Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909716371734528 |
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| author | Xu, Yunting Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Rajan, Deepu Yu, Liang Zhou, Haibo Jamalipour, Abbas Wang, Xianbin |
| author_facet | Xu, Yunting Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Rajan, Deepu Yu, Liang Zhou, Haibo Jamalipour, Abbas Wang, Xianbin |
| contents | Large vision models (LVMs) have emerged as a foundational paradigm in visual intelligence, achieving state-of-the-art performance across diverse visual tasks. Recent advances in LVMs have facilitated their integration into Internet of Things (IoT) scenarios, offering superior generalization and adaptability for vision-assisted network optimization. In this paper, we first investigate the functionalities and core architectures of LVMs, highlighting their capabilities across classification, segmentation, generation, and multimodal visual processing. We then explore a variety of LVM applications in wireless communications, covering representative tasks across the physical layer, network layer, and application layer. Furthermore, given the substantial model size of LVMs and the challenges of model retraining in wireless domains, we propose a progressive fine-tuning framework that incrementally adapts pretrained LVMs for joint optimization of multiple IoT tasks. A case study in low-altitude economy networks (LAENets) demonstrates the effectiveness of the proposed framework over conventional CNNs in joint beamforming and positioning tasks for Internet of drones, underscoring a promising direction for integrating LVMs into intelligent wireless systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications Xu, Yunting Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Rajan, Deepu Yu, Liang Zhou, Haibo Jamalipour, Abbas Wang, Xianbin Networking and Internet Architecture Large vision models (LVMs) have emerged as a foundational paradigm in visual intelligence, achieving state-of-the-art performance across diverse visual tasks. Recent advances in LVMs have facilitated their integration into Internet of Things (IoT) scenarios, offering superior generalization and adaptability for vision-assisted network optimization. In this paper, we first investigate the functionalities and core architectures of LVMs, highlighting their capabilities across classification, segmentation, generation, and multimodal visual processing. We then explore a variety of LVM applications in wireless communications, covering representative tasks across the physical layer, network layer, and application layer. Furthermore, given the substantial model size of LVMs and the challenges of model retraining in wireless domains, we propose a progressive fine-tuning framework that incrementally adapts pretrained LVMs for joint optimization of multiple IoT tasks. A case study in low-altitude economy networks (LAENets) demonstrates the effectiveness of the proposed framework over conventional CNNs in joint beamforming and positioning tasks for Internet of drones, underscoring a promising direction for integrating LVMs into intelligent wireless systems. |
| title | Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.00583 |