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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.00053 |
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| _version_ | 1866913712935272448 |
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| author | Ahmed, Tasnim Choudhury, Salimur |
| author_facet | Ahmed, Tasnim Choudhury, Salimur |
| contents | The adoption of unmanned aerial vehicles to monitor critical infrastructure is gaining momentum in various industrial domains. Organizational imperatives drive this progression to minimize expenses, accelerate processes, and mitigate hazards faced by inspection personnel. However, traditional infrastructure monitoring systems face critical bottlenecks-5G networks lack the latency and reliability for large-scale drone coordination, while manual inspections remain costly and slow. We propose a 6G-enabled drone swarm system that integrates ultra-reliable, low-latency communications, edge AI, and semantic communication to automate inspections. By adopting LLMs for structured output and report generation, our framework is hypothesized to reduce inspection costs and improve fault detection speed compared to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00053 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI and Semantic Communication for Infrastructure Monitoring in 6G-Driven Drone Swarms Ahmed, Tasnim Choudhury, Salimur Robotics Artificial Intelligence The adoption of unmanned aerial vehicles to monitor critical infrastructure is gaining momentum in various industrial domains. Organizational imperatives drive this progression to minimize expenses, accelerate processes, and mitigate hazards faced by inspection personnel. However, traditional infrastructure monitoring systems face critical bottlenecks-5G networks lack the latency and reliability for large-scale drone coordination, while manual inspections remain costly and slow. We propose a 6G-enabled drone swarm system that integrates ultra-reliable, low-latency communications, edge AI, and semantic communication to automate inspections. By adopting LLMs for structured output and report generation, our framework is hypothesized to reduce inspection costs and improve fault detection speed compared to existing methods. |
| title | AI and Semantic Communication for Infrastructure Monitoring in 6G-Driven Drone Swarms |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2503.00053 |