Background and aims COVID 19 (Coronavirus) pandemic has created surge demand for essential healthcare equipment, medicines along with the requirement for advance information technologies applications. Industry 4.0 is known as the fourth industrial revolution, which has the potential to fulfil customised requirement during COVID-19 crisis. This revolution has started with the applications of advance manufacturing and digital information technologies. Methods A detailed review of the literature is done on the technologies of Industry 4.0 and their applications in the COVID-19 pandemic, using appropriate search words on the databases of PubMed, SCOPUS, Google Scholar and Research Gate. Results We found several useful technologies of Industry 4.0 which help for proper control and management of COVID-19 pandemic and these have been discussed in this paper. The available technologies of Industry 4.0 could also help the detection and diagnosis of COVID-19 and other related problems and symptoms. Conclusions Industry 4.0 can fulfil the requirements of customised face masks, gloves, and collect information for healthcare systems for proper controlling and treating of COVID-19 patients. We have discussed ten major technologies of Industry 4.0 which help to solve the problems of this virus. It is useful to provide day to day update of an infected patient, area-wise, age-wise and state-wise with proper surveillance systems. We also believe that the proper implementation of these technologies would help to enhance education and communication regarding public health. These Industry 4.0 technologies could provide a lot of innovative ideas and solution for fighting local and global medical emergencies.

Industry 4.0 technologies and their applications in fighting COVID-19 pandemic.

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0

Enabling technologies for fog computing in healthcare IoT systems

A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system

HCI Challenges and Privacy Preservation in Big Data Security

With increased usage of IT solutions, a huge volume of data is generated from different sources like social networks, CRM, and healthcare applications, to name a few. The size of the data that is generated grows exponentially. As cloud computing provides an optimized, shared, and virtualized IT infrastructure, it is better to leverage the cloud services for storing and processing such Big Data. Securing the data is one of the major challenges in all the domains. Though security and privacy have been talked about for decades, there is still a growing need for high end methods for securing the rampant growth of data. The privacy of personal data, and to be more specific the health data, continues to be an important issue worldwide. Most of the health data in today’s IT world is being computerized. A patient’s health data may portray the different attributes such as his physical and mental health, its severity, financial status, and much more. Moreover, the medical data that are collected from the patients are being shared with other stakeholders of interest like doctors, insurance companies, pharmacies, researchers, and other health care providers. Individuals raise concern about the privacy of their health data in such a shared environment. M. Thilagavathi VIT University, India Daphne Lopez VIT University, India B. Senthil Murugan VIT University, India

Middleware for Preserving Privacy in Big Data

Grid Computing has emerged as an important new field focusing on resource sharing. One of the most challenging issues in Grid Computing is efficient scheduling of tasks. In this paper, we propose a new algorithm for fair scheduling, and we compare it to other scheduling schemes such as the First Come First Served and the Round Robin schemes for a computational grid. It aims at addressing the fairness issue by reducing the service time error..The algorithm assigns to each task enough computational power to complete it within its deadline. The resources that each user gets are proportional to the user’s weight or a share. The weight or share of a user may be defined as the user’s contribution to the infrastructure or the price he is willing to pay for services. Scheduling of tasks is based on an error called the Service time error which fairness among users. Fairness is defined as the proportional allocation of resources to tasks as per their demand. Simulated results and comparisons with the conventional scheduling schemes such as the FCFS and Round Robin are presented.

http://www.jatit.org/volumes/research-papers/Vol9No2/4Vol9No2.pdf

A dynamic error based fair scheduling algorithm for a computational grid

Deep learning has revolutionized the field of conversation modeling. A lot of the research has been toward making the conversational agent more human‐like. As a result, overall the model size increases. Bigger models require more data and are costly to build and maintain. Often, for some tasks, high‐quality responses are not necessary. In this paper, a model that consumes fewer resources and a way to augment conversation data without increasing the size of the vocabulary is proposed. The proposed model uses a modified version of the GRU instead of the LSTM to encode and decode sequences of text.

A scaled‐down neural conversational model for chatbots

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains. Architecture for Big Data Storage in Different Cloud Deployment Models

Daphne Lopez

Papers

HCI Challenges and Privacy Preservation in Big Data Security

Middleware for Preserving Privacy in Big Data

A dynamic error based fair scheduling algorithm for a computational grid

A scaled‐down neural conversational model for chatbots

Architecture for Big Data Storage in Different Cloud Deployment Models