Secure transmission for IoT wireless energy-carrying communication systems.

The wireless energy-carrying communication method for the Internet of Things (IoT) presents several difficulties for information security such as eavesdropping or data loss. To solve these issues, this paper presents a new secure transmission method for IoT wireless energy-carrying communication systems. In this method, first the secret message is turned into a word, delivered to the intended recipient and unlawful listener, respectively, and the received message is characterized as an entropy function. The message is iteratively solved using the block coordinate descent technique, and for each iteration, a digital baseband signal containing the receiver's secret message symbol and the matching beamforming vector is delivered. By concurrently optimizing the transmit beamforming vector, the noise covariance matrix, and the receiver power allocation factor based on a design that complies with the security rate and energy acquisition limitations for each receiver, the overall system transmit power is reduced. The Lagrangian method is used to solve the secure transmission problem of the communication system based on an iterative block coordinate descent algorithm, as well as to change the nonconvex problem into a convex problem and precisely derive the upper and lower bounds of the original transmission problem. In comparison to the conventional policy transmission scheme, the experimental results demonstrate that the DIPS (Digital Image Processing System) scheme can increase the STP (Signaling Transfer Point) by approximately 34.16 percent in the eavesdropper independent eavesdropping and joint eavesdropping scenarios. The usefulness of the secure transmission strategy for wireless energy-carrying communication systems is confirmed by this investigation.

Sistema inteligente para la medición del comportamiento humano en relación al cumplimiento del protocolo de bioseguridad Covid-19 implementando tecnología de IoB: aplicación en ITCA-FEPADE Sede Central / Smart system for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing IoB technology: application at ITCA-FEPADE

Este proyecto fue desarrollado por la Escuela de Ingeniería en Computación de ITCA-FEPADE y tuvo como objetivo usar las tecnologías para ayudar a mejorar el comportamiento de la comunidad educativa en pandemia Covid-19. Es un sistema inteligente para la medición del comportamiento humano con relación al cumplimiento del protocolo de bioseguridad Covid-19, implementando tecnologías de Internet del Comportamiento IoB, Internet de las Cosas IoT, Business Intelligence, Big Data y reconocimiento facial. La primera fase consistió en la toma de requerimientos e investigaciones previas. Posteriormente se diseñó la interfaz del aplicativo que interpreta los datos colectados y la estructura de un dispensador inteligente de alcohol gel para ser impreso en 3D. Finalmente se realizó la programación del sistema y del circuito que conforman el dispositivo. Como resultado se construyó un dispositivo inteligente que mide y alerta la temperatura, dispensa alcohol gel y toma de fotografía para reconocimiento facial en la portación correcta de mascarilla.

This research project was carried out in 2021 by the Escuela de Ingeniería en Computación of ITCA-FEPADE and aimed to use technologies to improve the behavior of the educational community in the context of Covid-19 pandemic. A smart system was development for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing Internet of Behavior (IoB), Internet of Things (IoT), Business Intelligence, Big Data and facial recognition technologies. The first phase consisted on the identification of requirements and previous investigations. Subsequently, the application interface that interprets the collected data and the structure of a smart hand sanitizer dispenser to be printed in 3D was designed. Finally, the programming of the system and the circuit that make up the device was carried out. As a result, a smart device that measures and alerts the body temperature, dispenses hand sanitizer and applies facial recognition for the detection of proper face mask wearing was built.

Development of an IoT-Based Sleep Apnea Monitoring System for Healthcare Applications.

Sleep is an essential and vital element of a person's life and health that helps to refresh and recharge the mind and body of a person. The quality of sleep is very important in every person's lifestyle, removing various diseases. Bad sleep is a big problem for a lot of people for a very long time. People suffering from various diseases are dealing with various sleeping disorders, commonly known as sleep apnea. A lot of people die during sleep because of uneven body changes in the body during sleep. On that note, a system to monitor sleep is very important. Most of the previous systems to monitor sleeping problems cannot deal with the real time sleeping problem, generating data after a certain period of sleep. Real-time monitoring of sleep is the key to detecting sleep apnea. To solve this problem, an Internet of Things- (IoT-) based real-time sleep apnea monitoring system has been developed. It will allow the user to measure different indexes of sleep and will notify them through a mobile application when anything odd occurs. The system contains various sensors to measure the electrocardiogram (ECG), heart rate, pulse rate, skin response, and SpO2 of any person during the entire sleeping period. This research is very useful as it can measure the indexes of sleep without disturbing the person and can also show it in the mobile application simultaneously with the help of a Bluetooth module. The system has been developed in such a way that it can be used by every kind of person. Multiple analog sensors are used with the Arduino UNO to measure different parameters of the sleep factor. The system was examined and tested on different people's bodies. To analyze and detect sleep apnea in real-time, the system monitors several people during the sleeping period. The results are displayed on the monitor of the Arduino boards and in the mobile application. The analysis of the achieved data can detect sleep apnea in some of the people that the system monitored, and it can also display the reason why sleep apnea happens. This research also analyzes the people who are not in the danger of sleeping problems by the achieved data. This paper will help everyone learn about sleep apnea and will help people detect it and take the necessary steps to prevent it.

Protocol for an interventional study to reduce postpartum weight retention in obese mothers using the internet of things and a mobile application: a randomized controlled trial (SpringMom).

BACKGROUND: Obese pregnant women are known to experience poorer pregnancy outcomes and are at higher risk of postnatal arteriosclerosis. Hence, weight control during and after pregnancy is important for reducing these risks. The objective of our planned randomized controlled trial is to evaluate whether the rate of change in body weight in obese women before pregnancy to 12 months postpartum would be lower with the use of an intervention consisting of Internet of Things (IoT) devices and mobile applications during pregnancy to 1 year postpartum compared to a non-intervention group. METHODS: Women will be recruited during outpatient maternity checkups at four perinatal care institutions in Japan. We will recruit women at less than 30 weeks of gestation with a pre-pregnancy body mass index ≥ 25 kg/m2. The women will be randomly assigned to an intervention or non-intervention group. The intervention will involve using data (weight, body composition, activity, sleep) measured with IoT devices (weight and body composition monitor, activity, and sleep tracker), meal records, and photographs acquired using a mobile application to automatically generate advice, alongside the use of a mobile application to provide articles and videos related to obesity and pregnancy. The primary outcome will be the ratio of change in body weight (%) from pre-pregnancy to 12 months postpartum compared to before pregnancy. DISCUSSION: This study will examine whether behavioral changes occurring during pregnancy, a period that provides a good opportunity to reexamine one's habits, lead to lifestyle improvements during the busy postpartum period. We aim to determine whether a lifestyle intervention that is initiated during pregnancy can suppress weight gain during pregnancy and encourage weight loss after delivery. TRIAL REGISTRATION: UMIN: UMIN (University hospital Medical Information Network) 000,041,460. Resisted on 18th August 2020. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047278.

Implementation of a MEIoT Weather Station with Exogenous Disturbance Input.

Due to the emergence of the coronavirus disease (COVID 19), education systems in most countries have adapted and quickly changed their teaching strategy to online teaching. This paper presents the design and implementation of a novel Internet of Things (IoT) device, called MEIoT weather station, which incorporates an exogenous disturbance input, within the National Digital Observatory of Smart Environments (OBNiSE) architecture. The exogenous disturbance input involves a wind blower based on a DC brushless motor. It can be controlled, via Node-RED platform, manually through a sliding bar, or automatically via different predefined profile functions, modifying the wind speed and the wind vane sensor variables. An application to Engineering Education is presented with a case study that includes the instructional design for the least-squares regression topic for linear, quadratic, and cubic approximations within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work's main contribution to the state-of-the-art is to turn a weather monitoring system into a hybrid hands-on learning approach thanks to the integrated exogenous disturbance input.

The Internet of Things enhancing animal welfare and farm operational efficiency.

The growth in wirelessly enabled sensor network technologies has enabled the low cost deployment of sensor platforms with applications in a range of sectors and communities. In the agricultural domain such sensors have been the foundation for the creation of decision support tools that enhance farm operational efficiency. This Research Reflection illustrates how these advances are assisting dairy farmers to optimise performance and illustrates where emerging sensor technology can offer additional benefits. One of the early applications for sensor technology at an individual animal level was the accurate identification of cattle entering into heat (oestrus) to increase the rate of successful pregnancies and thus optimise milk yield per animal. This was achieved through the use of activity monitoring collars and leg tags. Additional information relating to the behaviour of the cattle, namely the time spent eating and ruminating, was subsequently derived from collars giving further insights of economic value into the wellbeing of the animal, thus an enhanced range of welfare related services have been provisioned. The integration of the information from neck-mounted collars with the compositional analysis data of milk measured at a robotic milking station facilitates the early diagnosis of specific illnesses such as mastitis. The combination of different data streams also serves to eliminate the generation of false alarms, improving the decision making capability. The principle of integrating more data streams from deployed on-farm systems, for example, with feed composition data measured at the point of delivery using instrumented feeding wagons, supports the optimisation of feeding strategies and identification of the most productive animals. Optimised feeding strategies reduce operational costs and minimise waste whilst ensuring high welfare standards. These IoT-inspired solutions, made possible through Internet-enabled cloud data exchange, have the potential to make a major impact within farming practices. This paper gives illustrative examples and considers where new sensor technology from the automotive industry may also have a role.

A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications.

A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO2) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a -10 dB impedance bandwidth of 36 MHz, a peak realized gain of -4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.

Advanced Data Analytics for Clinical Research Part I: What are the Tools?

The concept of Big Data is changing the way that clinical research can be performed. Cardiothoracic surgeons need to understand the dynamic digital transformation taking place in the healthcare industry. In the last decade, technological advances and Big Data analytics have become powerful tools for businesses. In healthcare, rapid expansion of Big Data infrastructure has occurred in parallel with attempts to reduce cost and improve outcomes. Many hospitals around the country are augmenting traditional relational databases with Big Data infrastructure. Advanced data capture and categorization tools such as natural language processing and optical character recognition are being developed for clinical and research use, while Internet of Things in the form of wearable technology serves as an additional source of data usable for research. As cardiothoracic surgeons seek ways to innovate, novel approaches to data acquisition and analysis enable a more rigorous level of investigatory efforts.

Advanced Data Analytics for Clinical Research Part II: Application to Cardiothoracic Surgery.

In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances to the utilization of these analytics tools, which must be well understood by clinicians seeking to take advantage of these innovative research strategies. One must recognize technical challenges to NLP, such as unintended search outcomes and variability in the expression of human written texts. Other caveats include dealing written texts in image formats, which may ultimately be handled with transformation to text format by OCR, though this technology is still under development. IoT is beginning to be used in cardiac monitoring, medication adherence alerts, lifestyle monitoring, and saving traditional labs from equipment failure catastrophes. These technologies will become more prevalent in the future research landscape, and cardiothoracic surgeons should understand the advantages of these technologies to propel our research to the next level. Experience and understanding of technology are needed in building a robust NLP search result, and effective communication with the data management team is a crucial step in successful utilization of these technologies. In this second installment of the series, we provide examples of published investigations utilizing the advanced analytic tools introduced in Part I. We will explain our processes in developing the research question, barriers to achieving the research goals using traditional research methods, tools used to overcome the barriers, and the research findings.

Effectiveness of Lifestyle Intervention Using the Internet of Things System for Individuals with Early Type 2 Diabetes Mellitus.

Objective Exercise therapy is used for glycemic control in type 2 diabetes mellitus (T2DM). We evaluated the effects of intensive health guidance using the Internet of things (IoT) among Japanese company workers with early T2DM. Methods Fifty-three men (mean age: 54 years) with glycated hemoglobin (HbA1c) levels of >6.5% were enrolled in a 6-month exercise therapy program between August 2016 and January 2017. They used activity meters, scales, and sphygmomanometers connected to the Internet by Bluetooth. These devices automatically and continuously recorded daily information, and the participants simultaneously received health guidance from a public health nurse twice a month. Results The number of daily steps significantly increased, whereas the amount of physical activity increased but was not significant. The mean decrease (±SD) in HbA1c levels after 3 and 6 months was estimated to be -0.40% (±0.45, p<0.0001) and -0.19% (±0.55, p=0.033), respectively, by a linear mixed model that included baseline HbA1c levels and age as covariates. The program failed to improve the body mass index and blood pressure of the participants. The percentage of active stage (action and maintenance stage) in stage of health behavior significantly increased from 48% to 68% (p=0.011). Conclusion Intensive lifestyle intervention using a wearable monitoring system and remote health guidance improved diabetic control in middle-aged company workers.

Robust Real-Time Embedded EMG Recognition Framework Using Temporal Convolutional Networks on a Multicore IoT Processor.

Hand movement classification via surface electromyographic (sEMG) signal is a well-established approach for advanced Human-Computer Interaction. However, sEMG movement recognition has to deal with the long-term reliability of sEMG-based control, limited by the variability affecting the sEMG signal. Embedded solutions are affected by a recognition accuracy drop over time that makes them unsuitable for reliable gesture controller design. In this paper, we present a complete wearable-class embedded system for robust sEMG-based gesture recognition, based on Temporal Convolutional Networks (TCNs). Firstly, we developed a novel TCN topology (TEMPONet), and we tested our solution on a benchmark dataset (Ninapro), achieving 49.6% average accuracy, 7.8%, better than current State-Of-the-Art (SoA). Moreover, we designed an energy-efficient embedded platform based on GAP8, a novel 8-core IoT processor. Using our embedded platform, we collected a second 20-sessions dataset to validate the system on a setup which is representative of the final deployment. We obtain 93.7% average accuracy with the TCN, comparable with a SoA SVM approach (91.1%). Finally, we profiled the performance of the network implemented on GAP8 by using an 8-bit quantization strategy to fit the memory constraint of the processor. We reach a 4× lower memory footprint (460 kB) with a performance degradation of only 3% accuracy. We detailed the execution on the GAP8 platform, showing that the quantized network executes a single classification in 12.84 ms with a power envelope of 0.9 mJ, making it suitable for a long-lifetime wearable deployment.