An IoT-based framework for early identification and monitoring of COVID-19 cases

Background/objectives The Internet of Things (IoT) can create disruptive innovation in healthcare Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare For this, a brief study is required for research directions Methods Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology’s capabilities This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic Results Briefly studied the significant achievements of IoT with the help of a process chart Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them Conclusions In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases IoT’s sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic In the medical field, IoT’s focus is to help perform the treatment of different COVID-19 cases precisely It makes the surgeon job easier by minimising risks and increasing the overall performance By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital Medical students can now be better trained for disease detection and well guided for the future course of action IoT’s proper usage can help correctly resolve different medical challenges like speed, price, and complexity It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days

Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic

Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented literature on ML for reliability and safety applications, and it can be overwhelming to navigate and integrate into a coherent whole. In this work, we facilitate this task by providing a synthesis of, and a roadmap to this ever-expanding analytical landscape and highlighting its major landmarks and pathways. We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. We then look back and review the use of ML in reliability and safety applications. We examine several publications in each category/sub-category, and we include a short discussion on the use of Deep Learning to highlight its growing popularity and distinctive advantages. Finally, we look ahead and outline several promising future opportunities for leveraging ML in service of advancing reliability and safety considerations. Overall, we argue that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications. It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident prevention.

Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities

Nowadays, the transformations of metropolises into smart cities is a crucial factor in improving the living conditions of the inhabitants. The goal of the smart city concept is modern urban management using technical tools that offer state-of-the-art technologies, considering the applicable ecological standards while saving resources and achieving the expected results. The purpose of this article is to identify the areas of research analysed in the international literature in the field of smart cities. The bibliometric analysis was carried out to achieve the purpose. The analysis covered publications on smart cities published in Scopus and Web of Science databases from January 2009 to May 2019. Based on the bibliometric analysis, a bibliometric map was developed using the mapping technique VOS — the visualisation of similarities. Original clusters were created using the VOSviewer software. The bibliometric map visualises the results of the analysis that targeted the word coexistence.

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Smart city concept in the light of the literature review

With the development of emerging engineering technology and industrialization, there are greater changes in the life style of people in smart urban cities; therefore, there is also more chance of various health problems in urban areas. The life style of persons in metro urban areas with the expansive volume of population is similarly influenced by different application and administration frameworks. These are affecting the human health system up to an extended extent and there are more health-related issues and health hazard concerns that can be identified in urban areas. The purpose of this paper is to present an analytical study on various aspects of the smart health care system in a smart perspective by analyzing them with respect to emerging engineering technologies such as mobile network, cloud computing, Internet of Things (IoT), big data analytics and ubiquitous computing. This paper also carries out a detailed survey of health issues and improved solutions in automated systems using these technologies. Second, the paper also presents a novel health care system using smart and safe ambulances and their appropriate control at traffic points with safety and security features in a smart city, so that the valuable life of patients can be saved in time by immediate treatment in nearest hospital or health care units.,In this paper, an analytical survey was conducted for improvement in the health care sector using computer technology and IoT-based various modern health care applications. An idea of Smart Health Care Hospital using sensors, mobile agent smart vehicle configuration and safety traffic control for ambulance was proposed.,A simulation was carried out to see the performance of a safety mechanism in the proposed approach. Comparative analysis was carried out with other approaches to know the execution time, response time and probable delay due to the implementation of this approach.,It is an original research work with motivation inspired from current emergent technology to apply in the health care system.

Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach

Machine learning is a field that is developed out of artificial intelligence (AI). Applying AI, we needed to manufacture better and keen machines. Be that as it may, aside from a couple of simple errands, for example, finding the briefest way between two points, it isn't to program more mind boggling and continually developing difficulties. There was an acknowledgment that the best way to have the capacity to accomplish this undertaking was to give machines a chance to gain from itself. This sounds like a youngster learning from itself. So, machine learning was produced as another capacity for computers. Also, machine learning is available in such huge numbers of sections of technology that we don't understand it while utilizing it. This chapter explores advanced-level security in network and real-time applications using machine learning.

Advanced-Level Security in Network and Real-Time Applications Using Machine Learning Approaches

The emerging technical approach Machine Learning (ML) is apprehensive with the design and growth of algorithms and techniques that allocate computers to “learn”. The major focus of ML research is to extract information from data automatically, by computational and statistical methods. It is thus closely related to data mining and statistics. The power of neural networks stems from their representation capability. In many applications including current discussion of security in satellite communication, feed forward networks are proved to offer the capability of universal function approximation. This chapter thrashes out in details and highlights on important technical issues during machine learning strategies in developing satellite communication systems.

Security Approaches in Machine Learning for Satellite Communication

This paper presents monitoring system for Quality of Service (QoS) based task module called QoS Task Monitoring (QTM) in Mobile Adhoc Networks (MANET) using mobile agent as basic element. Currently MANET is one of the most promising and advanced solution for wireless networks due its significant performance in resuming connectivity in drastic situations. In such environment, there is maximum chance of network disconnection and possibility of immediate set up of network is almost impossible. The fundamental routing process in a MANET involves facilitating uninterrupted communication in the network system between two mobile stations at any point of time and the basic key concern being selection of the most suitable forwarding node to advance the real-time packets from source towards destination so that the optimization of the network can be achieved by maximum utilization of available resources. Transmission of real-time applications is one of the most challenging issue in MANET due to transportation of high volume of data including audio, video, images, animation, and graphics. This paper presents a monitoring approach for checking the Quality of Service (QoS) task modules during competent routing with the use of mobile agents. An intelligent mobile agent is proposed in QTM System which has been designed in the QoS-based platform for checking and controlling the processing tasks using longest critical path method at the forwarding node to select it as the best option out of all neighbor nodes. Simulation result shows higher packet delivery ratio and uniform jitter variation which suits favorably to multimedia and real time applications.

Mamata Rath

Papers

Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach

Advanced-Level Security in Network and Real-Time Applications Using Machine Learning Approaches

Security Approaches in Machine Learning for Satellite Communication

QTM: A QoS Task Monitoring System for Mobile Ad hoc Networks

An Overview on Social Networking: Design, Issues, Emerging Trends, and Security