Smart Healthcare

2018 ◽  
Vol 12 (4) ◽  
pp. 401-166 ◽  
Author(s):  
Hongxu Yin ◽  
Ayten Ozge Akmandor ◽  
Arsalan Mosenia ◽  
Niraj K. Jha ◽  
Jean-Baptiste Raclet ◽  
...  
Keyword(s):  
Smart Healthcare

Security in Smart Healthcare System: A Comprehensive Survey

2017 ◽  
Vol 7 (10) ◽  
pp. 39
Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha
Keyword(s):  
Secure Communication ◽  
Smart Cities ◽  
Healthcare Systems ◽  
Internet Security ◽  
Security Risk ◽  
System A ◽  
Smart Healthcare ◽  
Comprehensive Survey ◽  
Healthcare Monitoring ◽  
The Internet Of Things

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.

A Seven-Layered Model Architecture, Network Model, Protocol Stack, Security, Application, Issues and Challenges in Internet of Vehicle

2020 ◽  
Vol 14 ◽  
Author(s):  
Intyaz Alam ◽  
Sushil Kumar ◽  
Pankaj Kumar Kashyap
Keyword(s):  
Internet Of Things ◽  
Network Model ◽  
Transportation Systems ◽  
Vehicle Speed ◽  
Protocol Stack ◽  
Seamless Integration ◽  
Communication Architecture ◽  
Smart Transportation ◽  
Smart Healthcare ◽  
Essential Components

Background: Recently, Internet of Things (IoT) has brought various changes in the existing research field by including new areas such as smart transportation, smart home facilities, smart healthcare, etc. In smart transportation systems, vehicles contain different components to access information related to passengers, drivers, vehicle speed, and many more. This information can be accessed by connecting vehicles with Internet of Things leading to new fields of research known as Internet of Vehicles. The setup of Internet of Vehicle (IoV) consists of many sensors to establish a connection with several other sensors belonging to different environments by exploiting different technologies. The communication of the sensors faces a lot of challenging issues. Some of the critical challenges are to maintain security in information exchanges among the vehicles, inequality in sensors, quality of internet connection, and storage capacity. Objective: To overcome the challenging issues, we have designed a new framework consisting of seven-layered architecture, including the security layered, which provides seamless integration by communicating the devices present in the IoV environment. Further, a network model consisting of four components such as Cloud, Fog, Connection, and Clients has been designed. Finally, the protocol stack which describes the protocol used in each layer of the proposed seven-layered IoV architecture has been shown. Methods: In this proposed architecture, the representation and the functionalities of each layer and types of security have been defined. Case studies of this seven-layer IoV architecture have also been performed to illustrate the operation of each layer in real-time. The details of the network model including all the elements inside each component, have also been shown. Results: We have discussed some of the existing communication architecture and listed a few challenges and issues occurring in present scenarios. Considering these issues, which is presently occurring in the existing communication architecture. We have developed the seven-layered IoV architecture and the network model with four essential components known as the cloud, fog, connection, and clients. Conclusion: This proposed architecture provides a secure IoV environment and provides life safety. Hence, safety and security will help to reduce the cybercrimes occurring in the network and provides good coordination and communication of the vehicles in the network.

The Scoping Review of Smart Healthcare Applications in Medical Health (Preprint)

2020 ◽  
Author(s):  
Cheng Hang Wu ◽  
Ching Ju Chiu ◽  
Yen Ju Liou ◽  
Chun Ying Lee ◽  
Susan C. Hu
Keyword(s):  
Systematic Reviews ◽  
Mobile Health ◽  
Scoping Review ◽  
Search Strategy ◽  
Rapid Development ◽  
Related Research ◽  
Geographic Access ◽  
Smart Healthcare ◽  
Research Findings ◽  
Healthcare Interventions

BACKGROUND There is still no consensus on research terms for smart healthcare worldwide. The study conducted by Lewis 10 years ago showed extending geographic access was the major health purpose of health-related information communication technology (ICT), but today's situation may be different because of the rapid development of smart healthcare. Objective: The main aim of this study is to classify recent smart healthcare interventions. Therefore, this scoping review was conducted as a feasible tool for exploring this domain and summarizing related research findings. OBJECTIVE The main aim of this study is to classify recent smart healthcare interventions. Therefore, this scoping review was conducted as a feasible tool for exploring this domain and summarizing related research findings. METHODS The scoping review relies on the analysis of previous reviews of smart healthcare interventions assessed for their effectiveness in the framework of a systematic review and/or meta-analysis. The search strategy was based on the identification of smart healthcare interventions reported as the proposed keywords. In the analysis, the reviews published from January 2015 to December 2019 were included. RESULTS The number of publications for smart healthcare's systematic reviews has continued to grow in the past five years. The search strategy yielded 210 systematic reviews and/or meta-analyses addressed to target groups of interest. 68.5% of these publications used mobile health as a keyword. According to the classification by Lewis, 37.62% of the literature was applied to extend geographic access. According to the classification by the Joint Commission of Taiwan (JCT), 48.84% of smart healthcare was applied in clinical areas, and 60% of it was applied in outpatient medical services. CONCLUSIONS Smart healthcare interventions are being widely used in clinical settings and for disease management. The research of mobile health has received the most attention among smart healthcare interventions. The main purpose of mobile health was used to extend geographic access to increase medical accessibility in clinical areas. CLINICALTRIAL none

A fully homomorphic encryption based on magic number fragmentation and El‐Gamal encryption: Smart healthcare use case

2021 ◽  
Author(s):  
Mostefa Kara ◽  
Abdelkader Laouid ◽  
Mohammed Amine Yagoub ◽  
Reinhardt Euler ◽  
Saci Medileh ◽  
...  
Keyword(s):  
Homomorphic Encryption ◽  
Magic Number ◽  
Use Case ◽  
Healthcare Use ◽  
Fully Homomorphic Encryption ◽  
Smart Healthcare

scholarly journals Security of Things Intrusion Detection System for Smart Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1375
Author(s):  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Cresantus Biamba ◽  
Desire Ngabo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Web Security ◽  
Intrusion Detection Systems ◽  
High Detection Rate ◽  
Detection Systems ◽  
Smart Healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

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