Secure blockchain assisted Internet of Medical Things architecture for data fusion enabled cancer workflow
In today’s digital healthcare landscape, numerous clinical institutions collaborate to enhance healthcare quality in a ubiquitous fog and cloud environment. Data fusion plays a vital role in healthcare collaboration, enabling the integration of diverse healthcare sources. The primary advantage is th...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2023
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28433/2/0071712022024115016705.pdf http://eprints.utem.edu.my/id/eprint/28433/ https://www.sciencedirect.com/science/article/pii/S2542660523002512 https://doi.org/10.1016/j.iot.2023.100928 |
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| Summary: | In today’s digital healthcare landscape, numerous clinical institutions collaborate to enhance healthcare quality in a ubiquitous fog and cloud environment. Data fusion plays a vital role in healthcare collaboration, enabling the integration of diverse healthcare sources. The primary advantage is the improvement of healthcare treatments and the availability of services throughout the network. However, despite these benefits, there is room for improvement in addressing various security issues regarding collaboration among clinical healthcare institutions to meet data fusion requirements. The primary challenge lies in processing lung cancer workflow
data fusion on heterogeneous nodes while ensuring security in fog cloud networks. As a result, security emerges as a critical issue in the digital healthcare system operating within this ubiquitous environment. We present the secure Blockchain Internet of Medical Things (BIoMT) architecture for lung cancer workflow data fusion processing in fog cloud networks. The BIoMT architecture introduces the Blockchain Data Fusion Secure (BDFS) algorithm framework, which
consists of task scheduling and blockchain validation schemes. The study aims to minimize the makespan of the lung workflow tasks based on security and deadline constraints in fog and cloud networks. We consider security at an advanced level, where runtime ransomware attacks are
also identified in fog and cloud networks. Simulation results demonstrate that BDFS outperforms all existing BIoMT architectures regarding workflow processing while adhering to the specified constraints. Overall, the BDFS algorithm presented in the BIoMT architecture provides an
efficient and secure solution for lung cancer workflow data fusion in fog cloud networks, contributing to the advancement of digital healthcare systems in a ubiquitous environment. |
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