BSNCare+: A Robust IoT-Oriented Healthcare System with Non-Repudiation Transactions

Yeh, Kuo-Hui

doi:10.3390/app6120418

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…Non-repudiation: This metric encompasses the ability of the system to validate the presence or absence of an activity (Mosenia & Jha 2016;Yeh 2016). It ensures that the sending node is granted a delivery receipt and the receiver node is granted a proof on the identity of the sender so that none of them is denied in the process (Newaz et al 2020).…”

Section: B Security and Privacy In The Iomtmentioning

confidence: 99%

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

2021

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Background . The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device's lifespan.Methodology. This paper is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 to 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis were conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results.It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

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Section: B Security and Privacy In The Iomtmentioning

confidence: 99%

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

2021

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“…Previous studies identified significant advantages to utilizing Raspberry Pi for similar scenarios, including secure and robust healthcare services [62], intelligent video surveillance platforms [63], building management systems [64], and beehive monitoring [65]. Edge computing benefits have been clarified by several studies to include reduced response time [66] and energy consumption [67].…”

Section: Managerial Implicationsmentioning

confidence: 99%

An Affordable Fast Early Warning System for Edge Computing in Assembly Line

et al. 2018

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Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.

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“…BSNCare+ was proposed in [ 36 ], it is an IoT-based health care system which implements BSN. Another application was proposed in [ 37 ], it name is BSN-Care.…”

Section: Revised Soa Applicationsmentioning

confidence: 99%

“…On the other hand, in the case of the implementations exposed in [ 34 , 35 ], security was left in the hands of the HTTPS protocol. Yeh et al [ 36 ] implemented a robust authentication system generating a random number and an exclusive function. The security in [ 37 ] is divided into two aspects: network security and data security.…”

Section: Analysis From the Perspective Of Security Implementationsmentioning

confidence: 99%

Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare

Avila

Sanmartin

Molinares

et al. 2017

Sensors

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This article makes a literature review of applications developed in the health industry which are focused on patient care from home and implement a service-oriented (SOA) design in architecture. Throughout this work, the applicability of the concept of Internet of Things (IoT) in the field of telemedicine and health care in general is evaluated. It also performs an introduction to the concept of SOA and its main features, making a small emphasis on safety aspects. As a central theme, the description of different solutions that can be found in the health industry is developed, especially those whose goal is health care at home; the main component of these solutions are body sensor networks. Finally, an analysis of the literature from the perspectives of functionalities, security implementation and semantic interoperability is made to have a better understanding of what has been done and which are probable research paths to be studied in the future.

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BSNCare+: A Robust IoT-Oriented Healthcare System with Non-Repudiation Transactions

Cited by 14 publications

References 16 publications

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

An Affordable Fast Early Warning System for Edge Computing in Assembly Line

Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare

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