Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network
The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required m...
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my.um.eprints.344402022-06-14T07:58:11Z http://eprints.um.edu.my/34440/ Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network Lakhan, Abdullah Mastoi, Qurat-ul-ain Dootio, Mazhar Ali Alqahtani, Fehaid Alzahrani, Ibrahim R. Baothman, Fatmah Shah, Syed Yaseen Shah, Syed Aziz Anjum, Nadeem Abbasi, Qammer Hussain Khokhar, Muhammad Saddam QA75 Electronic computers. Computer science The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan. MDPI 2021-08 Article PeerReviewed Lakhan, Abdullah and Mastoi, Qurat-ul-ain and Dootio, Mazhar Ali and Alqahtani, Fehaid and Alzahrani, Ibrahim R. and Baothman, Fatmah and Shah, Syed Yaseen and Shah, Syed Aziz and Anjum, Nadeem and Abbasi, Qammer Hussain and Khokhar, Muhammad Saddam (2021) Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network. Electronics, 10 (16). ISSN 2079-9292, DOI https://doi.org/10.3390/electronics10161974 <https://doi.org/10.3390/electronics10161974>. 10.3390/electronics10161974 |
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QA75 Electronic computers. Computer science Lakhan, Abdullah Mastoi, Qurat-ul-ain Dootio, Mazhar Ali Alqahtani, Fehaid Alzahrani, Ibrahim R. Baothman, Fatmah Shah, Syed Yaseen Shah, Syed Aziz Anjum, Nadeem Abbasi, Qammer Hussain Khokhar, Muhammad Saddam Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network |
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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan. |
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Lakhan, Abdullah Mastoi, Qurat-ul-ain Dootio, Mazhar Ali Alqahtani, Fehaid Alzahrani, Ibrahim R. Baothman, Fatmah Shah, Syed Yaseen Shah, Syed Aziz Anjum, Nadeem Abbasi, Qammer Hussain Khokhar, Muhammad Saddam |
author_facet |
Lakhan, Abdullah Mastoi, Qurat-ul-ain Dootio, Mazhar Ali Alqahtani, Fehaid Alzahrani, Ibrahim R. Baothman, Fatmah Shah, Syed Yaseen Shah, Syed Aziz Anjum, Nadeem Abbasi, Qammer Hussain Khokhar, Muhammad Saddam |
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Lakhan, Abdullah |
title |
Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network |
title_short |
Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network |
title_full |
Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network |
title_fullStr |
Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network |
title_full_unstemmed |
Hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network |
title_sort |
hybrid workload enabled and secure healthcare monitoring sensing framework in distributed fog-cloud network |
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MDPI |
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2021 |
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http://eprints.um.edu.my/34440/ |
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1736834293112504320 |
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13.160551 |