{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T07:59:31Z","timestamp":1761638371945,"version":"build-2065373602"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"name":"Key R&D and Promotion Special Program of Henan Province","award":["222102110392"],"award-info":[{"award-number":["222102110392"]}]},{"DOI":"10.13039\/501100013066","name":"Key Scientific Research Project of Colleges and Universities in Henan Province","doi-asserted-by":"publisher","award":["23A460024"],"award-info":[{"award-number":["23A460024"]}],"id":[{"id":"10.13039\/501100013066","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013066","name":"Key Scientific Research Project of Colleges and Universities in Henan Province","doi-asserted-by":"publisher","award":["24B460030"],"award-info":[{"award-number":["24B460030"]}],"id":[{"id":"10.13039\/501100013066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2026,1,15]]},"abstract":"<jats:p>With the rapid development of vehicular networks, the amount of data generated by vehicles has increased sharply. However, how to efficiently and intelligently realize task offloading for mechanical vehicles in vehicular networks is still a hot and difficult topic in current research. This study first analyzes the dynamic nature of the network environment, the limitation of computing resources and the diversity of task types. Then, it proposes a deep learning model based on variational autoencoders-long short-term memory (VAE-LSTM), to predict and decide on task offloading strategies in networked vehicles. The VAE-LSTM model combines the generation capability of VAE with the time series processing capability of LSTM. Such a design can accurately capture the changing trend of network state and task requirements. To verify the performance of the model, this work makes comparison experiments by comparing the VAE-LSTM model with other deep learning models. The experimental results show that the VAE-LSTM model is superior to other models in terms of prediction accuracy and stability, and can significantly reduce the mean absolute error of task offloading, and improve the resource utilization and task execution efficiency of the edge computing system. It meets the real-time and reliability requirements of the vehicular network system.<\/jats:p>","DOI":"10.1142\/s0218126625504055","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T04:59:21Z","timestamp":1751950761000},"source":"Crossref","is-referenced-by-count":0,"title":["A Deep Learning-Based Task Offloading Method for Mechanical Vehicles in Edge Networks"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9635-4908","authenticated-orcid":false,"given":"Guangbo","family":"Xiang","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450064, P. R. China"},{"name":"Zhengzhou Industrial Design Center of Intelligent Equipment, Zhengzhou 450064, P. R. 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