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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the...

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Main Authors: Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan
Format: Article
Published: Springer Nature 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100490/
https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-022-00288-4
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spelling my.upm.eprints.1004902023-11-23T08:50:37Z http://psasir.upm.edu.my/id/eprint/100490/ Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection Nwogbaga, Nweso Emmanuel Latip, Rohaya Affendey, Lilly Suriani Abdul Rahiman, Amir Rizaan The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others. Springer Nature 2022-06-04 Article PeerReviewed Nwogbaga, Nweso Emmanuel and Latip, Rohaya and Affendey, Lilly Suriani and Abdul Rahiman, Amir Rizaan (2022) Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection. Journal of Cloud Computing: Advances, Systems and Applications, 11. art. no. 15. pp. 1-17. ISSN 2192-113X https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-022-00288-4 10.1186/s13677-022-00288-4
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.
format Article
author Nwogbaga, Nweso Emmanuel
Latip, Rohaya
Affendey, Lilly Suriani
Abdul Rahiman, Amir Rizaan
spellingShingle Nwogbaga, Nweso Emmanuel
Latip, Rohaya
Affendey, Lilly Suriani
Abdul Rahiman, Amir Rizaan
Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
author_facet Nwogbaga, Nweso Emmanuel
Latip, Rohaya
Affendey, Lilly Suriani
Abdul Rahiman, Amir Rizaan
author_sort Nwogbaga, Nweso Emmanuel
title Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
title_short Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
title_full Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
title_fullStr Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
title_full_unstemmed Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
title_sort attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
publisher Springer Nature
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100490/
https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-022-00288-4
_version_ 1783879919682453504
score 13.211869

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