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Published: 11 September 2021
Sustainable Cities and Society; https://doi.org/10.1016/j.scs.2021.103311

Abstract:
COVID-19 is a global infectious disease that can be easily spread by the contiguity of infected people. To prevent from COVID-19 and reduce its impact in sustainable smart cities, the global research communities are working relentlessly by harnessing the emerging technologies to develop the safest diagnosis, evaluation, and treatment procedures, and Internet of Things (IoT) is one of the pioneers among them. IoT can perform a pivotal role to diminish its immense contagious rate by suitable utilization in emerging healthcare IoT applications in sustainable smart cities. Therefore, the focus of this paper is to outline a survey of the emerging healthcare IoT applications practiced in the perspective of COVID-19 pandemic in terms of network architecture security, trustworthiness, authentication, and data preservation followed by identifying existing challenges to set the future research directions. The salient contributions of this work deal with the accomplishment of a detailed and comprehensive literature review of COVID-19 starting from 2019 through 2021 in the context of emerging healthcare IoT technology. In addition, we extend the correlated contributions of this work by highlighting the weak aspects of the existing emerging healthcare IoT applications, security of different network layers and secure communication environment followed by some associated requirements to address these challenges. Moreover, we also identify future research directions in sustainable smart cities for emerging healthcare IoT utilization in the context of COVID-19 with the most productive results and least network implementation costs.
, Ibrahim Abaker Targio Hashem, Olaide N. Oyelade, Mubarak Almutari, Mohammed A. Al-Garadi, Idris Nasir Abdullahi, , Amit K. Shukla,
Published: 11 September 2021
BioMed Research International, Volume 2021, pp 1-15; https://doi.org/10.1155/2021/5546790

Abstract:
The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.
, Vanshdeep Sahni
Lecture Notes in Electrical Engineering pp 169-179; https://doi.org/10.1007/978-981-16-4149-7_14

Abstract:
The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 has brought new techniques/technologies which are emerging a lot in today’s research industry including 5G, Internet-of-Things (IoT), Edge Computing, Blockchain, etc., to more light. This is attracting more researchers across the Globe to enhance the existing models shared through different platforms. In addition to that, many have come across having humanoids/robots deployed into streets for serving and public announcements, delivering medications or emergency support onto doorsteps. These technologies are also helping many organizations across planet to explore COVID-19 related useful solutions which can help to manipulate and process data in real time. In this paper, authors are concentrating on integrating the algorithmic approach of Edge Computing & Drone Technology which can enlighten the challenges of parameters namely latency, connectivity, data processing, onsite computation. etc. In addition to that, proposing an architecture for the design of Edge-based integrated Drone use case for the containment zones detection with offline data processing & computation and deployment of algorithm for thermal sensing. This approach of making UAV bandwidth-independent architecture will create new opportunities for exploring COVID Pandemic smoothness for every aspect of social duties.
Published: 8 September 2021
by MDPI
Sensors, Volume 21; https://doi.org/10.3390/s21186023

Abstract:
Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks. Thus, there is an immediate need to push AI (artificial intelligence) breakthroughs within edge networks to achieve the full promise of edge data analytics. EI solutions have supported digital technology workloads and applications from the infrastructure level to edge networks; however, there are still many challenges with the heterogeneity of computational capabilities and the spread of information sources. We propose a novel event-driven deep-learning framework, called EDL-EI (event-driven deep learning for edge intelligence), via the design of a novel event model by defining events using correlation analysis with multiple sensors in real-world settings and incorporating multi-sensor fusion techniques, a transformation method for sensor streams into images, and lightweight 2-dimensional convolutional neural network (CNN) models. To demonstrate the feasibility of the EDL-EI framework, we presented an IoT-based prototype system that we developed with multiple sensors and edge devices. To verify the proposed framework, we have a case study of air-quality scenarios based on the benchmark data provided by the USA Environmental Protection Agency for the most polluted cities in South Korea and China. We have obtained outstanding predictive accuracy (97.65% and 97.19%) from two deep-learning models on the cities’ air-quality patterns. Furthermore, the air-quality changes from 2019 to 2020 have been analyzed to check the effects of the COVID-19 pandemic lockdown.
, Wanmin Lian, Junzhang Tian
Published: 6 September 2021
Abstract:
UNSTRUCTURED The COVID-19 pandemic has accelerated the trend of smart hospital construction that has been cooking for years. However, there is no consistent conceptualization of what a smart hospital truly entails. A few hospitals have truly reached the status of being ‘smart’ so far, primarily failing to bring systems together and consider implications from all perspectives. Hospital Intelligent Twins, a new technology integration was first introduced by Huawei at HUAWEI CONNECT 2020, that powered by IoT, AI and cloud computing, as well as 5G application to create all-scenario intelligence for health care and hospital management.This article presents a smart hospital for all-scenario intelligence by creating the hospital Intelligent Twins.
, N. Mahesh, Asifullakhan, H. L. Gururaj, K. S. Vinayaka, P. Subramanya
Convergence of Internet of Things and Blockchain Technologies pp 165-192; https://doi.org/10.1007/978-3-030-76216-2_11

Abstract:
Coronavirus disease (COVID-19), triggered by the upsurge of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as a pandemic (widespread) in recent days which has made the life of people all over the globe standstill. Thus, in this strenuous situation, there is a need for applications that automate the process and complete with the minimum usage of human resources and well within the stipulated time along with security considerations. To mitigate the above-said challenges, ingenious technologies like Blockchain and Internet of Things (IoT) have come to light as promising solutions. The main intention of using IoT with Blockchain is to strengthen security and bring transparency in data collected from various external devices. Hence, this paper presents an exhaustive summary and review of the various Blockchain applications integrated with IoT to combat this COVID-19 situation. Taking into account a decent bunch of well-organized scholastic literature, we have broadly classified Blockchain-based IoT applications into four major segments which are smart healthcare, government, banking, and supply chain management to sustain in these difficult times. We shed light on a collective survey of COVID impact on abovementioned four segments, and then, we discuss measurable preventive actions that Blockchain with IOT can provide, and finally, we pull out challenges and future considerations that shed light on more research efforts in order to handle future coronavirus-like pandemic.
Dhawan Singh, Aditi Thakur, Maninder Singh, AmanPreet Sandhu
International Journal of Computer Applications in Technology, Volume 65; https://doi.org/10.1504/ijcat.2021.117303

Abstract:
The world, at present, is witnessing grave challenges to its established institutions and shared beliefs due to the outbreak of novel Coronavirus. Almost all of our establishments are under threat and unprecedented disruptions are being witnessed across all spheres of life. Besides medical hunt for discovering the cure, there exists an equally significant need to invent technological solutions for restoring numerous services while considering the restrictions imposed by the pandemic. Therefore, in this research work, we have investigated and analysed the possibilities, opportunities, and applications of IoT technology in the field of food safety and quality control, automatic disinfectant, healthcare systems, wearable health devices, and personal hygiene. We have assessed various features of currently available IoT design platforms and standard protocols and proposed feasible and dynamic strategies for their implementations. The efficacy of the system demonstrates an immense possibility for the continuation of IoT-based technology, even after the Coronavirus scare is over.
, Isa Hafidz, Billy Montolalu, Fauzan Rasyid
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control; https://doi.org/10.22219/kinetik.v6i3.1281

Abstract:
The emergency of the healthcare device unit, such as a ventilator, has been experienced during the COVID-19 pandemic in 2020. Therefore, ventilator usage is not hard suggested anymore for COVID-19 patients compared to the outbreak beginning. Despite that, it is still essential to have the ventilator ready, if possible, in each house, for the upcoming respiratory syndrome outbreak. Therefore, in this paper, a digital ventilator development is presented. The digital ventilator is comprised of three main parts, such as respiration mechanism (I), controller Internet of Things (IoT) module (II), and website application (III). The developed digital ventilator has been tested by comparing the measurement of respiratory data between the developed digital ventilator and gas flow analyzer. Results show that the respiratory data, such as Pressure Peak (PPeak), Positive End Respiratory Pressure (PEEP), Inspiratory Expiratory Ratio (IE Ratio), Breath per Minute (BPM), and Tidal Volume can be monitored and controlled both directly and online via website application consistently (standard deviation around 10%) with PPeak absolute error of 1.35 mbar, the PEEP absolute error of 0.16 mbar. Furthermore, the average time response of the digital ventilator to the input command from the website application is 0.23 s. Therefore, it is safe to assume that the doctor can use the website application to control the digital ventilator remotely.
Maha Mahmood, Wijdan Jaber Al-Kubaisy,
Advances in Intelligent Systems and Computing pp 859-872; https://doi.org/10.1007/978-981-16-3071-2_68

Abstract:
This paper presents a thorough review for researches that are proposed to address the COVID-19 disease by employing IoT techniques. COVID-19 disease, caused by a large family of coronaviruses, causes illness in humans and animals. The pandemic of COVID-19 has had unforeseeable global implications for the economy, customers, and social life. The lockdown policy restricted the movement of people as the only measure in the battle against the pandemic, reduced the reach of business operations, and changed the patterns of behavior of consumers who turned to stock accumulation in a panic. The Internet of Things (IoT) has been used as technology in a variety of applications. The current major research activities are proof that prosperity tracking by remote sensing is dependent on IoT. IoT should be used to prevent the distribution of COVID-19. IoT is a connection between physical devices and the Internet. Devices can monitor and respond in addition to the sense and record. IoT continues to be an effective tool for keeping track of an infected patient, which will restrict the spread of COVID-19. Technology appears today; in an internet-based environment, particularly in the present pandemic crisis, COVID-19, the feasible use of an educational technology tool can be achieved. The ability to respond to this global challenge continues to have a tremendous impact on the innovative application of digital technologies which can continue to be used to improve performance.
, Shamimul Qamar
Abstract:
Currently, the COVID‐19 has directly affected the millions of humans lives. The symptoms of the disease involving fever, malaise, chest infection, and breathing difficulties, were identified, and its existence is continuously becoming restructured. The World Health Organization (WHO) had mentioned the wide diagnostics test besides COVID-19 that would also assist medical facilities to recognize infectious diseases as well as currently focusing efficiently on preventing and afterward defeating this viral disease. The infection is usually transmitted among human beings in direct contact, greatest through the liquid bubbles generated through cough, sneeze, or speaking. This paper reviews the COVID 19 pandemic, its history, current updates, contact tracing applications, and use of emerging technologies like the Internet of Things (IoT) and Blockchain for stopping the spreading and provide service online to the patient from a distance.
Published: 23 August 2021
by MDPI
Abstract:
The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework.
Wireless Personal Communications pp 1-20; https://doi.org/10.1007/s11277-021-08777-6

Abstract:
Transportation management plays a vital role in the development of the country, with the help of IoT smart transportation has become a reality. Developing a smart and secured transportation system of food products to various shops during this pandemic period is an important task. The vehicle tracking system is the technology that is used by many companies and individuals to track a vehicle by using many ways like GPS that operates using satellites and ground-based stations. In this paper an Internet of Things based application is developed to monitor the moving vehicle, this proposed model provides a monitoring solution for a moving vehicle with the help of sensors Blind Spot Assist sensor, Collision Prevention sensor, Fuel Monitoring sensor, Door Sensor, and GPS/GPRS tracking module are integrated to make a smart vehicle prototype using raspberry pi. In this model, a Blind spot sensor is used to monitor the nearby vehicles, a Collision Prevision sensor is used to avoid the collision between the vehicles, a Fuel monitoring sensor is used to monitor the fuel level in the vehicle, the Door sensor is used to check the status of the door and GPS/GPRS tracking module is used to track the current location of the moving vehicle during the COVID-19 Pandemic period.
Amr Eltawil, , Yoshihisa Matsushita
Resilient and Responsible Smart Cities pp 233-242; https://doi.org/10.1007/978-3-030-63567-1_20

Abstract:
The Internet of Things (IoT) is a buzzing technology nowadays. It is believed to have the potential for revolutionizing today’s world. Several projects and publications addressed the use of IoT in various applications such as smart cities, smart homes, wearables, smart grids, connected vehicles, and health care. However, few works addressed the potential of using IoT in educational institutions. The technological revolution along with several changes through the past era, including the COVID-19 global pandemic, have made it indispensable to employ innovative learning methods, also, the students will be looking forward to existing in the environment of a smart campus. This study presents a framework to elucidate how IoT can be used to bring a smart and innovative university campus to life in order to improve the efficiency of delivering the daily educational activities. In the context of sustainable development, the social and environmental interactions should be considered to provide a sustainable campus. Also, challenges are investigated and research opportunities are highlighted. This framework can be put in practice in guiding universities to adopt novel visions to improve the performance of their educational activities and student life experience by properly adopting and deploying IoT technologies. These concepts are elaborated based on the case of the innovative campus of the Egypt-Japan University of Science and Technology, which is located in New Borg Elarab, Alexandria, Egypt.
Published: 18 August 2021
by MDPI
Sensors, Volume 21; https://doi.org/10.3390/s21165554

Abstract:
With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.
, · Velmurugan, Subbiah Parvathy, · Deepak Gupta, Ashish Khanna, Joel J P C Rodrigues, · K Shankar
Published: 16 August 2021
Abstract:
At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.
Distributed and Parallel Databases pp 1-19; https://doi.org/10.1007/s10619-021-07358-7

Abstract:
The pandemic situation has pretentious the habitual life of the human, it also has surpassed the regional, social, business activities and forced human society to live in a limited boundary. In this paper, the application of the internet of things (IoT) and machine learning (ML) based system to combat pandemic situation in health care application has been discussed. The developed ML and IoT based monitoring system help in tracking the infected persons from the previous data and makes them get isolate from the non-infected person. The developed ML combined IoT system uses parallel computing in tracking the pandemic disease and also in the prevention of pandemic disease by predicting and analysing the data using artificial intelligence. The implementation of ML-based IoT in the pandemic situation in healthcare application has proved its performance in tracking and prevents the spreading of pandemic disease. It also further has a positive impact on reducing medical costs and has recorded improved treatment for infected patients. The proposed methodology has an accuracy of 93 % in monitoring and tracking. The result obtained help in preventing the spread of the pandemic and provide support to the healthcare system.
Published: 4 August 2021
by MDPI
Abstract:
The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory electrocardiogram (ECG) monitoring system is presented. The system’s wearable single-channel data acquisition device supports 25 h of continuous operation. A right leg drive (RLD) circuit supported analog frontend (AFE) with a high common mode rejection ratio (CMRR) of 121 dB and a digitally implemented notch filter is used to suppress power-line frequency interference. The wearable device continuously sends the collected ECG data via Bluetooth to the user’s smartphone. An application on the user’s smartphone renders real-time ECG trace and heart rate and detects abnormal heart rhythms. This data are then shared in real-time with the user’s doctor via a real-time cloud database. An application on the doctor’s smartphone allows real-time visualization of this data and detection of arrhythmias. Simulations and experimental results demonstrate that reliable ECG signals can be captured with low latency and the heart rate computation is comparable to a commercial application. Low cost, scalability, low latency, real-time ECG monitoring, and improved performance of the system make the system highly suitable for the real-time remote identification and management of CVDs in users of rural areas and in the COVID-19 pandemic.
Mohd Hilmi Othman, Mohammed Bamasood
Journal of Advanced Mechanical Engineering Applications, Volume 02, pp 35-40; https://doi.org/10.30880/jamea.2021.02.01.004

Abstract:
This paper provides a review about the challenges in product design and development (PDD) in the context of the Industrial Revolution 4.0 (IR 4.0), with a particular focused on the problems that may be encountered by the project management (PM) team in the PDD phase. In recent decades, there has been a large number research, design, and development studies related to IR 4.0, such as synthesizing the applications of Big Data, Internet of things (IoT), Cloud Computing, Cybersecurity, and Artificial Intelligence. The effect of this revolution in technology is changing rapidly with new models and methods of manufacturing that have been proposed for the new future. The pandemic Covid-19 also accelerates the interest in using all kinds of online technology. However, to adapt and achieve the benefits of this revolution, industry players have to encounter several issues related to the PM, especially during the PDD phase. The management challenges discussed in this study were divided into four categories: the project team member selection, team leader selection, identifying potential customers, and design for the environment. In addition, some of the solutions and recommendation has been described using several examples.
, Kassahun Dessie Gashu, Zeleke Abebaw Mekonnen, Berhanu Fikadie Endehabtu, Dessie Abebaw Angaw
Yearbook of Medical Informatics, Volume 30, pp 026-037; https://doi.org/10.1055/s-0041-1726505

Abstract:
Summary Background: Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic. Methods: We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis. Results: A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers. Conclusion: Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.
Published: 29 July 2021
Cluster Computing pp 1-18; https://doi.org/10.1007/s10586-021-03367-4

Abstract:
The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed “EdgeSDN-I4COVID” architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.
, Mohammed Riyaz Ahmed, T. Nitesh Kumar, S.R. Prithviraj, A. Shahid Khan
Indian Journal Of Agricultural Research; https://doi.org/10.18805/ijare.a-5709

Abstract:
The COVID-19 influenced global pandemic severely affected the market of small industries and had a deep impact on the agri economic of the farmer community across the globe. The main objective of this article is to emphasize on the influence of global pandemic with agriculture and food sector. The lockdown made ambivalent in agriculture, the point of concern is that, at the first phase of lockdown in India, Rabi crops are at harvest stage, due to the lockdown the breakdown of supply chain has been interrupted and left a noticeable impact on the marketability of agriculture crops even though it has registered moderate growth in terms of yield. At present globally mankind is experiencing the waves of pandemic and it caused significant loss to the yield of crops. If the situation continuous, the world is going to experience the hunger deaths. To overcome the issue discussed, agriculture sector needs to adapt new technologies, right from the cultivation, harvest and supply chain with marketing to bring the new normal life back to mankind. This is the right time to have transition from conventional agri practices to the technology invented smart agriculture. Indian agriculture sector should adapt and the former community need to be educated in applying ICT based smart agriculture practices such as utilization of automated machinery, AI (artificial intelligence) enabled cultivation methods, Internet of Things (IoT) and Wireless Sensor Networks based monitoring and maintenance of the agriculture practice The application ICTs methods in agriculture practices facilitate to choose good quality seeds, optimum quantity of manures required for the enhanced crop yield, and direct monetary of the agriculture firm in order to show resilience to the global pandemic impact on agriculture sector. In the present review authors emphasised on various smart agriculture methods and their importance in promoting the agriculture practice as profitable venture and also how this ICT methods helps the sector to overcome the impact of global pandemic and to bring back the new normal life.
Manoj Agrawal, Shweta Agrawal
Published: 25 July 2021
Disaster Advances pp 90-99; https://doi.org/10.25303/148da9021

Abstract:
The eruption of COVID-19 Corona Virus, namely SARS-CoV-2, has created a disastrous condition throughout the world. The cumulative incidence of COVID-19 is increasing rapidly day by day all over the world. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Deep Learning can support healthcare system to fight and look ahead against fast spreading of new disease COVID-19. These technologies can significantly improve treatment consistency and decision making by developing useful algorithms. These technologies can be deployed very effectively to track the disease, to predict growth of the epidemic, design strategies and policy to manage its spread and drug and vaccine development. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this study aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. This study first presents an overview of AI and big data along with their applications in fighting against COVID-19 and then an attempt is made to standardize ongoing AI and deep learning activities in this area. Finally, this study highlighted challenges and issues associated with State-of-the-Art solutions to effectively control the COVID-19 situation.
Published: 24 July 2021
Materials Today: Proceedings; https://doi.org/10.1016/j.matpr.2021.07.379

Abstract:
The IoT can lead to disruptive healthcare innovation. Research articles on IoT in healthcare and COVID-19 pandemics are thus researched in order to discover the potential of this technology. This literature-based research may help professionals to explore solutions to associated issues and battle the COVID-19 epidemic. Using a process diagram, IoT's significant accomplishments were briefly evaluated. Then seven critical IoT technologies that look useful in healthcare during the COVID-19 Pandemic are identified and illustrated. Finally, in the COVID-19 Pandemic, potential fundamental IoT applications were identified for the medical industry with a short explanation. The present predicament has opened up a fresh avenue to creativity in our everyday lives. The Internet of Things is an up-and-coming technology that enhances and gives better solutions in the medical area, such as appropriate medical record-keeping, sample, device integration, and cause of sickness. IoT's sensor-based technology gives a remarkable ability to lower the danger of intervention in challenging circumstances and is helpful for the pandemic type COVID-19. In the sphere of medicine, IoT's emphasis is on helping to treat diverse COVID-19 situations accurately. It facilitates the work of the surgeon by reducing risks and enhancing overall performance. Using this technology, physicians may readily identify changes in the COVID-19's vital parameters. These information-based services provide new prospects for healthcare as they advance towards the ideal technique for an information system to adapt world-class outcomes by improving hospital treatment systems. Medical students may now be better taught and led in the future for the identification of sickness. Proper use of IoT may assist handle several medical difficulties such as speed, affordability, and complexity appropriately. It may simply be adapted to track patients' calorific intake and therapy with COVID-19 asthma, diabetes, and arthritis. In COVID-19 pandemic days, this digitally managed health management system may enhance the overall healthcare performance.
N. Kishor Narang
IEEE Internet of Things Magazine, Volume 4, pp 52-59; https://doi.org/10.1109/miot.2021.9492905

Abstract:
IoT Standards Matters will look at different segments of the IoT market as it relates to implementation and use of standards. Each column will select a particular vertical, and lay out the relevant standards and technologies that affect the evolving IoT hyperspace. The pace of the columns will start broadly with the vision of narrowing the subject of subsequent articles toward more specific applications of standards, whether in the development, application, test, or commissioning of IoT technologies.
Pallav Kumar Deb, Sudip Misra, Anandarup Mukherjee, Sukriti Shaw
IEEE Internet of Things Magazine, Volume 4, pp 16-19; https://doi.org/10.1109/iotm.0001.2100006

Abstract:
In this article, we propose an IoT-based acoustic solution – Eaves – for ensuring social distancing in public areas during pandemic-like situations. Existing solutions depend on either sensing nearby radio signals such as Bluetooth or through image processing of video frames from surveillance cameras. Such methods either mandate the need for all parties to have the same application or impose line of sight constraints. We overcome such restrictions by using audio to ensure social distancing. The varying amplitude of the audio signals from different distances is the crux of the proposed method. Toward this, we record audio from different distances to extract human-voice-centric components and use the corresponding Mel-frequency cepstral coefficients. We train multiple machine learning models for selecting the one that predicts the distances efficiently with minimum delay and also propose possible IoT-based architectures to overcome resource limitations. Through extensive experiments and deployment, we observe a training accuracy of 97 percent and prediction accuracy of almost 100 percent up to 2 meters.
Gaurav Soni
International Journal for Research in Applied Science and Engineering Technology, Volume 9, pp 1902-1907; https://doi.org/10.22214/ijraset.2021.36789

Abstract:
The aim of the Internet of things (IoT) is to bring every object online. These different objects generate huge data which consequently lead to the need of requirements of efficient storage and processing. Cloud computing is an emerging technology to overcome this problem. The pandemic due to COVID-19 has caused great impact on people’s approach to have proper lifestyle. People these days are found inactive, unhappy and less energetic, because of their busy routine and continual ignorance of overall health. By keeping a track of their mental and physical health, one could achieve better response and hence expected lifestyle. Our solution is to detect, analyze and deliver a solution to treat depression and assist people with fulfilling their daily energy requirement for being more active and enthusiastic. Our solution is a Soft-Ui Web Application that gives smooth UI/UX experience to users showcasing fluctuations in energy and playing games to get cognitive features’ result. The hardware is a wearable wrist band made with NodeMCU embedded with accelerometer and heart rate sensors. An analytical report is generated and updated in real time and user could download as per their convenience.
Trie Maya Kadarina, Rinto Priambodo
Journal of Electronics, Electromedical Engineering, and Medical Informatics, Volume 3, pp 64-71; https://doi.org/10.35882/jeeemi.v3i2.1

Abstract:
Internet of Things (IoT) applications can be used in healthcare services to monitor patients remotely. One implementation is that it is used to monitor COVID-19 patients. During the COVID-19 pandemic, people who are infected without symptoms must self-isolate so that the virus does not spread. Measurement of blood oxygen levels or SpO2 is one of the measurements that must be carried out in routine examination procedures for self-isolating patients for early detection of silent hypoxemia in COVID-19 patients. Previous research has developed an IoT-based health monitoring system with a Wireless Body Sensor Network (WBSN) and a gateway that can be used for data acquisition and transmission. The system uses a home pulse oximeter to measure SpO2 and heart rate and an Android application that functions as an IoT gateway to collect data from sensors and add location information before sending data to the server. The WBSN has been successfully integrated with two types of open source IoT platforms, namely ThingsBoard and Elasticsearch Logstash Kibana (ELK). However, it is necessary to carry out further studies on analytical and experimental performance tests of the two systems. Therefore, the purpose of this study is to develop a performance evaluation of the IoT-based SpO2 monitoring systems using the Thingsboard and ELK as IoT platforms. To evaluate the performace we ran the monitoring system on both platforms using pulse oximeter and Android device as IoT gateway with HTTP and MQTT as transport protocol for sending the data to the server. From this study we found that average time of message delivery in ELK compared to ThingsBoard using the same protocols was higher but stable.
, Yashwant K. Malaiya
Published: 19 July 2021
Discover Internet of Things, Volume 1, pp 1-15; https://doi.org/10.1007/s43926-021-00016-5

Abstract:
The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.
Kedar Nath Sahu, Ravindharan Ethiraj, Paramananda Jena
IoT Applications, Security Threats, and Countermeasures pp 177-193; https://doi.org/10.1201/9781003124252-10

Abstract:
One of the greatest achievements of the present scenario is the development of smartphones that connect to the Internet. A natural outcome of this would be to connect electronic devices to the Internet and look for possible benefits. Internet of Things (IoT) essentially comprises several objects interconnected through radio-frequency (RF) sensors and their monitoring by means of the Internet. It is about merging the linked devices with automated systems to gather data for analysis and to take actions. IoT has unfolded immense possibilities in the areas of health care and medicine. An ordinary medical device connected to the Internet can give invaluable additional data, which can lead to a far greater insight into the symptoms. If the same data is available to a health expert, then it would enable the patient to get superior treatment remotely. A new paradigm shift is happening with the advancement of nanotechnology. The Internet of Nano Things (IoNT) is not far off. Nanomedicine, along with IoNT, would change the very basis of investigating, curing, and preventing diseases. Forthcoming health care presumes to bank on e-health systems braced with the hypothesis of IoNT in order to evolve more customized, well-timed, and convenient as well as cost-effective investigation, medication, and tracking of health. The major concerns of the day are whether IoT can help control pandemics like COVID-19. This chapter aims to discuss the applications of IoT, challenges in overcoming health-care issues, and highlights the steps undertaken to uplift health care in India.
Published: 12 July 2021
by MDPI
Big Data and Cognitive Computing, Volume 5; https://doi.org/10.3390/bdcc5030030

Abstract:
The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.
Published: 8 July 2021
by MDPI
Sustainability, Volume 13; https://doi.org/10.3390/su13147647

Abstract:
Non-residential buildings contribute to around 20% of the total energy consumed in Europe. This consumption continues to increase globally. Smart building proposals (focused on Nearly Zero Energy Building (NZEB), air quality monitoring, energy saving with thermal comfort, etc.) were already necessary before 2020, and the pandemic has made this research and development area more essential. Furthermore, the need to meet the Sustainable Development Goals (SDG) and obtain technological solutions based on the Internet of Things (IoT) requires holistic contributions through real installations that serve as spaces for measuring, testing, study and research. This article proposes a “measure–analyse–decide and act” methodology to quantify the Smart Readiness Indicator (SRI) for university buildings as a reference environment for energy efficiency and COVID-19 prevention models. Two conceptual spaces (physical and digital) within two dimensions (users and infrastructures) are designated over an IoT three-level model (information acquisition, interoperable communication, and data-driven decision). An IoT ecosystem (sensoriZAR) was implemented as a proof-of-concept of a smart campus at the University of Zaragoza, Spain. Focused on CO2 and energy consumption monitoring, the results showed effectiveness through real installations, demonstrating the IoT potential as SDG-enabling technologies. These contributions allow not only experimental lab tests (from the authors’ expertise in several specialties of Industrial, Mechanical, Design, Thermal, Electrical, Electronic, Computer and Telecommunication Engineering) but also a reference model for direct application in academic works, research projects and institutional initiatives, extendable to professional environments, buildings and cities.
Sanjoy Mondal, Indrakshi Ghosh, Sayak Ghosh, Ayushi Gupta, Dipankar Basu
Published: 8 July 2021
SSRN Electronic Journal; https://doi.org/10.2139/ssrn.3882424

Abstract:
The revolution of Internet of Things (IoT) technologies has changed the healthcare system by assimilating the various technological, social and economical aspects. It emits the healthcare system from conventional to the more customized system. In the current global COVID-19 pandemic circumstance IoT play a crucial function to combat corona virus infections. The early diagnose of COVID-19 is very necessary in order to prevent the disease from spreading globally. In this paper we survey the significant role of IoT-based technologies along-with some associative advanced technologies to early detect the COVID-19 infections. We review the sate-of-the art architecture and applications to combat COVID-19 infections and propose an IoT-based framework which helps to early detect and monitoring the COVID-19 infections.
Jozwiak Lech, Stojanovic Radovan
Published: 7 July 2021
by Zenodo
Abstract:
Message from the editors, This Summer School on Cyber-Physical Systems and Internet of Things (SS-CPS&IoT’2021) is continuation of very successful 1st School from 2019. Unfortunately, last year, 2020, we were not able to organize the School because of Covid-19 pandemic. This year we adapted to the situation and managed the event on two tracks, remotely and on site. SS-CPS&IoT’2021 aims at serving the following main purposes: -advanced training of industrial and academic researchers, developers, engineers and decision-makers; academic teachers, Ph.D. and M.Sc. students; entrepreneurs, investors, research funding agents, and policy makers; and other participants who want to learn about CPS and IoT engineering; -dissemination, exchange and discussion of advanced knowledge and project results from numerous European R&D projects in CPS and IoT; -promotion and facilitation of international contacts and collaboration among people working or interested in the CPS and IoT area. The School is open to everybody, but previous knowledge or equivalent practical experience at least at the Bachelor level in engineering (e.g. system, computer, electronic, electrical, automotive, aviation, mechanical, or industrial engineering), computer science, informatics, applied physics or similar is recommended. Industry participation is encouraged. SSCPS&IoT’2021 is not only to follow courses and learn new knowledge on Embedded Systems, CPS and IoT from top professionals, but to meet people, interact and discuss with outstanding researchers, developers, academic lecturers, advanced students, and other participants, collaborate or start collaborations, and meet many talented people who may become employees of your companies as well. Distinguishing features of this advanced traditional Summer School are that its lectures, demonstrations, and practical hands-on sessions are given by top European and Worldwide specialists in particular CPS and IoT fields from industry and academia, delivering very fresh advanced knowledge. They are based on results from numerous currently running or recently finished European R&D projects in CPS and IoT, what gives an excellent opportunity to get acquainted with issues and challenges of CPS and IoT development; actual industrial problems, designs and case studies; and new concepts, advanced knowledge and modern design methods and tools created in the European R&D projects. This year, we had the honor to invite guest lecturer outside Europe, from Huawei, multinational company, leading global provider of information and communications technology (ICT) infrastructure and smart devices. Part of the students and lecturers came from the H2020 project SMART4ALL, “Self-sustained customized cyber physical system experiments for capacity building among European stakeholders”, so it can be said that it was a Joint School of our community with this significant project. SS-CPS&IoT’2021 is collocated with CPSIoT’2021, 9th International Conference on CyberPhysical Systems and Internet-of-Things and 10th Mediterranean Conference on Embedded Computing. The Summer School participants were encouraged to submit their papers to CPSIoT’2021 and MECO’2021, and thus gain additional experience of presenting work in one of the TOP conference in computing. The CPS&IoT’2021 Summer School Program is composed of four days of lectures, demonstrations, practical hands-on sessions, and discussions, as well as free participation in MECO’2021 and CPSIoT’2021 sessions. The topics of the lectures, demonstrations, and practical hands-on sessions cover major CPS applications (focusing on modern mobile applications that require high-performance or low energy consumption, as well as, high reliability, security and safety), computing technology for modern CPS, CPS architectures, development problems and solutions, as well as, design methodologies and design tools for all CPS design phases. In line with the technological challenges caused by the Covid-19 pandemic, part of the lecture was focused on fighting this disaster by using CPSs. There were also lectures from precision agriculture, in fact, Smart Anything Everywhere. Detailed list of the SS-CPS&IoT’2021 presentations including the names of their authors and presenters is provided in the Schedule of the School. Venue of SS-CPS&IoT’2021 was Hotel Budva*****, Budva, Montenegro. Budva is a 3500 years old town located at the Adriatic Sea coast of Montenegro. It is a popular touristic destination, with its charming Old Town, beautiful natural environment, 35 clean sandy beaches, and proximity to many famous touristic attractions as Kotor, Boka Kotorska, Sveti Stefan, Dubrovnik, and several national parks. It is an excellent place to have a summer school in a relaxed and friendly atmosphere. What were the brief data about this year Summer School? We had 70 lecturers and students, coming from over 20 countries around the world. We worked for four days in a 32-hour capacity, that is equivalent to an academic workload of 3 ECTS credits. The Chairmen of the SS-CPS&IoT’2021 express their thanks to all authors and presenters, as well as, to all other people who contributed to the success of the Summer School. We are especially proud on 2nd generation of students who successfully finished School and showed an enviable level of knowledge and interest. We are very grateful to Professor Budimur Lutovac, Publication Chair of CPSIoT’2021 and MECO’2021 helping us to compose these Proceedings, which represents only part of the results carried out by SS-CPSIoT’2021. The Proceedings is given here in form of open access document. We hope to see you again next year, mostly on the spot, in good health and mood. Yours, Lech Jóźwiak Eindhoven University of Technology, The Netherlands Radovan Stojanović University of Montenegro and MECOnet, Montenegro Contributors: Ioannis...
Jozwiak Lech, Stojanovic Radovan
Published: 7 July 2021
by Zenodo
Abstract:
Message from the editors, This Summer School on Cyber-Physical Systems and Internet of Things (SS-CPS&IoT’2021) is continuation of very successful 1st School from 2019. Unfortunately, last year, 2020, we were not able to organize the School because of Covid-19 pandemic. This year we adapted to the situation and managed the event on two tracks, remotely and on site. SS-CPS&IoT’2021 aims at serving the following main purposes: -advanced training of industrial and academic researchers, developers, engineers and decision-makers; academic teachers, Ph.D. and M.Sc. students; entrepreneurs, investors, research funding agents, and policy makers; and other participants who want to learn about CPS and IoT engineering; -dissemination, exchange and discussion of advanced knowledge and project results from numerous European R&D projects in CPS and IoT; -promotion and facilitation of international contacts and collaboration among people working or interested in the CPS and IoT area. The School is open to everybody, but previous knowledge or equivalent practical experience at least at the Bachelor level in engineering (e.g. system, computer, electronic, electrical, automotive, aviation, mechanical, or industrial engineering), computer science, informatics, applied physics or similar is recommended. Industry participation is encouraged. SSCPS&IoT’2021 is not only to follow courses and learn new knowledge on Embedded Systems, CPS and IoT from top professionals, but to meet people, interact and discuss with outstanding researchers, developers, academic lecturers, advanced students, and other participants, collaborate or start collaborations, and meet many talented people who may become employees of your companies as well. Distinguishing features of this advanced traditional Summer School are that its lectures, demonstrations, and practical hands-on sessions are given by top European and Worldwide specialists in particular CPS and IoT fields from industry and academia, delivering very fresh advanced knowledge. They are based on results from numerous currently running or recently finished European R&D projects in CPS and IoT, what gives an excellent opportunity to get acquainted with issues and challenges of CPS and IoT development; actual industrial problems, designs and case studies; and new concepts, advanced knowledge and modern design methods and tools created in the European R&D projects. This year, we had the honor to invite guest lecturer outside Europe, from Huawei, multinational company, leading global provider of information and communications technology (ICT) infrastructure and smart devices. Part of the students and lecturers came from the H2020 project SMART4ALL, “Self-sustained customized cyber physical system experiments for capacity building among European stakeholders”, so it can be said that it was a Joint School of our community with this significant project. SS-CPS&IoT’2021 is collocated with CPSIoT’2021, 9th International Conference on CyberPhysical Systems and Internet-of-Things and 10th Mediterranean Conference on Embedded Computing. The Summer School participants were encouraged to submit their papers to CPSIoT’2021 and MECO’2021, and thus gain additional experience of presenting work in one of the TOP conference in computing. The CPS&IoT’2021 Summer School Program is composed of four days of lectures, demonstrations, practical hands-on sessions, and discussions, as well as free participation in MECO’2021 and CPSIoT’2021 sessions. The topics of the lectures, demonstrations, and practical hands-on sessions cover major CPS applications (focusing on modern mobile applications that require high-performance or low energy consumption, as well as, high reliability, security and safety), computing technology for modern CPS, CPS architectures, development problems and solutions, as well as, design methodologies and design tools for all CPS design phases. In line with the technological challenges caused by the Covid-19 pandemic, part of the lecture was focused on fighting this disaster by using CPSs. There were also lectures from precision agriculture, in fact, Smart Anything Everywhere. Detailed list of the SS-CPS&IoT’2021 presentations including the names of their authors and presenters is provided in the Schedule of the School. Venue of SS-CPS&IoT’2021 was Hotel Budva*****, Budva, Montenegro. Budva is a 3500 years old town located at the Adriatic Sea coast of Montenegro. It is a popular touristic destination, with its charming Old Town, beautiful natural environment, 35 clean sandy beaches, and proximity to many famous touristic attractions as Kotor, Boka Kotorska, Sveti Stefan, Dubrovnik, and several national parks. It is an excellent place to have a summer school in a relaxed and friendly atmosphere. What were the brief data about this year Summer School? We had 70 lecturers and students, coming from over 20 countries around the world. We worked for four days in a 32-hour capacity, that is equivalent to an academic workload of 3 ECTS credits. The Chairmen of the SS-CPS&IoT’2021 express their thanks to all authors and presenters, as well as, to all other people who contributed to the success of the Summer School. We are especially proud on 2nd generation of students who successfully finished School and showed an enviable level of knowledge and interest. We are very grateful to Professor Budimur Lutovac, Publication Chair of CPSIoT’2021 and MECO’2021 helping us to compose these Proceedings, which represents only part of the results carried out by SS-CPSIoT’2021. The Proceedings is given here in form of open access document. We hope to see you again next year, mostly on the spot, in good health and mood. Yours, Lech Jóźwiak Eindhoven University of Technology, The Netherlands Radovan Stojanović University of Montenegro and MECOnet, Montenegro Contributors: Ioannis...
Yu-Ting Shen, Liang Chen, , Hui-Xiong Xu
Published: 6 July 2021
Frontiers in Medicine, Volume 8; https://doi.org/10.3389/fmed.2021.646506

Abstract:
In the year 2020, the coronavirus disease 2019 (COVID-19) crisis intersected with the development and maturation of several digital technologies including the internet of things (IoT) with next-generation 5G networks, artificial intelligence (AI) that uses deep learning, big data analytics, and blockchain and robotic technology, which has resulted in an unprecedented opportunity for the progress of telemedicine. Digital technology-based telemedicine platform has currently been established in many countries, incorporated into clinical workflow with four modes, including “many to one” mode, “one to many” mode, “consultation” mode, and “practical operation” mode, and has shown to be feasible, effective, and efficient in sharing epidemiological data, enabling direct interactions among healthcare providers or patients across distance, minimizing the risk of disease infection, improving the quality of patient care, and preserving healthcare resources. In this state-of-the-art review, we gain insight into the potential benefits of demonstrating telemedicine in the context of a huge health crisis by summarizing the literature related to the use of digital technologies in telemedicine applications. We also outline several new strategies for supporting the use of telemedicine at scale.
Published: 6 July 2021
by MDPI
Abstract:
Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.
, Nilisha Itankar, Shilpa Malge
Journal of Physics: Conference Series, Volume 1964; https://doi.org/10.1088/1742-6596/1964/4/042001

Abstract:
Objective: Currently, the entire world is dealing with a lethal crisis caused by COVID-19. SARS-CoV-2 is the root cause of the new disease Coronavirus. Novel Coronavirus has gripped more than 215 countries and territories worldwide. This study provides the bibliographic analysis of data on the epidemiological research conducted on COVID-19 and technology-based tools and applications extensively used to study and interpret the Coronavirus to tackle the pandemic in every way possible. Method: We investigated the data from the Scopus search engine, Google Scholar, and World Health Organization (WHO) using associated terms like Coronavirus, COVID-19, Artificial Intelligence (A.I.), Machine Learning (ML), Big data, Internet of Things (IoT). The data stretches from December 2019 up to 9 May 2020.Result: A.I., an ML-supported platform, is used by many agencies worldwide. Out of 217 publications from 57 countries, China and United States contributed maximum technology-based research articles in correlation with COVID 19. Conclusion: The world is in the middle of a pandemic due to COVID-19. Globally human health has been impacted. This paper attempts to understand the intellectual pattern of COVID-19 and 4.0 industry tools in research using the Scopus database and conducting the bibliometric analysis. This bibliometric analysis would also facilitate future researchers' pathway to identify research carried out in COVID-19, focusing on A.I., ML, and advanced digital technologies.It would develop the existing knowledge potential and help future researchers collaborate and facilitate interdisciplinary research to tackle the pandemic better.
Pradeep Kumar Dabla, , Bernard Gouget, Sergio Bernardini, Evgenija Homsak
Published: 29 June 2021
Abstract:
SARS-CoV-2, the new coronavirus causing COVID-19, is one of the most contagious disease of past decades. COVID-19 is different only in that everyone is encountering it for the first time during this pandemic. The world has gone from complete ignorance to a blitz of details in a matter of months. The foremost challenge that the scientific community faces is to understand the growth and transmission capability of the virus. As the world grapples with the global pandemic, people are spending more time than ever before living and working in the digital milieu, and the adoption of Artificial Intelligence (AI) is propelled to an unprecedented level especially as AI has already proven to play an important role in counteracting COVID-19. AI and Data Science are rapidly becoming important tools in clinical research, precision medicine, biomedical discovery and medical diagnostics. Machine learning (ML) and their subsets, such as deep learning, are also referred to as cognitive computing due to their foundational basis and relationship to cognition. To date, AI based techniques are helping epidemiologists in projecting the spread of virus, contact tracing, early detection, monitoring, social distancing, compiling data and training of healthcare workers. Beside AI, the use of telemedicine, mobile health or mHealth and the Internet of Things (IOT) is also emerging. These techniques have proven to be powerful tools in fighting against the pandemic because they provide strong support in pandemic prevention and control. The present study highlights applications and evaluations of these technologies, practices, and health delivery services as well as regulatory and ethical challenges regarding AI/ML-based medical products.
, Yashwant K. Malaiya
Published: 25 June 2021
Abstract:
The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This article illuminates the public view of the Internet of Things through a content-based analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a two-week period. Using supervised and unsupervised machine learning methods, we have identified interconnecting relationships between trending themes and the most mentioned industries. We have identified characteristics of language sentiment which can help to predict popularity within the realm of IoT conversation. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current Covid-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms.
, Jazuli S. Kazaure, Onyebuchi Amaonwu, Umar Abdu Adamu, Ibrahim Muhammad Hassan, Aminu Abdulahi Kazaure, Chibueze N. Ubochi
Algorithms for Intelligent Systems pp 27-53; https://doi.org/10.1007/978-981-16-1574-0_2

Abstract:
The global world had recently observed a set of computational paradigm shift characterised by Internet of Things (IoTs), Internet of Health Things (IoHTs), Innovative Internet of 5G Medical Robotic Things (IIo-5GMRTs), Artificial Intelligence and 5G network technology injection in the ongoing COVID-19 global pandemic which had assisted the healthcare service sector to deliver on the key mandates and countries to perform better in the ongoing COVID-19 global pandemic. The ongoing infectious COVID-19 pandemic had exerted the healthcare sector and compelled the healthcare professionals all over the globe to face the challenging moment of handling the infected persons with infinitesimal social and bodily contacts. The apprehension for the Healthcare sector workers to presumably stand at advantageous gain from technological development in the ongoing COVID-19 had motivated the development of several healthcare technologies to enable hospitals perform better through innovative approaches offered by Internet of Things, 5G Medical Robotics for hospital disinfection, Internet of Things Healthcare monitoring and Artificial Intelligence Drones for logistics management. The scientists believed that COVID-19 will ostensibly drive scientific innovations in the 5G medical robotics which will consolidate the industry 4.0 extreme healthcare automation. The current research proposed a cost effective healthcare technologies for hospital disinfection routine operations and remote access monitoring using Internet of Things Medical Robotics to lower the risk of hospital acquired infection in the ongoing COVID-19 pandemic.
Published: 21 June 2021
Abstract:
The ongoing COVID-19 (novel coronavirus disease 2019) pandemic has triggered a global emergency, resulting in significant casualties and a negative effect on socioeconomic and healthcare systems around the world. Hence, automatic and fast screening of COVID-19 infections has become an urgent need of this pandemic. Real-time reverse transcription polymerase chain reaction (RT-PCR), a commonly used primary clinical method, is expensive and time-consuming for skilled health professionals. With the aid of various AI functionalities and advanced technologies, chest CT scans may thus be a viable alternative for quick and automatic screening of COVID-19. At the moment, significant advances in 5G cellular and internet of things (IoT) technology are finding use in various applications in the healthcare sector. This study presents an IoT-enabled deep learning-based stacking model to analyze chest CT scans for effective diagnosis of COVID-19 encounters. At first, patient data will be obtained using IoT devices and sent to a cloud server during the data procurement stage. Then we use different fine-tuned CNN sub-models, which are stacked together using a meta-learner to detect COVID-19 infection from input CT scans. The proposed model is evaluated using an open access dataset containing both COVID-19 infected and non-COVID CT images. Evaluation results show the efficacy of the proposed stacked model containing fine-tuned CNNs and a meta-learner in detecting coronavirus infections using CT scans.
, Reek Roy, Sumanta Chakraborty
Smart Healthcare System Design pp 87-114; https://doi.org/10.1002/9781119792253.ch5

Abstract:
The advent of the Internet of Things (IoT) and large data analysis in the healthcare field has led to data obtained from areas where it was either performed manually or not at all before. The ability to detect an infectious disease and preventing it from spreading requires knowledge and monitoring in real-time. The IoT has been used to capture sensory data in real-time; by monitoring individuals, health systems, ecosystems, even in some of the globe's most remote areas. Based on a study, could recommend precautionary measures using data from the IoT network; further assess whether the suggested control measures are being implemented correctly. In scenarios like COVID 19 pandemic, it is important to get monitoring on the detection of the cause of the pandemic and on the spreading of the disease, especially in the developing countries. Our proposed framework includes a network model that uses wireless body sensors, wearable devices, and cloud computing to handle patient data in the form of text or pictures, or cloud voice. A mobile phone application and a website is built to keep track of the real-time data. Our system is robust and more efficient compared to other existing systems.
, Sumanta Chakraborty, Sourav Paul, Rajdeep Ghosh, Dipanwita Chakraborty Bhattacharya
Smart Healthcare System Design pp 115-142; https://doi.org/10.1002/9781119792253.ch6

Abstract:
The term, “Healthcare 4.0” has been introduced from the Industry 4.0. The virtualization and automation in healthcare is included in Healthcare 4.0. The Industry 4.0 can provide the automation and data communication by using Cloud Computing, Internet of Things (IoT), Big Data, 4G and 5G technologies, cryptography, Content-Based Image Retrieval (CBIR), Augmented Reality (AR), etc. We can think of the Healthcare 4.0 as the application of the Industry 4.0. The health related processes include home care of patients, remote health diagnosis, personalized home treatments, etc. Healthcare 4.0 consists of computers, communicating interfaces, bio-sensors, bio-actuators, etc. In Healthcare 4.0 the surgical operations on the patients can be monitored remotely. The mobile gadgets use the bio-sensors. Our proposed system uses machine learning techniques on the collected data by the sensors for analyses. Our system collects the patients' medical histories for analyses. In the current COVID-19 pandemic situation getting a bed for the treatment is very much difficult, especially in the developing and highly populated countries. Our proposed healthcare system 4.0 is built for transfer of the treatments from the hospitals to the patients' home with a high accuracy rate of detection of the diseases and tests.
P. Ramchandar Rao, , Sridevi Chitti, Shyamsunder Merugu, J. Tarun Kumar
Inventive Computation and Information Technologies pp 635-646; https://doi.org/10.1007/978-981-16-0666-3_52

Abstract:
In the pandemic situations, the physicians will never have the direct contact with the patients. Hence, a remote health management device is developed in this paper using ESP32 and ThingSpeak cloud application. If a person is suspected of having COVID-19, he has to contact primary health centre of his nearest place where the developed device is already been located. The person has to place the above device in contact with his body to measure temperature, heartbeat, oxygen level and cough. The same device will have the feature to measure room temperature and humidity. Various sensors are used to detect the above parameters. The sensed values will be taken up by ESP32. The data from ESP32 is stored in cloud through ThingSpeak application. The physician can take over the details from cloud and diagnose whether the person is suffering from COVID-19 or not. In this paper, the diagnostic information followed by the physician is the body temperature which is >37.8 °C, heartbeat is >100, oxygen level is 116 db. If these measures are satisfied then the buzzer sounds which alerts the consulting physician.
Published: 11 June 2021
SN Computer Science, Volume 2, pp 1-24; https://doi.org/10.1007/s42979-021-00719-0

Abstract:
Intelligent systems are enhancing city environments and improving their overall performance in all possible aspects. Innovations in the field of information and communication technologies (ICT) and the proliferation of big data, internet-of-things (IoT), and cloud (BIC) infrastructures revolutionize the existing agile city ecosystems while effectively addressing customers and citizens needs. In this paper, we address the technology-driven applications that are capable of influencing the existing city infrastructures during their transformation towards smart cities with contactless technologies. We present applications, design principles, technology standards, and cost-effective techniques that leverage BIC for contactless applications and discuss user interfaces deployed in smart city environments. We further discuss state-of-the-art sensing methods and smart applications that support cities with smart contactless features. Finally, a case study is reported on how BIC can assist in efficiently handling and managing emergency situations such as the COVID-19 pandemic.
Published: 10 June 2021
Abstract:
Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.
Mohamad Kassab, Valdemar Vicente Graciano Neto
Published: 10 June 2021
Procedia Computer Science, Volume 185, pp 37-44; https://doi.org/10.1016/j.procs.2021.05.005

Abstract:
Since the genesis case was confirmed in Wuhan, China in late 2019, the Novel Coronavirus Disease 2019 (COVID-19) has been spreading all over the world at an accelerating rate. Almost immediately, IoT technologies were deployed in various surveillance scenarios as part of an effort to combat the pandemic. Among the emerging solutions, contact tracing mobile applications have been playing an effective role to help stem the spread of the virus by tracking individuals and those they come into exposure with. This paper aims at providing a panoramic view of the digital tracking technologies that have been utilized so far in response to the pandemic. We particularly provide a detailed analysis of 47 contact tracing mobile applications that emerged in response to COVID-19. We accompany our analysis with a discussion on the privacy and the technology / social constraints that may challenge the deployment of these applications as digital surveillance platforms.
Published: 9 June 2021
Pervasive and Mobile Computing, Volume 75; https://doi.org/10.1016/j.pmcj.2021.101426

Abstract:
Internet of Things(IoT) facilitates key technologies that rely on sensing, communication and processing in daily routines. As an IoT-enabled paradigm, mobile crowdsensing (MCS) can offer more possibilities for data collection to support various IoT applications and services. As an extension, MCS can be used for data gathering amid COVID-19 pandemic crisis. Bridging Artificial Intelligence and IoT can achieve not only maintaining low infection rates of COVID-19 but can also facilitate an effective rapid testing strategy to reduce community spread. In this research, an intelligent strategy to deploy autonomous vehicle-based mobile testing facilities is proposed to enable early detection of infected cases based upon MCS data acquired through smart devices via wireless communications such as Wifi, LTE and 5G. To this end, a Self Organizing Feature Map is designed to manage MCS-based data for planning of the autonomous mobile assessment centers. Pre-identified zero-day locations and worst-case scenario are considered to determine the best combination for MCS participation rate and budget limitations. Numerical results demonstrate that once 30% of MCS participants are recruited, it becomes possible to cover the pre-identified zero-day locations and enable detection of infected cases under the worst case scenario to determine the AV routes more efficiently than other options for a certain number of neurons in SOFM. The worst-case scenario demonstrates that 30% participant rate ensures detection of infected cases in 27 days for 81 stops even infected cases are outside of the autonomous vehicle testing coverage.
, Ekasari Nugraheni, Devi Munandar, Andria Arisal, Wiwin Suwarningsih
Published: 8 June 2021
Abstract:
COVID-19 has induced many problems in various sectors of life for humanity around the world. After one year of pandemic, many studies have been carried out in exposing various technology innovations and applications to combat the coronavirus that has killed more people than most. The pandemic has accelerated the use of Big Data technology to mitigate the threats of COVID-19. This survey aims to explore the Big Data research for COVID-19. We collected and analyzed the relevant academic articles to identify how Big Data technology can cover the challenges faced in overcoming the pandemic. In determining the research areas addressed by the past studies, we highlight the technology contributions to five major areas of healthcare, social life, government policy, business and management, and the environment. We discuss how analytical techniques of machine learning, deep learning, statistics, and mathematics can solve pandemic issues. The Big Data research for COVID-19 used a wide variety of data sources available publicly or privately. At the end of the discussion, we present the data source used in the past studies encompassing government official data, institutional service data, IoT generated data, online media data, and open data. We hope that this survey will clarify the role of Big Data technology in enhancing the research for COVID-19.
Mobile Networks and Applications pp 1-12; https://doi.org/10.1007/s11036-021-01789-3

Abstract:
In the scenarios of specific conditions and crises such as the coronavirus pandemic, the availability of e-learning ecosystem elements is further highlighted. The growing importance for securing such an ecosystem can be seen from DDoS (Distributed Denial of Service) attacks on e-learning components of the Croatian e-learning system. The negative impact of the conducted attack is visible in numerous users who were prevented from participating in and implementing the planned teaching process. Network anomalies such as conducted DDoS attacks were identified as one of the crucial threats to the e-learning systems. In this paper, an overview of the network anomaly phenomenon was given and botnets’ role in generating DDoS attacks, especially IoT device impact. The paper analyzes the impact of the COVID-19 pandemic on the e-learning systems in Croatia. Based on the conclusions, a research methodology has been proposed to develop a cyber-threat detection model that considers the specifics of the application of e-learning systems in crisis, distinguishing flash crowd events from anomalies in the communication network. The proposed methodology includes establishing a theoretical basis on DDoS and flash crowd event traffic, defining a laboratory testbed setup for data acquisition, development of DDoS detection model, and testing the applicability of the developed model on the case study. The implementation of the proposed methodology can improve the quality of the teaching process through timely DDoS detection and it gives other socio-economic contributions such as developing a specific research domain, publicly available dataset of network traffic, and raising the cyber-security of the e-learning systems.
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