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Strategy Analysis of Mobile Edge Computing Based on EC-ANN in Task Vehicle Cooperative Unloading

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Veröffentlicht in:Informatica vol. 48, no. 22 (Dec 2024), p. 179
1. Verfasser: Wang, Chenwei
Weitere Verfasser: Li, Xiating
Veröffentlicht:
Slovenian Society Informatika / Slovensko drustvo Informatika
Schlagworte:
Collaboration
Communication
Artificial neural networks
Transportation industry
Electric vehicles
Optimization
Roads & highways
Edge computing
Resource allocation
Mobile computing
Transportation planning
Intelligent transportation systems
Probability distribution
Energy consumption
Efficiency
Distributed processing
Performance enhancement
Maintenance costs
Energy costs
Experiments
Network reliability
Decision making
Neural networks
Searching
Network latency
Information processing
Vehicles
Linear programming
Algorithms
Logistics
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Abstract: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
Quelle:Advanced Technologies & Aerospace Database