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Introduction: Internet of Things (IoT), which provides smart services and remote monitoring across healthcare systems according to a set of interconnected networks and devices, is a revolutionary technology in this domain. Due to its nature to sensitive and confidential information of patients, ensuring security is a critical issue in the development of IoT-based healthcare system. Aim: Our purpose was to identify the features and concepts associated with security requirements of IoT in healthcare system. Methods: A survey study on security requirements of IoT in healthcare system was conducted. Four digital databases (Web of Science, Scopus, PubMed and IEEE) were searched from 2005 to September 2019. Moreover, we followed international standards and accredited guidelines containing security requirements in cyber space. Results: We identified two main groups of security requirements including cyber security and cyber resiliency. Cyber security requirements are divided into two parts: CIA Triad (three features) and non-CIA (seven features). Six major features for cyber resiliency requirements including reliability, safety, maintainability, survivability, performability and information security (cover CIA triad such as availability, confidentiality and integrity) were identified. Conclusion: Both conventional (cyber security) and novel (cyber resiliency) requirements should be taken into consideration in order to achieve the trustworthiness level in IoT-based healthcare system.

Αs technologies and communications develop, more sabotaging attacks occur including phishing attacks which jeopardize users' security and critical information like their passwords and credentials. Several solutions have been proposed for existing dangers. One of which is the use of one-time passwords. This issue has remained as a main challenge and requires more extensive research. In this research, we have focused on one-time password combinations and we also have proposed solutions based on behavioral patterns which lead to significant optimizations while tending the simplicity for users. Efficiency of the proposed method has been measured through defining scenarios, modeling and simulations based on a prevention rate index. In addition, complexity coefficient of the proposed method showing the probability of unpredictability of passwords for attackers has been calculated. Ultimately, a descriptive comparison has shown that the proposed method is superior to some of the existing methods.

Objective The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. Methods The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). Results Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. Conclusions Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

Internet of Things (IoT), known as a new paradigm, has shown to have a significant role in healthcare domains including remote vital sign monitoring systems, physical activity tracking, early disease diagnosis, and prevention of disease risks. Therefore, designing an integrated healthcare system based on Internet of Things is highly dependent on designing a layered architecture pattern. However, there are no comprehensive studies on Internet of Things layered architecture in the healthcare industry. The purpose of this study was to identify and scrutinize different types of layered architecture of Internet of Things in healthcare in terms of functions, and technologies. We evaluated studies proposing layered architecture of Internet of Things based on security aspects (security requirements and solutions). A systematic literature review was conducted by searching IEEE, PubMed, Scopus and Web of Science between 2005 and. We were able to find 47 academic studies based on inclusion and exclusion criteria. We systematically reviewed applied functions and technologies and categorized them into three main layers namely, the perception, network, and application layers. This study also presented a comprehensive classification of sensor types. Only 28 out of 47 studies proposing Internet of Things architecture addressed security aspects among which privacy, authentication, and access control, confidentiality, and integrity had the highest rank. The layered architecture of Internet of Things is needed to provide an integrated framework for healthcare system, make better communication, and enhance the information management process. We suggest several potential solutions for future research directions according to technical, management, and security challenges Povzetek: Podan je pregled literatura za zdravstvene sisteme, ki uporabljajo večnivojske arhitekture interneta stvari.

Introduction: The Internet of Things (IoT) and Cloud computing are two recent technological advances whose potential have not been realized in a range of industries. These technologies have been used in healthcare systems to improve their performance. In this regard, the IoT generates a large amount of data; also, cloud computing is a viable option for data storage and complex computing. The purpose of this study is to identify and categorize various aspects of CIoT-based healthcare in terms of main application domains, sensors, wireless communication technologies, messaging protocols, cloud platforms, and artificial intelligence (AI) algorithms.Material and Methods: We conducted a literature systematic review and reported according to the PRISMA guideline. PubMed, IEEE, Scopus, and Web of Science were searched using the related keywords and their synonyms. Two independent authors reviewed the papers' eligibility according to the defined inclusion and exclusion criteria.Results: Of the 2,118 papers retrieved, 61 were eventually selected in the study.  Results of the present study revealed that the majority of CIoT research works were applied to patient monitoring systems and cardiovascular patient monitoring systems using Amazon and IBM dominating Cloud platforms. In addition, the most widely used communication technologies for CIoT in healthcare are cellular networks (3G and 4G), Wi-Fi, and Bluetooth. Cardiovascular, environmental, and position sensors are also the most common types of sensors used in healthcare CIoT applications. Among the Cloud platform providers, Amazon and IBM have the highest utility in healthcare systems. The majority of the included studies used Cloud-based AI algorithms to diagnose, classify, and predict diseases.Conclusion: The integration of the Cloud into the IoT can support healthcare systems in terms of processing power, storage capacity, security, privacy, performance, reliability, and scalability. We suggest researchers conduct experimental studies to evaluate the effectiveness of the CIoT approach in healthcare applications.

Background: the aging phenomenon has an increasing trend worldwide which caused the emergence of the SA1 concept. It is believed that the SA prediction model can increase the QoL2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and social factors by considering all factors affecting SA.Materials and methods: in this descriptive, applied, and retrospective study, the data of 975 related to SA and non-SA of the elderly were investigated. We used the Chi-square test at P<0.05 to determine the best factors affecting the SA. The AB3, J-48, RF4, ANN5, SVM6, BLR7, and NB8 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using the sensitivity, specificity, accuracy, F-measure, and AUC. Results: The Chi-square test showed that 28 variables had a meaningful relationship with SA. The results of comparing the ML9 model's performance showed that the RF with sensitivity=0.91, specificity= 0.98, accuracy= 0.95, F-test=0.9, and AUC-test= 0.884 is the best model for predicting the SA. Conclusion: using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

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