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

A Novel Hybrid Convolutional Neural Network- and Gated Recurrent Unit-Based Paradigm for IoT Network Traffic Attack Detection in Smart Cities

Internet of Things (IoT) devices within smart cities, require innovative detection methods. This paper addresses this critical challenge by introducing a deep learning-based approach for the detection of network traffic attacks in IoT ecosystems. Leveraging the Kaggle dataset, our model integrates Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to capture both spatial and sequential features in network traffic data. We trained and evaluated our model over ten epochs, achieving an impressive overall accuracy rate of 99%. The classification report reveals the model's proficiency in distinguishing various attack categories, including 'Normal', 'DoS' (Denial of Service), 'Probe', 'U2R' (User to Root), and 'Sybil'. Additionally, the confusion matrix offers valuable insights into the model's performance across these attack types. In terms of overall accuracy, our model achieves an impressive accuracy rate of 99% across all attack categories. The weighted- average F1-score is also 99%, showcasing the model's robust performance in classifying network traffic attacks in IoT devices for smart cities. This advanced architecture exhibits the potential to fortify IoT device security in the complex landscape of smart cities, effectively contributing to the safeguarding of critical infrastructure.

Toward integrated smart city: a new model for implementation and design challenges

In smart city architecture, information and communication technologies are used to improve living standards and its management by citizens and government. Most researchers have divided this structure into six main components: smart people, the smart government, smart environment, smart transportation, smart economy, and smart life. Due to the connection between smart cities and the challenges resulting from their implementation and especially its integration, there exists no perfect solution for the concept of an integrated smart city so far according to our studies. Some more general concepts such as security, ICT infrastructure, and knowledge are not seen integrative in these structures. Therefore, it seems that new sub-components and general extra-components should be added to the existing models to form an integrated structure in such a way that the executive projects are located in their proper place in this structure and create and guarantee the integration of the smart city. Therefore, the requirements engineering of the smart city can also be explained more precisely. This study presents a model of an integrated graph in such a way that besides maintaining and improving the model of the smart city and existing models, it will fully cover the integration and requirements engineering and methodologies of the smart city in the future. The present paper offers an upgraded model of a six-component smart city structure as a flexible integrated dynamic graph so that beside maintaining the features of existing smart city models, it ensures its integrity, dynamism, flexibility and performance and prevents the failure of smart operations. Due to its flexibility, adaptability and localization, the proposed model presented in this paper can create an integrated solution and facilitating the life cycle of executive systems and enable governments and communities to predict and prevent sudden events such as natural disasters, pandemics like Covid-19 and the like as well as managing and leading their target community in the best way.

Physician’s perception of E-consultation adoption amid of COVID-19 pandemic

Purpose

The purpose of this paper is to investigate physicians’ perceptions of e-consultation adoption, which has the potential to bridge existing gaps in the current health-care system, using the unified theory of acceptance and use of technology (UTAUT2) framework.

Design/methodology/approach

The judgemental sampling method was embraced to collect primary data from 337 physicians from Delhi-National Capital Region who had experience with e-consultation. A number of hypotheses was developed and tested using structural equation model based on UTAUT2.

Findings

The study’s findings revealed an affirmative and significant relation between a physician’s intention to embrace e-consultation and facilitating conditions, effort expectancy, social influence and performance expectancy; however, habit and experience are not significantly linked to it.

Originality/value

This study will not only add to the existing body of knowledge about e-consultation adoption, but it will also assist electronic health service providers in devising strategies to encourage the usage of e-consultation services in emerging economies such as India where people are deprived of the right to access better health care due to lack of physical infrastructure.

Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.

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

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.

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

The world has been facing the challenge of COVID-19 since the end of 2019. It is expected that the world will need to battle the COVID-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time COVID-19 detection and monitoring system. The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. An experiment was conducted to test these eight algorithms on a real COVID-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 %. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus.

Applying Nanomaterials to Modern Biomedical Electrochemical Detection of Metabolites, Electrolytes, and Pathogens

Personal biosensors and bioelectronics have been demonstrated for use in out-of-clinic biomedical devices. Such modern devices have the potential to transform traditional clinical analysis into a new approach, allowing patients or users to screen their own health or warning of diseases. Researchers aim to explore the opportunities of easy-to-wear and easy-to-carry sensors that would empower users to detect biomarkers, electrolytes, or pathogens at home in a rapid and easy way. This mobility would open the door for early diagnosis and personalized healthcare management to a wide audience. In this review, we focus on the recent progress made in modern electrochemical sensors, which holds promising potential to support point-of-care technologies. Key original research articles covered in this review are mainly experimental reports published from 2018 to 2020. Strategies for the detection of metabolites, ions, and viruses are updated in this article. The relevant challenges and opportunities of applying nanomaterials to support the fabrication of new electrochemical biosensors are also discussed. Finally, perspectives regarding potential benefits and current challenges of the technology are included. The growing area of personal biosensors is expected to push their application closer to a new phase of biomedical advancement.

P, P.J. K, S.S. M, Rajendran S, Kumar K, S. S, Jothikumar R. IoT role in prevention of COVID-19 and health care workforces behavioural intention in India - an empirical examination

Purpose

This paper aims to address the role of Internet of Things (IoT) in preventing COVID-19. The IoT devices can be used in various ways to track the patients and suspected person. Remote data collection can be done with the help of IoT and sensors. Later, the data can be analyzed with the help of data science engineers and researchers to predict and prevent the COVID-19.

Design/methodology/approach

IoT is a creative mean of amalgamating clinical gadgets and their applications to associate with the human services and data innovation frameworks. An investigation on the conceivable outcomes of defying progressive COVID-19 pandemic by implementing the IoT approach while offering treatment to all classes of patient without any partiality in poor and rich. The information sharing, report checking, patient tracking, data social affair, investigation, cleanliness clinical consideration and so forth are the different cloud-based administrations of IoT. It can totally change the working format of the medical services while rewarding the huge volume of patients with a predominant degree of care and more fulfilment, particularly during this pandemic of COVID-19 lockdown. Health workers can quickly focus on patient zero and identify everyone who has come into contact with the infected person and move these people to quarantine/isolation. As COVID-19 has emerged from the Wuhan province of China, IoT tools such as geographic information system could be used as an effective tool to curb the spread of pandemics by acting as an early warning system. Scanners at airports across the world could be used to monitor temperature and other symptoms. This paper addresses the role of IoT in preventing COVID-19.

Findings

In the period of continuous pandemic of COVID-19, IoT offers many propelled cloud-based administrations and offices to serve a greater number of patients effectively. The remote medicinal services framework provides a lot of significance in such a crucial time of lockdown. The powerful interconnected arrangement of gadgets, applications, Web, database and so on encourages the consumers to benefit the administrations in smart way. IoT additionally advances its administrations by building up the quality culture of perceptive medicinal services or portable centre. It is a “distinct advantage innovation,” which may totally change the practices universally. Indeed, even its quality administrations in this extreme time make this methodology progressively productive and beneficial. IoT helps in observing and tracking more recognized people and patients in remote areas for their human service prerequisites. The customary medicinal services are probably going to observe a huge change in perspective sooner rather than later, as the computerized revolution would place cutting-edge innovation and its associated items in the possession of the patients and give both patients and doctors in remote areas better access to quality clinical services.

Originality/value

The contemporary exploration study focuses on the proposed IoT system for the treatment of patients in this progressing COVID-19. The working principle of IoT approach incorporates the mix of human services apparatuses, clinical treatment framework, Web organize, programming and administrations. IoT framework empowers the information assortment, report observing, understanding database, testing pictures and investigation and so forth. Data has been collected through online mode; in this study, the authors adopted empirical research design. Total 150 (118/150 = 78.66% respondent response ratio) online questionnaires were sent in the Chennai city of Tamilnadu, India. The participated nature of work is clinical examination in critical care division.

Debunking the myth of industry 4.0 in health care: insights from a systematic literature review

Purpose

Industry 4.0, artificial intelligence and digitalization have got a momentum in health care. However, scholars and practitioners do not agree on their implications on health services' quality and effectiveness. The article aims at shedding light on the applications, aftermaths and drawbacks of industry 4.0 in health care, summarizing the state of the art.

Design/methodology/approach

A systematic literature review was undertaken. We arranged an ad hoc research design, which was tailored to the study purposes. Three citation databases were queried. We collected 1,194 scientific papers which were carefully considered for inclusion in this systematic literature review. After three rounds of analysis, 40 papers were taken into consideration.

Findings

Industry 4.0, artificial intelligence and digitalization are revolutionizing the design and the delivery of care. They are expected to enhance health services' quality and effectiveness, paving the way for more direct patient–provider relationships. In addition, they have been argued to allow a more appropriate use of available resources. There is a dark side of health care 4.0 involving both management and ethical issues.

Research limitations/implications

Industry 4.0 in health care should not be conceived as a self-nourishing innovation; rather, it needs to be carefully steered at both the policy and management levels. On the one hand, comprehensive governance models are required to realize the full potential of health 4.0. On the other hand, the drawbacks of industry 4.0 should be timely recognized and thoroughly addressed.

Originality/value

The article contextualizes the state of the art of industry 4.0 in the health care context, providing some insights for further conceptual and empirical developments.

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

Design and Development of Secured Framework for Efficient Routing in Vehicular Ad-Hoc Network

Due to many challenging issues in vehicular ad-hoc networks (VANETs), such as high mobility and network instability, this has led to insecurity and vulnerability to attacks. Due to dynamic network topology changes and frequent network re-configuration, security is a major target in VANET research domains. VANETs have gained significant attention in the current wireless network scenario, due to their exclusive characteristics which are different from other wireless networks such as rapid link failure and high vehicle mobility. In this are, the authors present a Secured and Safety Protocol for VANET (STVAN), as an intelligent Ad-Hoc On Demand Distance Vector (AODV)-based routing mechanism that prevents the Denial of Service attack (DoS) and improves the quality of service for secured communications in a VANET. In order to build a STVAN, the authors have considered a smart traffic environment in a smart city and introduced the concept of load balancing over VANET vehicles in a best effort manner. Simulation results reveal that the proposed STVAN accomplishes enhanced performance when compared with other similar protocols in terms of reduced delay, better packet delivery ratio, reasonable energy efficiency, increased network throughput and decreased data drop compared to other similar approach.

Appraisal of Soft Computing Methods in Collaboration With Smart City Applications and Wireless Network

Applications of soft computing methods are spread in fields that deal with intelligent analysis. As the human intelligence can survey the likelihood of some occasions in possibilities, comparatively soft computing systems additionally utilize some smart-based strategies to evaluate ongoing issues with diagnostic models. Fundamental segments of soft computing incorporate machine learning, probabilistic thinking, swarm intelligence, and ANN algorithms. In this research article, there is a broad analysis of these intelligence-based soft computing strategies connected as different operational parts of a wireless network and there is a scheme of a soft computing-based method for smart and safe health care systems.