Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction
With the rapid deployment of edge base stations and the widespread application of 5G technology, Mobile Edge Computing (MEC)has gradually transitioned from a theoretical concept to practical implementation, playing a key role in emerging human-machine interactions and innovative mobile applications....
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3025 |
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| author | Qian Zeng Xiaobo Li Yixuan Chen Minghao Yang Xingbang Liu Yuetian Liu Shiwei Xiu |
| author_facet | Qian Zeng Xiaobo Li Yixuan Chen Minghao Yang Xingbang Liu Yuetian Liu Shiwei Xiu |
| author_sort | Qian Zeng |
| collection | DOAJ |
| description | With the rapid deployment of edge base stations and the widespread application of 5G technology, Mobile Edge Computing (MEC)has gradually transitioned from a theoretical concept to practical implementation, playing a key role in emerging human-machine interactions and innovative mobile applications. In the MEC environment, efficiently allocating services, effectively utilizing edge device resources, and ensuring timely service responses have become critical research topics. Existing studies often treat MEC service allocation as an offline strategy, where the real-time location of users is used as input, and static optimization is applied. However, this approach overlooks dynamic factors such as user mobility. To address this limitation, this paper constructs a model based on constraints, optimization objectives, and server connection methods, determines experimental parameters and evaluation metrics, and sets up an experimental framework. We propose an Edge Location Prediction Model (ELPM) suitable for the MEC scenario, which integrates Spatial-Temporal Graph Neural Networks and attention mechanisms. By leveraging attention parameters, ELPM acquires spatio-temporal adaptive weights, enabling accurate location predictions. We also design an improved service allocation strategy, MESDA, based on the Gray Wolf Optimization (GWO) algorithm. MESDA dynamically adjusts its exploration and exploitation components, and introduces a random factor to enhance the algorithm’s ability to determine the direction during later stages. To validate the effectiveness of the proposed methods, we conduct multiple controlled experiments focusing on both location prediction models and service allocation algorithms. The results show that, compared to the baseline methods, our approach achieves improvements of 2.56%, 5.29%, and 2.16% in terms of the average user connection to edge servers, average service deployment cost, and average service allocation execution time, respectively, demonstrating the superiority and feasibility of the proposed methods. |
| format | Article |
| id | doaj-art-2c20782dba624621bc800e262e251e9b |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2c20782dba624621bc800e262e251e9b2025-08-20T03:47:57ZengMDPI AGSensors1424-82202025-05-012510302510.3390/s25103025Device-Driven Service Allocation in Mobile Edge Computing with Location PredictionQian Zeng0Xiaobo Li1Yixuan Chen2Minghao Yang3Xingbang Liu4Yuetian Liu5Shiwei Xiu6State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, ChinaResearch Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, ChinaWith the rapid deployment of edge base stations and the widespread application of 5G technology, Mobile Edge Computing (MEC)has gradually transitioned from a theoretical concept to practical implementation, playing a key role in emerging human-machine interactions and innovative mobile applications. In the MEC environment, efficiently allocating services, effectively utilizing edge device resources, and ensuring timely service responses have become critical research topics. Existing studies often treat MEC service allocation as an offline strategy, where the real-time location of users is used as input, and static optimization is applied. However, this approach overlooks dynamic factors such as user mobility. To address this limitation, this paper constructs a model based on constraints, optimization objectives, and server connection methods, determines experimental parameters and evaluation metrics, and sets up an experimental framework. We propose an Edge Location Prediction Model (ELPM) suitable for the MEC scenario, which integrates Spatial-Temporal Graph Neural Networks and attention mechanisms. By leveraging attention parameters, ELPM acquires spatio-temporal adaptive weights, enabling accurate location predictions. We also design an improved service allocation strategy, MESDA, based on the Gray Wolf Optimization (GWO) algorithm. MESDA dynamically adjusts its exploration and exploitation components, and introduces a random factor to enhance the algorithm’s ability to determine the direction during later stages. To validate the effectiveness of the proposed methods, we conduct multiple controlled experiments focusing on both location prediction models and service allocation algorithms. The results show that, compared to the baseline methods, our approach achieves improvements of 2.56%, 5.29%, and 2.16% in terms of the average user connection to edge servers, average service deployment cost, and average service allocation execution time, respectively, demonstrating the superiority and feasibility of the proposed methods.https://www.mdpi.com/1424-8220/25/10/3025mobile edge computinglocation predictionservice dynamic allocationservice deployment cost |
| spellingShingle | Qian Zeng Xiaobo Li Yixuan Chen Minghao Yang Xingbang Liu Yuetian Liu Shiwei Xiu Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction Sensors mobile edge computing location prediction service dynamic allocation service deployment cost |
| title | Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction |
| title_full | Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction |
| title_fullStr | Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction |
| title_full_unstemmed | Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction |
| title_short | Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction |
| title_sort | device driven service allocation in mobile edge computing with location prediction |
| topic | mobile edge computing location prediction service dynamic allocation service deployment cost |
| url | https://www.mdpi.com/1424-8220/25/10/3025 |
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