Imperial College London
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ProfessorArnabMajumdar

Faculty of Engineering, Department of Civil and Environmental Engineering

Professor of Transport Risk and Safety
 
 
 
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Contact

 

+44 (0)20 7594 6037a.majumdar

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

604Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lakhan:2022:10.3390/s22062379,
author = {Lakhan, A and Sodhro, AH and Majumdar, A and Khuwuthyakorn, P and Thinnukool, O},
doi = {10.3390/s22062379},
journal = {Sensors (Basel, Switzerland)},
title = {A lightweight secure adaptive approach for internet-of-medical-things healthcare applications in edge-cloud-based networks},
url = {http://dx.doi.org/10.3390/s22062379},
volume = {22},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications' execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.
AU  - Lakhan,A
AU  - Sodhro,AH
AU  - Majumdar,A
AU  - Khuwuthyakorn,P
AU  - Thinnukool,O
DO  - 10.3390/s22062379
PY  - 2022///
SN  - 1424-8220
TI  - A lightweight secure adaptive approach for internet-of-medical-things healthcare applications in edge-cloud-based networks
T2  - Sensors (Basel, Switzerland)
UR  - http://dx.doi.org/10.3390/s22062379
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35336549
UR  - http://hdl.handle.net/10044/1/96300
VL  - 22
ER  -
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