Lightweight and delay-aware resource management scheme in smart grid IoT networks
Abstract Mobile edge computing has gained significant attention in smart grid IoT, as it is seen as a promising technique for supporting computation-heavy services. Efficient online task processing is crucial in this context, as it ensures real-time decision-making and system responsiveness, which a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-03-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-025-02434-3 |
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| _version_ | 1850039808444858368 |
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| author | Danni Liu Shengda Wang Xiaofu Sun Chunyan An Weijia Su Jiakang Liu |
| author_facet | Danni Liu Shengda Wang Xiaofu Sun Chunyan An Weijia Su Jiakang Liu |
| author_sort | Danni Liu |
| collection | DOAJ |
| description | Abstract Mobile edge computing has gained significant attention in smart grid IoT, as it is seen as a promising technique for supporting computation-heavy services. Efficient online task processing is crucial in this context, as it ensures real-time decision-making and system responsiveness, which are vital for maintaining grid stability and optimizing resource management. However, the challenge of meeting online service requirements within the constraints of limited resources and strict task processing delay persists. To address this, we propose an online delay-aware online mobile computation offloading scheme consisting of four crucial algorithms, which firstly classify users into heterogeneous networks and then design the online resource allocation methods on the macro base station (MBS) and small base stations (SBSs), respectively, and finally design the updating strategy of the control parameters to ensure the load balancing among bases. Simulation results demonstrate that for the case of 50 mobile users, the proposed algorithm reduces task execution delay by 42.2%, 44.4%, and 62.9% relative to the three baseline algorithms, which allow the tasks to be executed only at the MBS, SBS or to be executed locally. |
| format | Article |
| id | doaj-art-668bdb0896fa4d61bc3bcca0a35b82d3 |
| institution | DOAJ |
| issn | 1687-1499 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj-art-668bdb0896fa4d61bc3bcca0a35b82d32025-08-20T02:56:13ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992025-03-012025112110.1186/s13638-025-02434-3Lightweight and delay-aware resource management scheme in smart grid IoT networksDanni Liu0Shengda Wang1Xiaofu Sun2Chunyan An3Weijia Su4Jiakang Liu5JiLin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation LtdJiLin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation LtdJiLin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation LtdState Grid Smart Grid Research Institute Co., Ltd., Electric Power Intelligent Sensing Technology and Application of State Grid Corporation Joint LaboratoryJiLin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation LtdState Grid Smart Grid Research Institute Co., Ltd., Electric Power Intelligent Sensing Technology and Application of State Grid Corporation Joint LaboratoryAbstract Mobile edge computing has gained significant attention in smart grid IoT, as it is seen as a promising technique for supporting computation-heavy services. Efficient online task processing is crucial in this context, as it ensures real-time decision-making and system responsiveness, which are vital for maintaining grid stability and optimizing resource management. However, the challenge of meeting online service requirements within the constraints of limited resources and strict task processing delay persists. To address this, we propose an online delay-aware online mobile computation offloading scheme consisting of four crucial algorithms, which firstly classify users into heterogeneous networks and then design the online resource allocation methods on the macro base station (MBS) and small base stations (SBSs), respectively, and finally design the updating strategy of the control parameters to ensure the load balancing among bases. Simulation results demonstrate that for the case of 50 mobile users, the proposed algorithm reduces task execution delay by 42.2%, 44.4%, and 62.9% relative to the three baseline algorithms, which allow the tasks to be executed only at the MBS, SBS or to be executed locally.https://doi.org/10.1186/s13638-025-02434-3Smart grid IoT networksControl parameterCompetitive ratio |
| spellingShingle | Danni Liu Shengda Wang Xiaofu Sun Chunyan An Weijia Su Jiakang Liu Lightweight and delay-aware resource management scheme in smart grid IoT networks EURASIP Journal on Wireless Communications and Networking Smart grid IoT networks Control parameter Competitive ratio |
| title | Lightweight and delay-aware resource management scheme in smart grid IoT networks |
| title_full | Lightweight and delay-aware resource management scheme in smart grid IoT networks |
| title_fullStr | Lightweight and delay-aware resource management scheme in smart grid IoT networks |
| title_full_unstemmed | Lightweight and delay-aware resource management scheme in smart grid IoT networks |
| title_short | Lightweight and delay-aware resource management scheme in smart grid IoT networks |
| title_sort | lightweight and delay aware resource management scheme in smart grid iot networks |
| topic | Smart grid IoT networks Control parameter Competitive ratio |
| url | https://doi.org/10.1186/s13638-025-02434-3 |
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