Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems
Abstract The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud compu...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81055-0 |
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| author | Ruchika Bhakhar Rajender Singh Chhillar |
| author_facet | Ruchika Bhakhar Rajender Singh Chhillar |
| author_sort | Ruchika Bhakhar |
| collection | DOAJ |
| description | Abstract The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes. |
| format | Article |
| id | doaj-art-dd41e3a6876c4b5881bace232563d28d |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dd41e3a6876c4b5881bace232563d28d2025-08-20T02:20:45ZengNature PortfolioScientific Reports2045-23222024-12-0114113710.1038/s41598-024-81055-0Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systemsRuchika Bhakhar0Rajender Singh Chhillar1Department of computer science and applications, Maharshi Dayanand UniversityDepartment of computer science and applications, Maharshi Dayanand UniversityAbstract The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.https://doi.org/10.1038/s41598-024-81055-0Internet of ThingsDynamic schedulingMulti-criteria optimizationFog computingCloud computingSmart home |
| spellingShingle | Ruchika Bhakhar Rajender Singh Chhillar Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems Scientific Reports Internet of Things Dynamic scheduling Multi-criteria optimization Fog computing Cloud computing Smart home |
| title | Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems |
| title_full | Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems |
| title_fullStr | Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems |
| title_full_unstemmed | Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems |
| title_short | Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems |
| title_sort | dynamic multi criteria scheduling algorithm for smart home tasks in fog cloud iot systems |
| topic | Internet of Things Dynamic scheduling Multi-criteria optimization Fog computing Cloud computing Smart home |
| url | https://doi.org/10.1038/s41598-024-81055-0 |
| work_keys_str_mv | AT ruchikabhakhar dynamicmulticriteriaschedulingalgorithmforsmarthometasksinfogcloudiotsystems AT rajendersinghchhillar dynamicmulticriteriaschedulingalgorithmforsmarthometasksinfogcloudiotsystems |