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Health is one of the most important aspects of life. However, people still could not get proper health services. It is caused by limitation to the used technology in hospitals and limitations to get to the hospital. Internet-of-Things (IoT) as one of the most trending topics nowadays, already giving so many solutions in many ways, for example, in healthcare. IoT is implemented in many healthcare ways, including detecting disease as a precaution, treating a disease as a healing solution, and monitoring a disease as a healing process itself. As part of IoT, wearable devices have been developed to help people get the right treatment for themselves. In this paper, a systematic literature review of wearable IoT devices for healthcare is conducted. The review is started by searching the references by pre-defined keywords and filtering the references based on the relevance and the published year. In this review, we discuss the utilization of wearable devices in handling the problem in the healthcare aspects, including disease detection, monitoring, and curing. Other than that, this review paper also explained how the architecture of wearable devices applied. Finally, the future challenge of research in this field is defined. [ABSTRACT FROM AUTHOR]

There are various diseases arising in the world with new unexpected areas. Each disease has its own symptoms and cures. Some people are capable to fight against these diseases but most of them are a part of the attacks done by the diseases. This paper introduces the Parkinson's disease patient with the idea to work their body normally with the help of artificial life to their neurons by adding a chip known as Electroencephalography (EEG) into their brains that work in the form of wearable devices. This is required for those people who are suffering from this disease and could not able to communicate with another people just because they cannot able to send their signals properly to the brain because they do not get the signals on time because of dead neurons. This paper is going to introduce architecture to work on the brain by using several chips, which help to send signals and become a mediator/ transmitter who sends to do work. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has penetrated its roots in almost every domain of life. Smart healthcare is one of the major domains that extensively uses IoT infrastructures and solutions. IoT-based smart healthcare systems have immensely added value to the healthcare domain with the use of wearable and mobile devices. This leads to a substantial use of health data sharing for the improved, accurate, and timely diagnosis. However, smart healthcare systems are highly vulnerable to several security breaches and various malignant attacks, such as privacy leakage, tempering, forgery, etc. Recently, the blockchain technology emerged as a propitious solution against such breaches and challenges. This paper presents an up-to-date survey on different challenges and open issues faced in smart healthcare due to the traditional security measures along with the security requirements of such domains. It also amalgamates the potentials of blockchain technology as a promising security measure, highlights potential challenges in the healthcare domain, and provides an analysis of different blockchain-based security solutions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects huge number of devices using the internet to collect data. The IoT has a variety of application domains, including health care. In this scenario, the patients can use wearable or implanted medical sensors for measuring medical parameters anywhere and anytime. The medical sensors are typically equipped with batteries and have limited energy. In order to prolong the network lifetime, a powerful routing protocols are required. In this paper, we propose PEERP: An Priority-based Energy-Efficient Routing Protocol for Reliable Data Transmission in Healthcare using the IoT. We classify health information into two categories assigned corresponding priorities: Emergency Situation and Vital Health Data. The Emergency Situation is the highest priority data and should be successfully delivered as soon as required. The Vital Health data is the data requested by doctors for continuous monitoring of the patients. The direct communication is used for critical data while Multi-hop communication is used for vital health data delivery. The simulation results show that the routing protocol can improve the energy utilization of the sensor nodes and prolong the network lifetime while guaranteeing the delivery ratio and reliability. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) as a non-invasive and low-cost technique plays a significant role in wearable Internet-of-Things based health monitoring systems, enabling continuous health and well-being data collection. As PPG monitoring is relatively simple, non-invasive, and convenient, it is widely used in a variety of wearable devices (e.g., smart bands, smart rings, smartphones) to acquire different vital signs such as heart rate and pulse rate variability. However, the accuracy of such vital signs highly depends on the quality of the signal and the presence of artifacts generated by other resources such as motion. This unreliable performance is unacceptable in health monitoring systems. To tackle this issue, different studies have proposed motion artifacts reduction and signal quality assessment methods. However, they merely focus on improvements in the results and signal quality. Therefore, they are unable to alleviate erroneous decision making due to invalid vital signs extracted from the unreliable PPG signals. In this paper, we propose a novel PPG quality assessment approach for IoT-based health monitoring systems, by which the reliability of the vital signs extracted from PPG quality is determined. Therefore, unreliable data can be discarded to prevent inaccurate decision making and false alarms. Exploiting a Convolutional Neural Networks (CNN) approach, a hypothesis function is created by comparing heart rate in the PPG with corresponding heart rate values extracted from ECG signal. We implement a proof-of-concept IoT-based system to evaluate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

• To present a very recent systematic survey about online heart monitoring systems. • To highlight the number of remote technologies enabled to aid cardiac patient and family. • The IoT-based techniques found are identified and its advances and open issues are discussed. • This survey is interesting both for researchers and health professionals. The Internet of Health Things promotes personalized and higher standards of care. Its application is diverse and attracts the attention of a substantial section of the scientific community. This approach has also been applied by people looking to enhance quality of life by using this technology. In this paper, we perform a survey that aims to present and analyze the advances of the latest studies based on medical care and assisted environment. We focus on articles for online monitoring, detection, and support of the diagnosis of cardiovascular diseases. Our research covers published manuscripts in scientific journals and recognized conferences since the year 2015. Also, we present a reference model based on the evaluation of the resources used from the selected studies. Finally, our proposal aims to help future enthusiasts to discover and enumerate the required factors for the development of a prototype for online heart monitoring purposes. [ABSTRACT FROM AUTHOR]

Digital images are being frequently used for diagnosis in clinics today. Diagnostic images with identifying patient data are stored and transmitted across open networks. Security measures are therefore becoming more and more crucial to safeguard the confidentiality of sensitive patient data. In medical imaging, adjacent pixels show a strong degree of connection. This paper presents an efficient intra-inter bit-level pixel operation-based image encryption method that eliminates pixel correlation and provides strong security. In this method, after gathering medical image data from IoT sensors, the intra bit-level pixel permutation and diffusion operations are executed separately and in parallel in each of the bit planes, and the inter bit-level pixel permutation and diffusion operations are then performed in the concatenated bit-planes. The bit-level operation not only permutes but also diffuses the pixels at the same time. As a result, the security of the algorithm is increased by executing several pixel permutations and diffusions. In addition to this, the approach is effective due to the parallel execution of bit-plane permutation and diffusion. The simulation results and the security analysis indicate the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) as a non-invasive and low-cost technique plays a significant role in wearable Internet-of-Things based health monitoring systems, enabling continuous health and well-being data collection. As PPG monitoring is relatively simple, non-invasive, and convenient, it is widely used in a variety of wearable devices (e.g., smart bands, smart rings, smartphones) to acquire different vital signs such as heart rate and pulse rate variability. However, the accuracy of such vital signs highly depends on the quality of the signal and the presence of artifacts generated by other resources such as motion. This unreliable performance is unacceptable in health monitoring systems. To tackle this issue, different studies have proposed motion artifacts reduction and signal quality assessment methods. However, they merely focus on improvements in the results and signal quality. Therefore, they are unable to alleviate erroneous decision making due to invalid vital signs extracted from the unreliable PPG signals. In this paper, we propose a novel PPG quality assessment approach for IoT-based health monitoring systems, by which the reliability of the vital signs extracted from PPG quality is determined. Therefore, unreliable data can be discarded to prevent inaccurate decision making and false alarms. Exploiting a Convolutional Neural Networks (CNN) approach, a hypothesis function is created by comparing heart rate in the PPG with corresponding heart rate values extracted from ECG signal. We implement a proof-of-concept IoT-based system to evaluate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

People today are a capable of living longer, healthier lives than ever before, which results in a growing elderly population. The elderly face issues of reduced physical ability, having to manage multiple health issues over long periods of time, and being digital immigrants. These issues pose unique challenges that often lead to elderly feeling loss of agency due to not being actively involved in managing their own well-being. In this paper, we propose participatory design of IOT technologies to enact universal design in order to better include elderly in managing their health, and thus improving their quality of life. [ABSTRACT FROM AUTHOR]

The rising elderly demographic, often with long-term conditions, represents a significant challenge globally in terms of planning for the efficient use of increasingly expensive and constrained health care resources. The internet of things (IoT) emerged as a disruptive and transformative new technology that could potentially stimulate development of new innovative assisted living health and care services. In this paper, we argue that as the human agency and relationship intrinsically associated with care get transferred to the material agency of smart technology, value and trust should be a vital consideration for designing such services. Drawing on interdisciplinary perspectives from the literature on services innovation, design science and trust in relation to healthcare technologies, we present a conceptual framework that articulates various levels of trust among the concerned stakeholders in the service ecosystem and suggests value-sensitive design considerations, anchored on the principles of trust, for future IoT-enabled assistive care services. [ABSTRACT FROM AUTHOR]

The enormous exploitation of connected objects for patient monitoring becomes a source of a huge quantity of health data. This data can be badly expressed, understood, and exploited by other systems and devices. Additionally, the huge amount of connected objects embeds with resource-constrained devices (sensors, actuators, RFIDS, etc.) are also challenging. Consequently, semantic representation of both medical connected objects and their data becomes important. Furthermore, the semantic reasoning of this data requires the definition of diverse rules in order to provide adequate and efficient results. This paper suggests two main contributions. The first one focuses on the semantic representation of both the medical connected objects and their data by proposing a HealthIoT ontology. The second contribution is proposed to provide practical backup to the use of this ontology. In fact, an IoT Medicare system is proposed to evaluate the HealthIoT ontology. [ABSTRACT FROM AUTHOR]

Recently, with the advances of information and electrical technologies, Internet of Thing (IoT) has moved from being a far vision to an increasing market reality. IoT promises to connect all physical “things” via a dynamic global network infrastructure and create intelligent behaviors in different contexts without human manipulations. In line with this development, the number of IoT solutions also have augmented exponentially covering many domains from health care, urban management to life quality improvement and so forth. However, separate appearances of these solutions today has led to barriers in enlarging smart environments because they lack a mechanism for integration. It is well-known that in the very near future, IoT requires the capabilities of high scale and dynamic adaptation with linking many new and existing smart contexts together. Hence, the need of having a comprehensive management method for the large-scale IoT environments is urgent at the time. In this paper, we focus on designing an overlay management architecture based on Chord algorithm to manage things in heterogeneous space. The architecture is a collection of multiple peer Chord rings, and thus each of these rings is considered as a smart context. We also bring forward operation mechanisms for the architecture such as thing identification, key insert/remove, key lookup and node joining/leave. Our proposed architecture is simulated and evaluated with diverse properties. The gained results showed the feasibility of our approach in reality meeting scalability, robustness and maintainability requirements applying to manage and control cyber-physical systems. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework that takes advantage of the computing capabilities provided by the Internet of Thing (IoT) paradigm in order to support collaborative applications. It looks at the requirements needed to run a wide range of computing tasks on a set of devices in the user environment with limited computing resources. This approach contributes to building the social dimension of the IoT by enabling the addition of computing resources accessible to the user without harming the other activities for which the IoT devices are intended. The framework mainly includes a model of the computing load, a scheduling mechanism and a handover procedure for transferring tasks between available devices. The experiments show the feasibility of the approach and compare different implementation alternatives. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective. [ABSTRACT FROM AUTHOR]

The new era of information technologies made smart healthcare by including edge, Internet of Things (loT), Internet of Medical Things (IoMT), cloud computing etc. This adaptation has partially replaced the old medical system, making healthcare more efficient, easy, and customized. Wireless medical sensors are used in the smart healthcare sector to capture the patient's health data and transmit it to the remote server for treatment or diagnostic purposes. Every time a cloud-based solution is adopted to collect and preserve the sensitive and personal health information. This collected patient data is shared between the different stakeholders across the network. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. Secure data access is a big challenge in such systems. In this paper, a lightweight authentication technique with the privacy-preserving schema using fully homomorphic encryption is proposed. This approach can be used for a secure access of patient data through online mode, and also the data can be shared in an encrypted format for desired purposes of stakeholders. The proposed authentication scheme is for Internet of Medical Things that can resist all network attacks and is easy to implement. The performance analysis of the proposed work shows that overall execution time (80.03%), communication cost (15.62%) and energy consumption (79.19%) is reduced in comparison with existing work. [Display omitted] • A lightweight mutual authentication scheme for smart healthcare systems is designed and implemented using on login ID, password, and perceptual hash based biometric security. • A formal security analysis is introduced using Burrows–Abadi–Needham (BAN) logic that resists all well-known attacks and provide anonymity of patient identity. • A performance comparison of the proposed scheme with existing schemes is also carried out in terms of security features and communication overheads, computation overheads, storage and network throughput. [ABSTRACT FROM AUTHOR]

• A novel deep learning models for arrhythmia detection in IoT healthcare applications are proposed. • It has the independence of the ECG signal quality, as operation is on the spectrograms of the ECG signals. • Ability to work on a single ECG lead, which reduces the complexity of operations. • Applicability of the proposed framework in the smart healthcare platform for real-time operation. • The proposed framework overcomes the overfitting problems and achieves high accuracy on several datasets. In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D format, and then the obtained images are fed into the proposed DLMs for classification. This helps to overcome most of the problems of the previous machine and deep learning models such as overfitting, and working on more than one lead of ECG signals. We use several publicly available datasets from PhysioNet such as MIT-BIH, PhysioNet 2016 and PhysioNet 2018 for model assessment. Overall accuracies of 97%, 98 %, 94 % and 91 % are obtained on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016 dataset, and PhysioNet 2018 dataset, respectively. Compared to the previous works, the proposed framework is more robust and efficient, especially in the case of noisy data. Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. [Display omitted] [ABSTRACT FROM AUTHOR]

Critical infrastructures (CIs) include the vital resources for the country's economic and health systems and should be kept secure. We face improvements in the Internet of Things which brings benefits and, at the same time, dependency for CIs. Internet of Medical Things (IoMT) is among the CI sectors that gather health-related information from patients via sensors and provide healthcare services accordingly. However, research has highlighted that this large-scale system opens the door to the patients' private data disclosure. Recent work has concentrated on proposing authentication schemes to address this challenge. Motivated by this, in this paper, we introduce a secure and lightweight authentication and key agreement model named Slight. We informally prove Slight's security and robustness against attacks and formally by using the Scyther tool. We analyze Slight's performance to show it causes minimal computational overhead (0.0076 ms) and comparable communication overhead (1632 bits), making it suitable for IoMT. [Display omitted] • We propose secure and lightweight authentication and key agreement model named Slight for IoMT environments to provide privacy and security of patients' health-related data. • The security of the proposed protocol is validated formally through the Scyther tool. • The proposed protocol provides resistance against well-known potential security attacks. • The performance analysis showed that proposed protocol is more efficient than other competing protocols in terms of computational and communication overhead. [ABSTRACT FROM AUTHOR]

• A novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. • 3-DES has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. • The encrypted and authenticated ECG features are transmitted to cardiologist for analysis via an IoT platform. • The significant of the proposed technique attains better value of results as compared to the existing methods. Telecardiology offers early detection, consultations and disease management in Golden Hours to patients. Transmitting ECG data to a cardiologist without security may incline to misdiagnosis. In this paper, a novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. Firstly, ECG datasets are taken from the MIT-BIH database and pre-processed. Savitzky-Golay (SG) filter has been used for the removal of low-frequency noise, while high frequency noise is eliminated using maximal overlap wavelet packet transform (MOWPT) followed by features extraction. For security, triple data encryption standard (3-DES) has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. Then encrypted and authenticated ECG features are transmitted to cardiologist for analysis via ThingSpeak, an internet of things (IoT) platform. The performance of the proposed method is calculated viz. avalanche effect is 50.12% and mean square error is 0.463, number of pixels change rate (NPCR) is 100, unified averaged changed intensity (UACI) is 39.698 and execution time is 0.003 msec. The significant of the proposed technique provides minimum error with less processing time, better values of avalanche effect, NCPR and UACI as compared to the existing methods. [ABSTRACT FROM AUTHOR]