To develop an Indoor Air Quality (IAQ) monitoring and detecting system based on a new Internet of Thing (IoT) sensory technology device that incorporated nine recommended indoor pollutants by the academic literature and reliable organizations, such as World Health Organization (WHO), Environmental Protection Agency (EPA), and International Organization for Standardization (ISO). The pollutants include Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2), Ozone (O3), Formaldehyde (HCHO), Volatile Organic Compounds (VOC), Particulate Matter 2.5 (PM2.5) as well as air humidity and temperature that are used to assess the variety of indoor pollutants and provide a new IAQ pollutants dataset. Besides, the newly developed system provides real-time air quality monitoring, reports the pollutants’ data to a cloud platform (i.e., ThingSpeak), and can trigger early warnings as a service when abnormalities occurred in the air quality index. The system was tested to ensure its conformance to the recommended pollutants by collaborating with surgeons and specializing in IAQ in a hospital surgical intensive care unit (SICU), emergency department (ED), and in the women’s ward, which accommodate patients who are either newly born mothers (in case they need that) or who have had an operation, as well as pregnant patients who need to stay in the hospital to be under the supervision of medical care. Nine pollutants were identified and collected the pollutants dataset and their thresholds that affect the air quality within the hospital facilities and services (SICU, ED) to be used for assessing the effectiveness of the amount, concentration, and diversity of the pollutants. In the SICU, the concentrations of some pollutants were high in the beginning due to the residues of the previous surgery and because of the frequent use of sterilizers to clean and prepare the surgery room. Then, the concentrations of pollutants were moderate, but minutes after the start of the surgical, an increase in CO2 and formaldehyde was observed, which exceeds the threshold limit because of the use of anesthetic gas and sterilization. In the women’s ward, was all concentrations generally moderate except for particles matter PM2.5, and the same context with the 3rd installed location in the pharmacy of ED, most concentrations were moderate, except formaldehyde which exceeded the threshold. “CO” was the highest positive correlated and strongly correlated to “NO2” and that was expected because CO influences the oxidation of NO to NO2. On the contrary, the “CO” had the highest negative correlation with “VOC”, and the “NO2” had the highest negative correlation with “VOC”, chemistry is part of the responsibility for the weak correlation observed between the pollutants.

https://oss.signavitae.com/mre-signavitae/article/20220224-85/pdf/SV2021103001.pdf

Development of a real-time monitoring and detection indoor air quality system for intensive care unit and emergency department

COCOMA: a resource-optimized cooperative UAVs communication protocol for surveillance and monitoring applications

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring.

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Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

The outbreak of COVID-19 has severely affected the healthcare infrastructure. The limitations of conventional healthcare urge the use of the digital technologies to lessen the overall load on the healthcare infrastructure and assist healthcare workers/staff. This chapter focuses on digital technologies to enable smart healthcare solutions to sustain and improve health services. The chapter focuses on two main driving technologies (internet of things [IoT] and artificial intelligence [AI]), pioneering automation and digitalization of healthcare. The enabling technologies possess the potential to transform the healthcare with emergence of new and novel research directions to realize and address healthcare needs. Therefore, it is essential to focus on key driving and complementing technologies to establish multidisciplinary research solutions with cross-platform design coupled with translational learning to unlock the potentials of next generation healthcare.

Overview of IoT and Machine Learning for E-Healthcare in Pandemics and Health Crises

Many healthcare organizations have developed the practice of acquiring essential medical supplies and equipment, storing vital medications, meeting medical needs, and scheduling patient treatments without coordinating and synchronizing those actions. Professionals in the supply chain are finding it difficult to keep up with the vast amounts of data that are needed to create an integrated, efficient, effective, and agile supply chain in today’s complex, connected world. As a result, there is now a high demand for systems that can swiftly and intelligently analyze diverse large amounts of supply chain data. Artificial Intelligence (AI) is one of the tools used to resolve the emerging industrial challenges. This study intends to explore how AI is used to manage e-Healthcare supply chains while also focusing on numerous security and privacy issues that occur in e-healthcare environment. 

Artificial Intelligence in e-Healthcare Supply Chain Management System: Challenges and Future Trends

The emerging challenges in healthcare, especially with the coronavirus (COVID-19) crisis, has changed the way healthcare operates. Countries where the pandemic has hit hard have brought healthcare institutes to the verge of collapsing, where the capabilities of healthcare departments and hospitals are tested over and over. In these challenging circumstances, the technology alternatives are stressed as never before, and the need for transformation of healthcare from traditional techniques to technology-driven healthcare solutions is advocated. While the Internet of Things (IoT) and other healthcare technologies (machine learning, cloud, edge computing, security) have been under development for years, none of the developments were planned to sustain immense pressure, such as that experienced during pandemics and special circumstances. Therefore, a suitable transformation in healthcare technologies is very desirable to cope with the exacerbating world healthcare infrastructure. This article discusses the role of IoT and intelligent healthcare services in emerging health-related threats and challenges. It discusses the limitations, challenges, and future of IoT in health crises. The article also stresses extensive healthcare infrastructure, which benefits from IoT, artificial intelligence, distributed control, cognitive decision support services, security and privacy, blockchain, and cloud and edge services. We use the heuristic bargaining algorithm to maximize the profit of the participants of the task migration in order to increase their enthusiasm.

Challenges and Limitations of Internet of Things Enabled Healthcare in COVID-19

The massive increase in population density in cities has led to several urban problems, such as an increment of air pollution, traffic congestion, and a faster spread of infectious diseases. With the rapid innovation in the intelligent sensors technology, and its integration into smart vehicles and Unmanned Aerial Vehicles (UAVs), a novel sensing paradigm has been promoted, namely vehicular crowdsensing, which leverages on-board sensors to capture information from the surrounding environment. Collected data are then analyzed to take proper countermeasures. In this paper, we present a smart coordination mechanism between UAVs and ground vehicles (GVs), which sense information like body temperature and breathing rate of people, in order to support a variety of monitoring applications, including discovering the presence of infectious diseases. In our framework, namely GUAVA, aerial and ground vehicles are equipped with GPS devices and thermal cameras to monitor specific geographic areas, detect humans’ vital parameters and, at the same time, discover duplicate data by identifying matching faces in thermal video sequences with the GaussianFace algorithm. The sensing tasks in hard-to-reach places are assigned to UAVs, with the ability to power up wirelessly from the nearest GV and offload the collected monitoring images to it. Simulation results have assessed our proposed framework, showing good performance in terms of distinct Quality of Service (QoS) metrics.

Muhammad Imran

Papers

Challenges and Limitations of Internet of Things Enabled Healthcare in COVID-19

A cooperative crowdsensing system based on flying and ground vehicles to control respiratory viral disease outbreaks