FPGA Based Light Weight Encryption of Medical Data for IoMT Devices using ASCON Cipher

Internet of Things applications with various sensors in public network are vulnerable to cyber physical attacks. The technology of IoT in smart health monitoring systems popularly known as Internet of Medical Things (IoMT) devices. The rapid growth of remote telemedicine has witnessed in the post COVID era. Data collected over IoMT devices is sensitive and needs security, hence provided by enhancing a light weight encryption module on IoMT device. An authenticated Encryption with Associated Data is employed on the IoMT device to enhance the security to the medical wellness of patient. This paper presents FPGA-based implementation of ASCON-128, a light weight cipher for data encryption. A LUT6 based substitution box (SBOX) is implemented on FPGA as part of cipher permutation block. The proposed architecture takes 1330 number of LUTs, which is 35% less compared to the best existing design. Moreover, the proposed ASCON architecture has improved the throughput by 45% compared to the best existing design. This paper presents the results pertaining to encryption and decryption of medical data as well as normal images. © 2022 IEEE.

An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection

Advancement of smart medical sensors, devices, cloud computing, and health care technologies is getting remarkable attention from academia and the health care industry. As, Internet of things (IoT) has been recognized as one of the promising research topics in the domain of health care, particularly in medical image processing. Researchers utilized various machine and deep learning techniques along with artificial intelligence for analyzing medical images. These developed techniques are used to detect diseases that might assist medical experts in diagnosing diseases at early stages and providing accurate, consistent, effective, and speedy results, and decrease the mortality rate. Nowadays, Coronavirus (COVID-19) has been growing as one of the most rigorous and severe infections and spreading globally. Consequently, an intelligent automated system is required as an active diagnostic choice that might be used to prevent the spread of COVID-19. Thus, this work presented an IoT-enabled smart health care system for the automatic screening and classification of contagious diseases (Pneumonia, COVID-19) in Chest X-ray images. The developed system is based on two different deep learning architectures used with a multi-layers feature fusion and feature selection approach to classify X-ray images of infectious diseases. This work comprises the following steps: to enhance the diversity of the data set, data augmentation is performed, while for feature extraction, deep learning architectures, i.e., VGG-19 and Inception-V3, are used along with transfer learning. For the fusion of extracted features obtained from deep learning architectures, a parallel maximum covariance, and for feature selection, the multi-logistic regression controlled entropy variance approach is applied. For experimentation, a data set is customized containing Chest X-ray images using various publicly available resources. The system provides an overall classification accuracy of 97%. [ FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Study on Functional Satisfaction with Smart Health Care Air Purification Product in the Post-epidemic Era

In the context of the COVID-19, respiratory diseases have become the focus of social attention, and the elderly, as a susceptible population, is more significantly affected by the epidemic. In order to fully protect the respiratory system of the elderly and enhance their satisfaction with the function of smart products, this study proposes a design method for smart health care product based on the cognitive behavior of elderly users. Firstly, the user demand gap is explored and determined by using the A-Kano model;secondly, a functional model is created based on the FAST functional theory. After converting the user demand into function, and then the TRIZ theory is applied to choose to use 40 invention principles and 39 general engineering parameters to analyze the problem and get conflict domain solutions, so as to filter out the most ideal solution and innovate its function;Finally, by the design and practice of the smart health care air purification product, its purification range, monitoring data and wearing method will be effectively optimized, and the feasibility of the design solution will be verified by the user interaction satisfaction questionnaire. The study provides new ideas for the design of smart health care products and the solutions of contradictory problems, which would also be a theoretical guidance for relevant designer and researchers. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Plastic wastes in the time of COVID-19: Their environmental hazards and implications for sustainable energy resilience and circular bio-economies.

The global scope of pollution from plastic waste is a well-known phenomenon associated with trade, mass consumption, and disposal of plastic products (e.g., personal protective equipment (PPE), viral test kits, and vacuum-packaged food). Recently, the scale of the problem has been exacerbated by increases in indoor livelihood activities during lockdowns imposed in response to the coronavirus disease 2019 (COVID-19) pandemic. The present study describes the effects of increased plastic waste on environmental footprint and human health. Further, the technological/regulatory options and life cycle assessment (LCA) approach for sustainable plastic waste management are critically dealt in terms of their implications on energy resilience and circular economy. The abrupt increase in health-care waste during pandemic has been worsening environmental quality to undermine the sustainability in general. In addition, weathered plastic particles from PPE along with microplastics (MPs) and nanoplastics (NPs) can all adsorb chemical and microbial contaminants to pose a risk to ecosystems, biota, occupational safety, and human health. PPE-derived plastic pollution during the pandemic also jeopardizes sustainable development goals, energy resilience, and climate control measures. However, it is revealed that the pandemic can be regarded as an opportunity for explicit LCA to better address the problems associated with environmental footprints of plastic waste and to focus on sustainable management technologies such as circular bio-economies, biorefineries, and thermal gasification. Future researches in the energy-efficient clean technologies and circular bio-economies (or biorefineries) in concert with a "nexus" framework are expected to help reduce plastic waste into desirable directions.

Internet of Things-Based e-Health Care: Key Challenges and Recommended Solutions for Future

Internet of Things (IoT) has changed the way of living today. Today, Internet connected things (ICT) are increasing at a rapid rate and connecting with devices to reduce load from human being. Irrespective of the sector, the IoT devices are everywhere taking care of everything from the agriculture sector to the sector of manufacturing. But, due to the global COVID 19 pandemic, the sector of health care demands the major use of IoT today. Due to the prevailing pandemic, healthcare professionals also choose to treat the patients virtually rather than treating them physically. IoT plays a major role here. But, most of the application providers or service providers or any other system involving IoT devices for generating and storing data may become a way of leak of information or stolen by a third party for black mailing or financial gain thus leading to privacy and security leak of the user. This work includes all such views with various issues and recommended solutions for the same. Also, other security and privacy requirements and corresponding solutions are also included to provide future researchers a solid base and a clear depth in knowledge regarding the security and privacy issues and solutions required. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Role of Smart Dustbin in Creating a Smart Environment

Recent developments in Internet of Things (IoT) have significantly changed modern lifestyles through linkages of smart objects and smart applications that can be controlled anytime anywhere in the world. It is expected that as of 2021, more than 35 billion devices are connected with each other under the broad IoT umbrella. The autonomous nature of IoT brings an opportunity for virtual representation and unique identification of devices, applications, and services. Lower costs, lower levels of energy consumption, higher levels of outputs, smart and user-friendly precincts, and others are some factors behind rising popularity of IoT, which is also becoming more effective in providing higher levels of security and reducing errors and abuses. In this paper, we describe a model of smart dustbin designed and configured by the authors, and explain how the smart dustbin model can be applied efficiently to achieve smart environmental goals. This example of smart dustbin application can be adopted by cities and communities to maximize public health and hygiene objectives in smarter ways, and to curb negative impacts of public health crises situations such as the ongoing COVID-19 pandemic and other infectious diseases that may bring any type of pandemic in the future. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection

Advancement of smart medical sensors, devices, cloud computing, and health care technologies is getting remarkable attention from academia and the health care industry. As, Internet of things (IoT) has been recognized as one of the promising research topics in the domain of health care, particularly in medical image processing. Researchers utilized various machine and deep learning techniques along with artificial intelligence for analyzing medical images. These developed techniques are used to detect diseases that might assist medical experts in diagnosing diseases at early stages and providing accurate, consistent, effective, and speedy results, and decrease the mortality rate. Nowadays, Coronavirus (COVID-19) has been growing as one of the most rigorous and severe infections and spreading globally. Consequently, an intelligent automated system is required as an active diagnostic choice that might be used to prevent the spread of COVID-19. Thus, this work presented an IoT-enabled smart health care system for the automatic screening and classification of contagious diseases (Pneumonia, COVID-19) in Chest X-ray images. The developed system is based on two different deep learning architectures used with a multi-layers feature fusion and feature selection approach to classify X-ray images of infectious diseases. This work comprises the following steps: to enhance the diversity of the data set, data augmentation is performed, while for feature extraction, deep learning architectures, i.e., VGG-19 and Inception-V3, are used along with transfer learning. For the fusion of extracted features obtained from deep learning architectures, a parallel maximum covariance, and for feature selection, the multi-logistic regression controlled entropy variance approach is applied. For experimentation, a data set is customized containing Chest X-ray images using various publicly available resources. The system provides an overall classification accuracy of 97%. [ FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Applications of Blockchain in the Medical Field: Narrative Review.

BACKGROUND: As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analyzed blockchain applications from the perspectives of technology, business, or patient care, few studies have focused on actual use-case scenarios of blockchain in health care. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice. OBJECTIVE: This paper aims to provide a systematic review of the current and projected uses of blockchain technology in health care, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in health care is discussed. METHODS: We searched databases such as PubMed, EMBASE, Scopus, IEEE, and Springer using a combination of terms related to blockchain and health care. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarize the key medical scenarios using blockchain technology. RESULTS: We found a total of 1647 relevant studies, 60 of which were unique studies that were included in this review. These studies report a variety of uses for blockchain and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues, and regulatory policy. CONCLUSIONS: Blockchain technology can improve health care services in a decentralized, tamper-proof, transparent, and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term benefits. Not only can it be a mechanism to secure electronic health records, but blockchain also provides a powerful tool that can empower users to control their own health data, enabling a foolproof health data history and establishing medical responsibility.