Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones
Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/23 |
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| author | Sofiane Zaidi Mohamed Amine Attalah Lazhar Khamer Carlos T. Calafate |
| author_facet | Sofiane Zaidi Mohamed Amine Attalah Lazhar Khamer Carlos T. Calafate |
| author_sort | Sofiane Zaidi |
| collection | DOAJ |
| description | Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the IoD. The proposed solution applies the PSO for task offloading from unmanned aerial vehicles (UAVs) to fog base stations (FBSs) in order to optimize the offloading delay (transmission delay and fog computing delay) and to guarantee higher storage and processing capacity. The performance of PSO BS-Fog was evaluated through simulations conducted in the MATLAB environment and compared against PSO UAV-Fog and PSO UAV-Edge IoD technologies. Experimental results demonstrate that PSO BS-Fog reduces task offloading delay by up to 88% compared to PSO UAV-Fog and by up to 97% compared to PSO UAV-Edge. |
| format | Article |
| id | doaj-art-7928e394c716476495b03ffb936254b2 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-7928e394c716476495b03ffb936254b22025-01-24T13:29:41ZengMDPI AGDrones2504-446X2024-12-01912310.3390/drones9010023Task Offloading Optimization Using PSO in Fog Computing for the Internet of DronesSofiane Zaidi0Mohamed Amine Attalah1Lazhar Khamer2Carlos T. Calafate3Department of Mathematics and Computer Science, Research Laboratory on Computer Science’s Complex Systems (RELA(CS)2), University of Oum El Bouaghi, Oum El Bouaghi 04000, AlgeriaDepartment of Electronics, University Center of Tipaza, Tipaza 42000, AlgeriaDepartment of Mathematics and Computer Science, Research Laboratory on Computer Science’s Complex Systems (RELA(CS)2), University of Oum El Bouaghi, Oum El Bouaghi 04000, AlgeriaDepartment of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, SpainRecently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the IoD. The proposed solution applies the PSO for task offloading from unmanned aerial vehicles (UAVs) to fog base stations (FBSs) in order to optimize the offloading delay (transmission delay and fog computing delay) and to guarantee higher storage and processing capacity. The performance of PSO BS-Fog was evaluated through simulations conducted in the MATLAB environment and compared against PSO UAV-Fog and PSO UAV-Edge IoD technologies. Experimental results demonstrate that PSO BS-Fog reduces task offloading delay by up to 88% compared to PSO UAV-Fog and by up to 97% compared to PSO UAV-Edge.https://www.mdpi.com/2504-446X/9/1/23Internet of Dronesfog computing networksparticle swarm optimizationtask offloading in IoDunmanned aerial vehicles |
| spellingShingle | Sofiane Zaidi Mohamed Amine Attalah Lazhar Khamer Carlos T. Calafate Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones Drones Internet of Drones fog computing networks particle swarm optimization task offloading in IoD unmanned aerial vehicles |
| title | Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones |
| title_full | Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones |
| title_fullStr | Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones |
| title_full_unstemmed | Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones |
| title_short | Task Offloading Optimization Using PSO in Fog Computing for the Internet of Drones |
| title_sort | task offloading optimization using pso in fog computing for the internet of drones |
| topic | Internet of Drones fog computing networks particle swarm optimization task offloading in IoD unmanned aerial vehicles |
| url | https://www.mdpi.com/2504-446X/9/1/23 |
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