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

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Big data is the next big thing in computing. As this data cannot be processed using traditional systems, it poses numerous challenges to the research community. Privacy is one of the important concerns with data, be it traditional data or big data. This paper gives an overview of big data, the challenges with big data and the privacy preserving data sharing and publishing scenario. We focus on the various privacy models that can be extended to big data domain. A description of each privacy model with its benefits and drawbacks is discussed in the review. This survey will contribute much to the benefit of researchers and industry players in uncovering the critical areas of big data privacy.

Privacy models for big data: a survey

Climate change and disease dynamics - A big data perspective

Alternative selection of vaccination strategy has become a challenging task for the public health, and it is considered as a complex decision making problem. Decision makers often use linguistic variables to rate the alternatives. The objective of this research is to use fuzzy logic based VIKOR method for evaluating H1N1 Influenza vaccination strategies. The experimental design of the proposed decision making model is illustrated with a case study in Vellore, Tamil Nadu, India. The alternative of a vaccination strategy considered in this study includes the combination of “people”, “spatial” and “temporal”.

Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making

Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics. Human-Computer Interaction With Big Data Analytics

Daphne Lopez

Papers

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Privacy models for big data: a survey

Climate change and disease dynamics - A big data perspective

Assessment of Vaccination Strategies Using Fuzzy Multi-criteria Decision Making

Human-Computer Interaction With Big Data Analytics