Enhancing Wireless Networks for IoT with Large Vision Models: Foundations and Applications

2025-08-01

AI Summary

To address the weak generalization capability and poor task adaptability of vision-assisted wireless optimization in Low-Altitude Economic Networks (LAENets), this paper proposes a Large Vision Model (LVM)-integrated framework tailored for IoT wireless systems. Methodologically, we design a progressive multi-task fine-tuning mechanism enabling cross-layer co-optimization—spanning the physical layer (joint beamforming and localization), network layer, and application layer—while unifying classification, segmentation, generation, and multimodal understanding capabilities. Our key contribution lies in overcoming the limitations of conventional CNNs by enabling continual adaptation and lightweight deployment of LVMs under resource-constrained wireless conditions. Experiments in UAV-enabled IoT scenarios demonstrate that our approach significantly outperforms baseline CNNs in both joint optimization performance and cross-scenario generalization, thereby validating the technical feasibility and practical advantages of leveraging LVMs for intelligent wireless systems.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Integrating large vision models into IoT for network optimization
• Adapting pretrained LVMs for multiple IoT tasks efficiently
• Improving joint beamforming and positioning in drone networks

Innovation

• Integrating LVMs for IoT network optimization
• Progressive fine-tuning for multi-task adaptation
• Joint beamforming and positioning in drone networks