Neighborhood-Level Particle Pollution Assessment during the COVID-19 Pandemic via a Novel IoT Solution

In recent years, the concentration levels of various air pollutants have been constantly increasing, primarily due to the high vehicle flow. In 2020, however, severe lockdowns in Greece were imposed to reduce the spread of the COVID-19 pandemic, which led to a rapid reduction in the concentration levels of air pollutants such as PM2.5 and PM10 in the atmosphere. Initially, this paper seeks to identify the correlation between the concentration levels of PM10 and the traffic flow by acquiring data from low-cost IoT devices which were placed in Thessaloniki, Greece, from March to August 2020. The correlation and the linearity between the two parameters were further investigated by applying descriptive analytics, regression techniques, Pearson correlation, and independent T-testing. The obtained results indicate that the concentration levels of PM10 are strongly correlated to the vehicle flow. Therefore, the results hint that the decrease in the vehicle flow could result in improving the quality of environmental air. Finally, the acquired results point out that the temperature and humidity are weakly correlated with the concentration levels of PM10 present in the atmosphere.

Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients.

By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.

IoT-based indoor access control and monitoring: a case study from the containment measures of the COVID-19 pandemic

The COVID-19 pandemic has led many serving environments to seek solutions to control people's access and avoid crowding in order to contain its spread, and to ensure the health and safety of users. Given the various current solutions, this paper presents a monitoring system that shows, in real-time, via web, the status of people in closed environments. It uses internet of things (IoT) techniques for data interconnections and electronic components - NodeMCU board and proximity sensors - to monitor the entrance and exit of people in an enclosed environment, providing the statistics through an IoT platform (application) that can be installed in a mobile device (smartphone). This study highlights a low-budget system, shows the implementation of IoT platforms in the development of prototypes and the tests carried out in the academic service office.

IoT System for Vital Signs Monitoring in Suspicious Cases of Covid-19

Currently the world is going through a pandemic caused by Covid-19, the World Health Organization recommends to stay isolated from the rest of the people. This research shows the development of a prototype based on the internet of things, which aims to measure three very important aspects: heart rate, blood oxygen saturation and body temperature, these will be measured through sensors that will be connected to a NodeMCU module that integrates a Wi-Fi module, which will transmit the data to an IoT platform through which the data can be displayed, achieving real-time monitoring of the vital signs of the patient suspected of Covid-19.

Information technology platform for the selection and analytical processing of information on COVID-19

Currently, the most effective way to counteract COVID-19 is to slow its spread through personal distancing, hand washing and the use of personal protective equipment. Vaccination processes of citizens are becoming widespread. At the same time, information technology will be able to help slow down the spread of COVID-19 by early detection, prediction and monitoring of new cases. This paper provides an overview of current research on the selection and processing of COVID-19 data. The role and location of IoT devices, communication networks and cloud infrastructure for the selection and processing of COVID-19 data are described. Based on the analysis of IoT-platforms for detection and monitoring of COVID-19, the structure of the information technology platform was formed. There are included data collection tools, primary networks, Internet, cloud infrastructure, data presentation tools. The architecture of the information technology platform for the selection and processing of COVID-19 data is proposed. A description of the process of collecting and analytical processing of COVID-19 information using machine learning algorithms is given. The model of the information technology platform classes structure for the selection and processing of COVID-19 data is considered. That contains more than 50 classes to describe more than 120 characteristics of information entities. The processes of selection and aggregation of COVID-19 data and integration of analytical processing tools based on machine learning algorithms into the information technology platform are described. © 2021 IEEE.

CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19.

The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that govern the outbreak of a contagious disease. The understanding of COVID-19 will be enriched by the analysis of data related to the phenomena, and this data can be collected using IoT sensors. In this paper, we show an integrated solution based on IoT technologies that can serve as opportunistic health data acquisition agents for combating the pandemic of COVID-19, named CIoTVID. The platform is composed of four layers-data acquisition, data aggregation, machine intelligence and services, within the solution. To demonstrate its validity, the solution has been tested with a use case based on creating a classifier of medical conditions using real data of voice, performing successfully. The layer of data aggregation is particularly relevant in this kind of solution as the data coming from medical devices has a very different nature to that coming from electronic sensors. Due to the adaptability of the platform to heterogeneous data and volumes of data; individuals, policymakers, and clinics could benefit from it to fight the propagation of the pandemic.