Yichi Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yichi Yang has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Ruibin Yan
College of Information and Cyber Security, People’s Public Security University of China, Beijing 102600, China
1 shared publication - Yijun Gu
College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China
1 shared publication
Latest Publications
- Open Access
- 12 September 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (18) , 10232
LTransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing
Vehicular edge computing (VEC) is essential in vehicle applications such as traffic control and in-vehicle services. In the task offloading process of VEC, predictive-mode transmission based on deep learning is constrained by limited computational resources. Furthermore, the accuracy of deep learning algorithms in VEC is compromised due to the lack of edge computing features in algorithms. To solve these problems, this paper proposes a task offloading optimization approach that enables edge servers to store deep learning models. Moreover, this paper proposes the LTransformer, a transformer-based framework that incorporates edge computing features. The framework consists of pre-training, an input module, an encoding–decoding module, and an output module. Compared with four sequential deep learning methods, namely a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), a Gated Recurrent Unit (GRU), and the Transformer, the LTransformer achieves the highest accuracy, reaching 80.1% on the real dataset. In addition, the LTransformer achieves 0.008 s when predicting a single trajectory, fully satisfying the fundamental requirements of real-time prediction and enabling task offloading optimization.
- 12 September 2023
- Published by MDPI AG in Applied Sciences
- Vol. 13 (18) , 10232
Open AccessLTransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing
Vehicular edge computing (VEC) is essential in vehicle applications such as traffic control and in-vehicle services. In the task offloading process of VEC, predictive-mode transmission based on deep learning is constrained by limited computational resources. Furthermore, the accuracy of deep learning algorithms in VEC is compromised due to the lack of edge computing features in algorithms. To solve these problems, this paper proposes a task offloading optimization approach that enables edge servers to store deep learning models. Moreover, this paper proposes the LTransformer, a transformer-based framework that incorporates edge computing features. The framework consists of pre-training, an input module, an encoding–decoding module, and an output module. Compared with four sequential deep learning methods, namely a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), a Gated Recurrent Unit (GRU), and the Transformer, the LTransformer achieves the highest accuracy, reaching 80.1% on the real dataset. In addition, the LTransformer achieves 0.008 s when predicting a single trajectory, fully satisfying the fundamental requirements of real-time prediction and enabling task offloading optimization.