Fault tolerant & priority basis task offloading and scheduling model for IoT logistics
The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824011712 |
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| author | Asif Umer Mushtaq Ali Ali Daud Lal Hussain Amal Bukhari Ali Imran Jehangiri |
| author_facet | Asif Umer Mushtaq Ali Ali Daud Lal Hussain Amal Bukhari Ali Imran Jehangiri |
| author_sort | Asif Umer |
| collection | DOAJ |
| description | The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments. |
| format | Article |
| id | doaj-art-5615ca5e3e4c49b7be5829349a1bbf3a |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-5615ca5e3e4c49b7be5829349a1bbf3a2025-01-09T06:13:23ZengElsevierAlexandria Engineering Journal1110-01682025-01-01110400419Fault tolerant & priority basis task offloading and scheduling model for IoT logisticsAsif Umer0Mushtaq Ali1Ali Daud2Lal Hussain3Amal Bukhari4Ali Imran Jehangiri5Department of Computer Science & Information Technology, Hazara University, Mansehra, KPK 21130, PakistanDepartment of Computer Science & Information Technology, Hazara University, Mansehra, KPK 21130, Pakistan; Corresponding authors.Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates; Corresponding authors.Department of Computer Science, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, AJK 13230, Pakistan; Department of Computer Science & IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, AJK 13100, Pakistan; Corresponding author at: Department of Computer Science, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, AJK 13230, Pakistan.Department of Information Systems and Technology, Collage of Computer Science and Engineering, University of Jeddah, Saudi ArabiaDepartment of Computer Science & Information Technology, Hazara University, Mansehra, KPK 21130, PakistanThe Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.http://www.sciencedirect.com/science/article/pii/S1110016824011712IoT task offloaderFog VM selectionEnergy consumptionIoT logisticsRe-clusteringTask scheduling |
| spellingShingle | Asif Umer Mushtaq Ali Ali Daud Lal Hussain Amal Bukhari Ali Imran Jehangiri Fault tolerant & priority basis task offloading and scheduling model for IoT logistics Alexandria Engineering Journal IoT task offloader Fog VM selection Energy consumption IoT logistics Re-clustering Task scheduling |
| title | Fault tolerant & priority basis task offloading and scheduling model for IoT logistics |
| title_full | Fault tolerant & priority basis task offloading and scheduling model for IoT logistics |
| title_fullStr | Fault tolerant & priority basis task offloading and scheduling model for IoT logistics |
| title_full_unstemmed | Fault tolerant & priority basis task offloading and scheduling model for IoT logistics |
| title_short | Fault tolerant & priority basis task offloading and scheduling model for IoT logistics |
| title_sort | fault tolerant amp priority basis task offloading and scheduling model for iot logistics |
| topic | IoT task offloader Fog VM selection Energy consumption IoT logistics Re-clustering Task scheduling |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824011712 |
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