Strategy Analysis of Mobile Edge Computing Based on EC-ANN in Task Vehicle Cooperative Unloading
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| Published in: | Informatica vol. 48, no. 22 (Dec 2024), p. 179 |
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| Published: |
Slovenian Society Informatika / Slovensko drustvo Informatika
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| Online Access: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3157227869 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0350-5596 | ||
| 022 | |a 1854-3871 | ||
| 024 | 7 | |a 10.31449/inf.v48i22.5973 |2 doi | |
| 035 | |a 3157227869 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 179436 |2 nlm | ||
| 100 | 1 | |a Wang, Chenwei | |
| 245 | 1 | |a Strategy Analysis of Mobile Edge Computing Based on EC-ANN in Task Vehicle Cooperative Unloading | |
| 260 | |b Slovenian Society Informatika / Slovensko drustvo Informatika |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Collaboration | ||
| 653 | |a Communication | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Transportation industry | ||
| 653 | |a Electric vehicles | ||
| 653 | |a Optimization | ||
| 653 | |a Roads & highways | ||
| 653 | |a Edge computing | ||
| 653 | |a Resource allocation | ||
| 653 | |a Mobile computing | ||
| 653 | |a Transportation planning | ||
| 653 | |a Intelligent transportation systems | ||
| 653 | |a Probability distribution | ||
| 653 | |a Energy consumption | ||
| 653 | |a Efficiency | ||
| 653 | |a Distributed processing | ||
| 653 | |a Performance enhancement | ||
| 653 | |a Maintenance costs | ||
| 653 | |a Energy costs | ||
| 653 | |a Experiments | ||
| 653 | |a Network reliability | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 653 | |a Searching | ||
| 653 | |a Network latency | ||
| 653 | |a Information processing | ||
| 653 | |a Vehicles | ||
| 653 | |a Linear programming | ||
| 653 | |a Algorithms | ||
| 653 | |a Logistics | ||
| 700 | 1 | |a Li, Xiating | |
| 773 | 0 | |t Informatica |g vol. 48, no. 22 (Dec 2024), p. 179 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3157227869/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3157227869/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3157227869/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |