Strategy Analysis of Mobile Edge Computing Based on EC-ANN in Task Vehicle Cooperative Unloading
שמור ב:
| הוצא לאור ב: | Informatica vol. 48, no. 22 (Dec 2024), p. 179 |
|---|---|
| מחבר ראשי: | |
| מחברים אחרים: | |
| יצא לאור: |
Slovenian Society Informatika / Slovensko drustvo Informatika
|
| נושאים: | |
| גישה מקוונת: | Citation/Abstract Full Text Full Text - PDF |
| תגים: |
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
|
| Resumen: | To improve the performance and reliability of task vehicle collaborative unloading, the study adopted Monte Carlo tree search and deep neural networks to optimize resource allocation of task vehicles in collaborative unloading. Secondly, through multi-mode collaboration, the relay unloading task of roadside units was carried out. Meanwhile, the service range of vehicle collaborative unloading was expanded based on the calculation results, achieving the full utilization of idle computing resources. These experiments confirmed that compared to random search and greedy search, the proposed network model scheme improved service latency performance by 58.3% and 47.1%, respectively. The proposed multi-mode joint unloading mechanism had significant performance improvement under the collaborative unloading mechanism from adjacent vehicles to vehicles. It offloaded tasks to service vehicles outside the communication range, reducing completion latency by approximately 33.6%. Therefore, this task vehicle collaboration unloading method improved the performance of mobile edge computing systems, reduced computing and storage costs, and lowered the energy consumption and maintenance costs of task vehicles. This research method can improve the efficiency and safety of task vehicle collaboration unloading, providing technical support for the optimization of intelligent transportation systems. |
|---|---|
| ISSN: | 0350-5596 1854-3871 |
| DOI: | 10.31449/inf.v48i22.5973 |
| Fuente: | Advanced Technologies & Aerospace Database |