A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization
Abstract To address the challenges of low efficiency and high redundancy in massive data acquisition within the Power Internet of Things (PIoT), existing systems suffer from redundant acquisition and resource waste due to insufficient identification of overlapping regions, while traditional scheduli...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02882-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850125892311842816 |
|---|---|
| author | Jindong Cui Yuqing Wang Zengchen Zhu Ruotong Li |
| author_facet | Jindong Cui Yuqing Wang Zengchen Zhu Ruotong Li |
| author_sort | Jindong Cui |
| collection | DOAJ |
| description | Abstract To address the challenges of low efficiency and high redundancy in massive data acquisition within the Power Internet of Things (PIoT), existing systems suffer from redundant acquisition and resource waste due to insufficient identification of overlapping regions, while traditional scheduling mechanisms struggle to balance task priorities with dynamic scenario requirements. This paper proposes a data acquisition task decomposition and scheduling method optimized through overlapping data analysis. Initially, hash functions are employed to rapidly identify overlapping regions in target data clusters, with a “hyperlink anchoring” mechanism implemented to eliminate redundant data acquisition. Subsequently, a task decomposition model centered on total cost minimization is formulated, prioritizing the allocation of tasks with maximum overlapping regions to optimize resource distribution strategies. Finally, a multi-dimensional dynamic priority scheduling model is developed, integrating task criticality and temporal characteristics to dynamically adjust execution sequences, ensuring high-value tasks achieve priority completion. Case study results demonstrate that the proposed method improves task efficiency by 18.7% compared to baseline methods, while maintaining operational effectiveness under high-load scenarios. |
| format | Article |
| id | doaj-art-461a1fa238b94cd0a4ad7d23564c2b6e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-461a1fa238b94cd0a4ad7d23564c2b6e2025-08-20T02:34:02ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-02882-3A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimizationJindong Cui0Yuqing Wang1Zengchen Zhu2Ruotong Li3School of Economics and Management, Northeast Electric Power UniversitySchool of Economics and Management, Northeast Electric Power UniversitySchool of Economics and Management, Northeast Electric Power UniversitySchool of Economics and Management, Northeast Electric Power UniversityAbstract To address the challenges of low efficiency and high redundancy in massive data acquisition within the Power Internet of Things (PIoT), existing systems suffer from redundant acquisition and resource waste due to insufficient identification of overlapping regions, while traditional scheduling mechanisms struggle to balance task priorities with dynamic scenario requirements. This paper proposes a data acquisition task decomposition and scheduling method optimized through overlapping data analysis. Initially, hash functions are employed to rapidly identify overlapping regions in target data clusters, with a “hyperlink anchoring” mechanism implemented to eliminate redundant data acquisition. Subsequently, a task decomposition model centered on total cost minimization is formulated, prioritizing the allocation of tasks with maximum overlapping regions to optimize resource distribution strategies. Finally, a multi-dimensional dynamic priority scheduling model is developed, integrating task criticality and temporal characteristics to dynamically adjust execution sequences, ensuring high-value tasks achieve priority completion. Case study results demonstrate that the proposed method improves task efficiency by 18.7% compared to baseline methods, while maintaining operational effectiveness under high-load scenarios.https://doi.org/10.1038/s41598-025-02882-3Power Internet of ThingsData acquisitionIdentification of overlapping regionsTask decompositionPriority settingMulti-task scheduling |
| spellingShingle | Jindong Cui Yuqing Wang Zengchen Zhu Ruotong Li A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization Scientific Reports Power Internet of Things Data acquisition Identification of overlapping regions Task decomposition Priority setting Multi-task scheduling |
| title | A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization |
| title_full | A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization |
| title_fullStr | A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization |
| title_full_unstemmed | A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization |
| title_short | A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization |
| title_sort | task decomposition and scheduling model for power iot data acquisition with overlapping data efficiency optimization |
| topic | Power Internet of Things Data acquisition Identification of overlapping regions Task decomposition Priority setting Multi-task scheduling |
| url | https://doi.org/10.1038/s41598-025-02882-3 |
| work_keys_str_mv | AT jindongcui ataskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization AT yuqingwang ataskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization AT zengchenzhu ataskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization AT ruotongli ataskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization AT jindongcui taskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization AT yuqingwang taskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization AT zengchenzhu taskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization AT ruotongli taskdecompositionandschedulingmodelforpoweriotdataacquisitionwithoverlappingdataefficiencyoptimization |