Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions
TL;DR: A comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications is presented in healthcare domains and the presented future directions in this domain can significantly help the scholarly community determine research gaps to address.
Abstract: Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as ‘machine learning’, blockchain, ‘Internet of Things or IoT’, and keywords conjoined with ‘healthcare’ and ‘health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.
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TL;DR: In this article , the authors proposed an IoT task management mechanism based on predictive optimization for energy consumption minimization in smart residential buildings, which has a predictive optimization module based on prediction and an optimization module for solving energy consumption optimization problems.
29 citations
TL;DR: This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector and comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies.
Abstract: With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.
17 citations
TL;DR: A Blockchain-based technique oriented on IoMT applications with a focus on maintaining Confidentiality, Integrity, and Availability (the CIA triad) of data communication in the system is proposed, oriented toward trusted and secure real-time communication.
Abstract: The Internet of Medical Things (IoMT) global market has grown and developed significantly in recent years, and the number of IoMT devices is increasing every year. IoMT systems are now very popular and have become part of our everyday life. However, such systems should be properly protected to preventing unauthorized access to the devices. One of the most popular security methods that additionally relies on real-time communication is Blockchain. Moreover, such a technique can be supported by the Trusted Third Party (TTP), which guarantees data immutability and transparency. The research and industrial community has predicted the proliferation of Blockchain-based IoMT (BIoMT), for providing security, privacy, and effective insurance processing. A connected environment comprises some of the unique features of the IoMT in the form of sensors and devices that capture and measure, recognize and classify, assess risk, notify, make conclusions, and take action. Distributed communication is also unique due to the combination of the fact that the Blockchain cannot be tampered with and the Peer-to-Peer (P2P) technique, especially compared to the traditional cloud-based techniques where the reliance of IoMT systems on the centralized cloud makes it somewhat vulnerable. This paper proposes a Blockchain-based technique oriented on IoMT applications with a focus on maintaining Confidentiality, Integrity, and Availability (the CIA triad) of data communication in the system. The proposed solution is oriented toward trusted and secure real-time communication. The presented method is illustrated by an example of a cloud-based hospital application. Finally, the security aspects of the proposed approach are studied and analyzed in detail.
11 citations
Journal Article•
TL;DR: This paper gives out specific design of quantum group classifier and sympletic group classifiers, andceptions of Lie group machine learning, assumption axioms, algebra learning model, geometric learning model and so on.
Abstract: This paper summarizes the relevant research of Lie group machine learning,including:conceptions of Lie group machine learning,assumption axioms,algebra learning model,geometric learning model,geometric learning algorithms of Dynkin diagram,learning algorithms of orbits generated and so on.Especially,this paper gives out specific design of quantum group classifier and sympletic group classifier.
10 citations
TL;DR: Experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks and offer an automated BTD method that aids in the early diagnosis of brain cancers.
Abstract: Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.
6 citations
References
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27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
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TL;DR: Aerosol and Surface Stability of SARS-CoV-2 In this research letter, investigators report on the stability of Sars-CoVs and the viability of the two virus under experimental conditions.
Abstract: Aerosol and Surface Stability of SARS-CoV-2 In this research letter, investigators report on the stability of SARS-CoV-2 and SARS-CoV-1 under experimental conditions. The viability of the two virus...
7,412 citations
TL;DR: An overview of the Internet of Things with emphasis on enabling technologies, protocols, and application issues, and some of the key IoT challenges presented in the recent literature are provided and a summary of related research work is provided.
Abstract: This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.
6,131 citations
"Comprehensive Survey of IoT, Machin..." refers background in this paper
...Other such challenges include IoT and cloud integration [15], IoT standardization [16], IoT scalable architecture, and IoT security [17–20]....
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TL;DR: The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday.
Abstract: It has been next to impossible in the past months not to come across the term ‘‘Internet of Things’’ (IoT) one way or another. Especially the past year has seen a tremendous surge of interest in the Internet of Things. Consortia have been formed to define frameworks and standards for the IoT. Companies have started to introduce numerous IoTbased products and services. And a number of IoT-related acquisitions have been making the headlines, including, e.g., the prominent takeover of Nest by Google for $3.2 billion and the subsequent acquisitions of Dropcam by Nest and of SmartThings by Samsung. Politicians as well as practitioners increasingly acknowledge the Internet of Things as a real business opportunity, and estimates currently suggest that the IoT could grow into a market worth $7.1 trillion by 2020 (IDC 2014). While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses. The origins of the term date back more than 15 years and have been attributed to the work of the Auto-ID Labs at the Massachusetts Institute of Technology (MIT) on networked radio-frequency identification (RFID) infrastructures (Atzori et al. 2010; Mattern and Floerkemeier 2010). Since then, visions for the Internet of Things have been further developed and extended beyond the scope of RFID technologies. The International Telecommunication Union (ITU) for instance now defines the Internet of Things as ‘‘a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies’’ (ITU 2012). At the same time, a multitude of alternative definitions has been proposed. Some of these definitions exhibit an emphasis on the things which become connected in the IoT. Other definitions focus on Internet-related aspects of the IoT, such as Internet protocols and network technology. And a third type centers on semantic challenges in the IoT relating to, e.g., the storage, search and organization of large volumes of information (Atzori et al. 2010). The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday. The most prominent areas of application include, e.g., the smart industry, where the development of intelligent production systems and connected production sites is often discussed under the heading of Industry 4.0. In the smart home or building area, intelligent thermostats and security systems are receiving a lot of attention, while smart energy applications focus on smart electricity, gas and water meters. Smart transport solutions include, e.g., vehicle fleet tracking and mobile ticketing, while in the smart health area, topics such as patients’ surveillance and chronic disease management are being addressed. And in the context of Accepted after one revision by Prof. Dr. Sinz.
3,499 citations
01 Jan 2020
TL;DR: Globally, as of 10,47am CEST, 28 May 2020, there have been 5,556,679 confirmed cases of COVID-19, including 351,866 deaths, reported to WHO.
Abstract: Globally, as of 10:47am CEST, 28 May 2020, there have been 5,556,679 confirmed cases of COVID-19, including 351,866 deaths, reported to WHO
2,785 citations