Private Blockchain-Based AI-Envisioned Home Monitoring Framework in IoMT-Enabled COVID-19 Environment

Bera, Basudeb; Mitra, Ankush; Das, Ashok Kumar; Puthal, Deepak

doi:10.1109/mce.2021.3137104

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…During the period of COVID–19, all over the world, human's faced severe health issues. Thus, Bera et al [ 21 ] say that in COVID – 19 periods, IoMT enabled a framework to monitor from home and is based upon blockchain technology. At the same time, Fog computing, as well as blockchain technology, plays a vital role in COVID -19 pandemic in monitoring the patient from home and is more secure in IoMT.…”

Section: Related Workmentioning

confidence: 99%

An intelligent blockchain strategy for decentralised healthcare framework

Goel

Neduncheliyan

2023

Peer-to-Peer Netw. Appl.

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Nowadays, securely sharing medical data is one of the significant concerns in blockchain technology. The existing blockchain approaches have faced high time consumption, low confidentiality, and high memory usage for transferring the file in a secure way because of attack harmfulness and large unstructured records. It has ended in security threat, so the integrity of the user data has been lost. Hence, a novel hybrid Deep Belief-based Diffie Hellman (DBDH) security framework was presented to protect medical data from malicious events. Incorporating a deep belief neural system continuously monitors the system and identifies the attacks. Initially, the IoMT dataset was collected from the standard site and imported into the system. Moreover, hash 1 was calculated for the original data and stored in the cloud server for verification. Then, the original data was encrypted with a private key for data hiding. The incorporation of homomorphic property helps to calculate hash 2 for encrypted data. Finally, in the verification module, both hash values are verified. In addition, cryptanalysis was performed by launching an attack to validate the performance of the designed model. Moreover, the estimated outcomes of the presented model were compared with existing approaches to determine the improvement score.

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Section: Related Workmentioning

confidence: 99%

An intelligent blockchain strategy for decentralised healthcare framework

Goel

Neduncheliyan

2023

Peer-to-Peer Netw. Appl.

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“…This is done particularly by performing the five standard procedures listed below: initiating the transaction, broadcasting and verifying it, creating and validating the new blocks using a consensus method, and lastly updating the blockchain ledger. These functions are implemented on top of several other security techniques such as cryptography, hashing, and Peer-to-Peer (P2P) networking [13]. Moreover, when compared with centralized servers, the blockchain enabled network provide a trusted and authentic execution setup to ensure permissible access to the system and data.…”

Section: A Motivation Of Blockchain and Deep Learningmentioning

confidence: 99%

Deep-Learning-Based Blockchain for Secure Zero Touch Networks

Kumar

Aloqaily

et al. 2023

IEEE Commun. Mag.

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The recent technological advancements in wireless communication systems and Internet of Things (IoT) has accelerated the development of Zero Touch Networks (ZTNs). ZTNs provide self-monitoring, self-configuring, and automated servicelevel policies that cannot be fulfilled by the traditional network Management and Orchestration (MANO) approaches. Despite the hype, the majority of data exchange between participating entities occurs over insecure public channels, which present a number of possible security risks and attacks. Toward this end, we first analyze the attack surface on IoT-enabled ZTNs and the inherent architectural flaws for such threats. After an overview of attack surface, this article presents a new deep learning and blockchain-assisted case study for secure data sharing in ZTNs. Specifically, first, we design a novel Variational AutoEncoder (VAE) and Attention-based Gated Recurrent Units (AGRU)based Intrusion Detection System (IDS) for ZTNs. Second, a novel authentication protocol that combines blockchain, Smart Contracts (SC), Elliptic Curve Cryptography (ECC), and Proofof-Authority (PoA) consensus mechanism is developed to improve secure data sharing in ZTNs. The extensive experimental results show the effectiveness of the proposed approach. Lastly, this work discusses critical issues, opportunities, and open research directions to solve these challenges.

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“…The proposed work in [ 103 ] uses blockchain technology and fog computing to build a secure patient monitoring system. Authors have also used the IoMT in their system.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

Krishna

Kumar

Kushwaha

2023

Wireless Pers Commun

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Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients’ health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients’ vitals and storing them securely have risen more than ever. Analyzing the stored patients’ data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients’ data, and processing/analyzing the stored patients’ data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.

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Private Blockchain-Based AI-Envisioned Home Monitoring Framework in IoMT-Enabled COVID-19 Environment

Cited by 17 publications

References 13 publications

An intelligent blockchain strategy for decentralised healthcare framework

An intelligent blockchain strategy for decentralised healthcare framework

Deep-Learning-Based Blockchain for Secure Zero Touch Networks

A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

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