Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Abstract 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 ameli...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-06-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-022-00288-4 |
| _version_ | 1811257325728563200 |
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| author | Nweso Emmanuel Nwogbaga Rohaya Latip Lilly Suriani Affendey Amir Rizaan Abdul Rahiman |
| author_facet | Nweso Emmanuel Nwogbaga Rohaya Latip Lilly Suriani Affendey Amir Rizaan Abdul Rahiman |
| author_sort | Nweso Emmanuel Nwogbaga |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T17:55:34Z |
| format | Article |
| id | doaj.art-69f4358a78a94febb36be1a119ec7cdc |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-12T17:55:34Z |
| publishDate | 2022-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-69f4358a78a94febb36be1a119ec7cdc2022-12-22T03:22:22ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2022-06-0111111710.1186/s13677-022-00288-4Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selectionNweso Emmanuel Nwogbaga0Rohaya Latip1Lilly Suriani Affendey2Amir Rizaan Abdul Rahiman3Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra MalaysiaDepartment of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra MalaysiaDepartment of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra MalaysiaDepartment of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra MalaysiaAbstract 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.https://doi.org/10.1186/s13677-022-00288-4Computation offloadingMobile edge computingTask and resource schedulingAttribute reduction |
| spellingShingle | Nweso Emmanuel Nwogbaga Rohaya Latip Lilly Suriani Affendey Amir Rizaan Abdul Rahiman 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 Computation offloading Mobile edge computing Task and resource scheduling Attribute reduction |
| title | 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_short | 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 |
| topic | Computation offloading Mobile edge computing Task and resource scheduling Attribute reduction |
| url | https://doi.org/10.1186/s13677-022-00288-4 |
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