IoMT, Edge Computing, Blockchain, and Deep Learning for Next Generation Healthcare Systems (Preprint)

2021 ◽  
Author(s):  
Mohammad (Behdad) Jamshidi ◽  
Tarek Frikha ◽  
Asal Sabet ◽  
Omar Cheikhrouhou ◽  
Habib Hamam
Keyword(s):  
Intelligent Systems ◽  
Personal Information ◽  
Healthcare Systems ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Medical Documentation ◽  
Next Generation ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Flexible Infrastructure

UNSTRUCTURED Processing medical data, diagnosing diseases, determining the best possible medical centers or physicians, and recommending the more effective remedies or drugs in the earliest time are the most important challenges to deploy intelligent systems for healthcare purposes. Hence, utilization of the Internet of Medical Things (IoMT) with Edge Computing (EC) technology will result in a strong network to aggregate the healthcare data more reliably and solve the aforementioned challenges. However, the administration of the millions of individuals with a wide variety of physical or mental disorders is another challenge associated with the use of such Artificial Intelligence-based platforms, especially when it comes to a large number of insurance conditions and companies. Furthermore, although the EC-based platforms can increase the security of the data, there are still vulnerable to face some cyber-attacks. Thus, the privacy of sensitive personal information of patients should be considered. Blockchain is a suitable option to overcome the problems associated with medical documentation and administration of patient’s affairs using smart contracts. An EC-based platform based on blockchain to improve the weaknesses of conventional smart healthcare systems is rendered in this research. The proposed platform takes the advantage of both EC and blockchain in the terms of speed, security, accuracy, and bandwidth. It should be noted that this method could be utilized as a flexible infrastructure for the next generation healthcare systems using any kind of crypto network like Bitcoin, Ethereum, Cardano, etc.

Opportunistic Edge Computing Architecture for Smart Healthcare Systems

2021 ◽  
pp. 289-306
Author(s):  
Nivethitha V. ◽  
Aghila G.
Keyword(s):  
Cloud Computing ◽  
Healthcare Systems ◽  
Edge Computing ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Network Resource ◽  
Computing Architecture ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Network Response Time

Some of the largest global industries that is driving smart city environments are anywhere and anytime health monitoring applications. Smart healthcare systems need to be more preventive and responsive as they deal with sensitive data. Even though cloud computing provides solutions to the smart healthcare applications, the major challenge imposed on cloud computing is how could the centralized traditional cloud computing handle voluminous data. The existing models may encounter problems related to network resource utilization, overheads in network response time, and communication latency. As a solution to these problems, edge-oriented computing has emerged as a new computing paradigm through localized computing. Edge computing expands the compute, storage, and networking capabilities to the edge of the network which will respond to the above-mentioned issues. Based on cloud computing and edge computing, in this chapter an opportunistic edge computing architecture is introduced for smart provisioning of healthcare data.

scholarly journals A Secure and Lightweight Three-Factor-Based Authentication Scheme for Smart Healthcare Systems

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7136
Author(s):  
Jihyeon Ryu ◽  
Dongwoo Kang ◽  
Hakjun Lee ◽  
Hyoungshick Kim ◽  
Dongho Won
Keyword(s):  
User Authentication ◽  
Healthcare Systems ◽  
Authentication Scheme ◽  
Sensitive Information ◽  
Symmetric Encryption ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Verification Tool ◽  
User Authentication Scheme ◽  
Three Factor

Internet of Things (IoT) technology has recently been integrated with various healthcare devices to monitor patients’ health status and share it with their healthcare practitioners. Since healthcare data often contain personal and sensitive information, healthcare systems must provide a secure user authentication scheme. Recently, Adavoudi-Jolfaei et al. and Sharma and Kalra proposed a lightweight protocol using hash function encryption only for user authentication on wireless sensor systems. In this paper, we found some weaknesses in target schemes. We propose a novel three-factor lightweight user authentication scheme that addresses these weaknesses and verifies the security of the proposed scheme using a formal verification tool called ProVerif. In addition, our proposed scheme outperforms other proposed symmetric encryption-based schemes or elliptic curve-based schemes.

scholarly journals Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3619 ◽  
Author(s):  
Gordana Gardašević ◽  
Konstantinos Katzis ◽  
Dragana Bajić ◽  
Lazar Berbakov
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Secure Communication ◽  
Communication Technologies ◽  
Healthcare Systems ◽  
Healthcare Applications ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Communication Links

Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

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.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Smishing Detection: Using Artificial Intelligence

2021 ◽  
Vol 9 (8) ◽  
pp. 2218-2224
Author(s):  
Samyak Sadanand Shravasti
Keyword(s):  
Artificial Intelligence ◽  
Short Message Service ◽  
Personal Information ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Sensitive Information ◽  
Short Message ◽  
Proposed Model ◽  
Message Service ◽  
A Company

Abstract: Phishing occurs when people's personal information is stolen via email, phone, or text communications. In Smishing Short Message Service (SMS) is used for cyber-attacks, Smishing is a type of theft of sensitive information. People are more likely to give personal information such as account details and passwords when they receive SMS messages. This data could be used to steal money or personal information from a person or a company. As a result, Smishing is a critical issue to consider. The proposed model uses an Artificial Intelligence to detect smishing. Analysing a SMS and successfully detecting Smishing is possible. Finally, we evaluate and analyse our proposed model to show its efficacy. Keywords: Phishing, Smishing, Artificial Intelligence, LSTM, RNN

Sign in / Sign up

Export Citation Format

Share Document