dedicated for Use in Aviation Laboratories.

Reliable testing of aviation components depends on the quality and configuration flexibility of measurement systems. In a typical approach to test instrumentation, there are tens or hundreds of sensors on the test head and test facility, which are connected by wires to measurement cards in control cabinets. The preparation of wiring and the setup of measurement systems are laborious tasks requiring diligence. The use of smart wireless transducers allows for a new approach to test preparation by reducing the number of wires. Moreover, additional functionalities like data processing, alarm-level monitoring, compensation, or self-diagnosis could improve the functionality and accuracy of measurement systems. A combination of low power consumption, wireless communication, and wireless power transfer could speed up the test-rig instrumentation process and bring new test possibilities, e.g., long-term testing of moving or rotating components. This paper presents the design of a wireless smart transducer dedicated for use with sensors typical of aviation laboratories such as thermocouples, RTDs (Resistance Temperature Detectors), strain gauges, and voltage output integrated sensors. The following sections present various design requirements, proposed technical solutions, a study of battery and wireless power supply possibilities, assembly, and test results. All presented tests were carried out in the Components Test Laboratory located at the Lukasiewicz Research Network-Institute of Aviation.

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

The Advantage of the 5G Network for Enhancing the Internet of Things and the Evolution of the 6G Network.

The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network.

Batteryless wireless magnetostrictive Fe30Co70/Ni clad plate for human coronavirus 229E detection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been garnered increasing for its rapid worldwide spread. Each country had implemented city-wide lockdowns and immigration regulations to prevent the spread of the infection, resulting in severe economic consequences. Materials and technologies that monitor environmental conditions and wirelessly communicate such information to people are thus gaining considerable attention as a countermeasure. This study investigated the dynamic characteristics of batteryless magnetostrictive alloys for energy harvesting to detect human coronavirus 229E (HCoV-229E). Light and thin magnetostrictive Fe-Co/Ni clad plate with rectification, direct current (DC) voltage storage capacitor, and wireless information transmission circuits were developed for this purpose. The power consumption was reduced by improving the energy storage circuit, and the magnetostrictive clad plate under bending vibration stored a DC voltage of 1.9 V and wirelessly transmitted a signal to a personal computer once every 5 min and 10 s under bias magnetic fields of 0 and 10 mT, respectively. Then, on the clad plate surface, a novel CD13 biorecognition layer was immobilized using a self-assembled monolayer of -COOH groups, thus forming an amide bond with -NH2 groups for the detection of HCoV-229E. A bending vibration test demonstrated the resonance frequency changes because of HCoV-229E binding. The fluorescence signal demonstrated that HCoV-229E could be successfully detected. Thus, because HCoV-229E changed the dynamic characteristics of this plate, the CD13-modified magnetostrictive clad plate could detect HCoV-229E from the interval of wireless communication time. Therefore, a monitoring system that transmits/detects the presence of human coronavirus without batteries will be realized soon.

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning.

Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.

A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks.

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing "no-touch" smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.

Resource Scheduling and Energy Consumption Optimization Based on Lyapunov Optimization in Fog Computing.

Delay-sensitive tasks account for an increasing proportion of all tasks on the Internet of Things (IoT). How to solve such problems has become a hot research topic. Delay-sensitive tasks scenarios include intelligent vehicles, unmanned aerial vehicles, industrial IoT, intelligent transportation, etc. More and more scenarios have delay requirements for tasks and simply reducing the delay of tasks is not enough. However, speeding up the processing speed of a task means increasing energy consumption, so we try to find a way to complete tasks on time with the lowest energy consumption. Hence, we propose a heuristic particle swarm optimization (PSO) algorithm based on a Lyapunov framework (LPSO). Since task duration and queue stability are guaranteed, a balance is achieved between the computational energy consumption of the IoT nodes, the transmission energy consumption and the fog node computing energy consumption, so that tasks can be completed with minimum energy consumption. Compared with the original PSO algorithm and the greedy algorithm, the performance of our LPSO algorithm is significantly improved.

Towards Developing a Robust Intrusion Detection Model Using Hadoop-Spark and Data Augmentation for IoT Networks.

In recent years, anomaly detection and machine learning for intrusion detection systems have been used to detect anomalies on Internet of Things networks. These systems rely on machine and deep learning to improve the detection accuracy. However, the robustness of the model depends on the number of datasamples available, quality of the data, and the distribution of the data classes. In the present paper, we focused specifically on the amount of data and class imbalanced since both parameters are key in IoT due to the fact that network traffic is increasing exponentially. For this reason, we propose a framework that uses a big data methodology with Hadoop-Spark to train and test multi-class and binary classification with one-vs-rest strategy for intrusion detection using the entire BoT IoT dataset. Thus, we evaluate all the algorithms available in Hadoop-Spark in terms of accuracy and processing time. In addition, since the BoT IoT dataset used is highly imbalanced, we also improve the accuracy for detecting minority classes by generating more datasamples using a Conditional Tabular Generative Adversarial Network (CTGAN). In general, our proposed model outperforms other published models including our previous model. Using our proposed methodology, the F1-score of one of the minority class, i.e., Theft attack was improved from 42% to 99%.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model.

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network.

The coronavirus pandemic has affected people all over the world and posed a great challenge to international health systems. To aid early detection of coronavirus disease-2019 (COVID-19), this study proposes a real-time detection system based on the Internet of Things framework. The system collects real-time data from users to determine potential coronavirus cases, analyses treatment responses for people who have been treated, and accurately collects and analyses the datasets. Artificial intelligence-based algorithms are an alternative decision-making solution to extract valuable information from clinical data. This study develops a deep learning optimisation system that can work with imbalanced datasets to improve the classification of patients. A synthetic minority oversampling technique is applied to solve the problem of imbalance, and a recursive feature elimination algorithm is used to determine the most effective features. After data balance and extraction of features, the data are split into training and testing sets for validating all models. The experimental predictive results indicate good stability and compatibility of the models with the data, providing maximum accuracy of 98% and precision of 97%. Finally, the developed models are demonstrated to handle data bias and achieve high classification accuracy for patients with COVID-19. The findings of this study may be useful for healthcare organisations to properly prioritise assets.

Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view.

Since December 2019, we have been in the battlefield with a new threat to the humanity known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this review, we describe the four main methods used for diagnosis, screening and/or surveillance of SARS-CoV-2: Real-time reverse transcription polymerase chain reaction (RT-PCR); chest computed tomography (CT); and different complementary alternatives developed in order to obtain rapid results, antigen and antibody detection. All of them compare the highlighting advantages and disadvantages from an analytical point of view. The gold standard method in terms of sensitivity and specificity is the RT-PCR. The different modifications propose to make it more rapid and applicable at point of care (POC) are also presented and discussed. CT images are limited to central hospitals. However, being combined with RT-PCR is the most robust and accurate way to confirm COVID-19 infection. Antibody tests, although unable to provide reliable results on the status of the infection, are suitable for carrying out maximum screening of the population in order to know the immune capacity. More recently, antigen tests, less sensitive than RT-PCR, have been authorized to determine in a quicker way whether the patient is infected at the time of analysis and without the need of specific instruments.

Distributed Energy IoT-Based Real-Time Virtual Energy Prosumer Business Model for Distributed Power Resource.

Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

Defining a Cyber Resilience Investment Strategy in an Industrial Internet of Things Context.

The fourth industrial revolution has brought several risks to factories along with its plethora of benefits. The convergence of new technologies, legacy technologies, information technologies and operational technologies in the same network generates a wide attack surface. At the same time, factories need continuous production to meet their customers' demand, so any stopped production can have harsh effects on a factory's economy. This makes cyber resilience a key requirement in factories nowadays. However, it is difficult for managers to define effective cyber resilience strategies, especially considering the difficulty of estimating adequate investment in cyber resilience policies before the company has suffered cyber incidents. In this sense, the purpose of this article is to define and model an effective cyber resilience strategy. To achieve this, the system dynamics methodology was followed in order to get five experts' opinions on the best strategy to invest in cyber resilience. Interviews were conducted with these experts; their reasoning was put into behavior over time graphs and a system dynamics model was built from these findings. The main conclusion is that a cyber resilience investment strategy should be dynamic, investing in both technical security and personnel training, but at first with an emphasis on technical security and later shifting to have an emphasis on training.

Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care.

Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO2 predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO2 levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO2 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%.

Indoor Air Quality Assessment Using a CO2 Monitoring System Based on Internet of Things.

Indoor air quality (IAQ) parameters are not only directly related to occupational health but also have a significant impact on quality of life as people typically spend more than 90% of their time in indoor environments. Although IAQ is not usually monitored, it must be perceived as a relevant issue to follow up for the inhabitants' well-being and comfort for enhanced living environments and occupational health. Carbon dioxide (CO2) has a substantial influence on public health and can be used as an essential index of IAQ. CO2 levels over 1000 ppm, indicates an indoor air potential problem. Monitoring CO2 concentration in real-time is essential to detect IAQ issues to quickly intervene in the building. The continuous technological advances in several areas such as Ambient Assisted Living and the Internet of Things (IoT) make it possible to build smart objects with significant capabilities for sensing and connecting. This paper presents the iAirCO2 system, a solution for CO2 real-time monitoring based on IoT architecture. The iAirCO2 is composed of a hardware prototype for ambient data collection and a Web and smartphone software for data consulting. In future, it is planned that these data can be accessed by doctors in order to support medical diagnostics. Compared to other solutions, the iAirCO2 is based on open-source technologies, providing a total Wi-Fi system, with several advantages such as its modularity, scalability, low-cost, and easy installation. The results reveal that the system can generate a viable IAQ appraisal, allowing to anticipate technical interventions that contribute to a healthier living environment.

Defence against Black Hole and Selective Forwarding Attacks for Medical WSNs in the IoT.

Wireless sensor networks (WSNs) are being used to facilitate monitoring of patients in hospital and home environments. These systems consist of a variety of different components/sensors and many processes like clustering, routing, security, and self-organization. Routing is necessary for medical-based WSNs because it allows remote data delivery and it facilitates network scalability in large hospitals. However, routing entails several problems, mainly due to the open nature of wireless networks, and these need to be addressed. This paper looks at two of the problems that arise due to wireless routing between the nodes and access points of a medical WSN (for IoT use): black hole and selective forwarding (SF) attacks. A solution to the former can readily be provided through the use of cryptographic hashes, while the latter makes use of a neighbourhood watch and threshold-based analysis to detect and correct SF attacks. The scheme proposed here is capable of detecting a selective forwarding attack with over 96% accuracy and successfully identifying the malicious node with 83% accuracy.

Machine learning and deep learning approaches in IoT.

The internet is a booming sector for exchanging information because of all the gadgets in today's world. Attacks on Internet of Things (IoT) devices are alarming as these devices evolve. The two primary areas of the IoT that should be secure in terms of authentication, authorization, and data privacy are the IoMT (Internet of Medical Things) and the IoV (Internet of Vehicles). IoMT and IoV devices monitor real-time healthcare and traffic trends to protect an individual's life. With the proliferation of these devices comes a rise in security assaults and threats, necessitating the deployment of an IPS (intrusion prevention system) for these systems. As a result, machine learning and deep learning technologies are utilized to identify and control security in IoMT and IoV devices. This research study aims to investigate the research fields of current IoT security research trends. Papers about the domain were searched, and the top 50 papers were selected. In addition, research objectives are specified concerning the problem, which leads to research questions. After evaluating the associated research, data is retrieved from digital archives. Furthermore, based on the findings of this SLR, a taxonomy of IoT subdomains has been given. This article also identifies the difficult areas and suggests ideas for further research in the IoT.

IoT technologies in smart environment: security issues and future enhancements.

IoT plays an important role in the overall development and advancement of the country as it is the key ingredient for the development of the smart environment. IoT is a network of physical objects, devices that contain embedded technologies such as sensors, controllers, etc., which can sense, communicate, and interact with the system to carry out desired operations. The advancement in technology over the past years has provided a new era for computational processing and sensing to facilitate the vision of a smart environment. Researchers have put several efforts to use IoT to facilitate our lives. This paper purposes on an integrated smart environment using IoT. Various sectors such as agriculture, transportation, garbage collection, security issues, sensors, etc. are discussed along with the key technologies including RFID, IP, EPC, Wi-Fi, Bluetooth, and ZigBee. This paper will provide a complete insight into the one who wants to research in the field of IoT. It also highlights the unprecedented opportunities brought by IoT-based technologies to human life. Finally, we have discussed the future enhancements in IoT.

Using industrial 4.0 technologies to combat the COVID-19 pandemic.

The COVID 19 (Coronavirus) pandemic has led to a surge in the demand for healthcare devices, pre-cautions, or medicines along with advanced information technology. It has become a global mission to control the Coronavirus to prevent the death of innocent people. The fourth industrial revolution (I4.0) is a new approach to thinking that is proposed across a wide range of industries and services to achieve greater success and quality of life. Several initiatives associated with industry 4.0 are expected to make a difference in the fight against COVID-19. Implementing I4.0 components effectively could lead to a reduction in barriers between patients and healthcare workers and could result in improved communication between them. The present study aims to review the components of I4.0 and related tools used to combat the Coronavirus. This article highlights the benefits of each component of the I4.0, which is useful in controlling the spread of COVID-19. From the present study, it is stated that I4.0 technologies could provide an effective solution to deal with local as well as global medical crises in an innovative way.