IoT-Brain：为语义-空间传感器调度 grounding LLMs
IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling
AI总结：
本文提出IoT-Brain系统，通过引入空间轨迹图（STG）解决LLM在语义-空间传感器调度中的表示、推理和优化缺陷，提升任务成功率37.6%并显著降低资源消耗。
摘要：
由大规模传感器网络驱动的智能系统正从预定义监控转向意图驱动操作，揭示了语义到物理映射的关键缺口。尽管大型语言模型（LLMs）在语义理解方面表现出色，但现有以感知为中心的管道在回顾性操作中忽略了根本的决策问题：何时以及什么需要感知。本文将这种前瞻性决策形式化为语义-空间传感器调度（S3），并证明直接LLM规划不可靠，因为存在表示、推理和优化方面的固有差距。为弥补这些差距，我们引入了空间轨迹图（STG），这是一种受“验证后再提交”纪律约束的神经符号范式，将开放性规划转化为可验证的图优化问题。基于STG，我们实现了IoT-Brain系统，并构建了校园级基准TopoSense-Bench，包含5250个自然语言查询和2510个摄像头。评估显示，IoT-Brain在任务成功率方面比最强的搜索密集型方法提高了37.6%，运行速度几乎快两倍，使用提示词的数量减少了6.6倍。在现实世界部署中，它接近可靠性上限，同时减少了4.1倍的网络带宽，为LLM与物理世界交互提供了前所未有的可靠性和效率的基础框架。
英文摘要：
Intelligent systems powered by large-scale sensor networks are shifting from predefined monitoring to intent-driven operation, revealing a critical Semantic-to-Physical Mapping Gap. While large language models (LLMs) excel at semantic understanding, existing perception-centric pipelines operate retrospectively, overlooking the fundamental decision of what to sense and when. We formalize this proactive decision as Semantic-Spatial Sensor Scheduling (S3) and demonstrate that direct LLM planning is unreliable due to inherent gaps in representation, reasoning, and optimization. To bridge these gaps, we introduce the Spatial Trajectory Graph (STG), a neuro-symbolic paradigm governed by a verify-before-commit discipline that transforms open-ended planning into a verifiable graph optimization problem. Based on STG, we implement IoT-Brain, a concrete system embodiment, and construct TopoSense-Bench, a campus-scale benchmark with 5,250 natural-language queries across 2,510 cameras. Evaluations show that IoT-Brain boosts task success rate by 37.6% over the strongest search-intensive methods while running nearly 2 times faster and using 6.6 times fewer prompt tokens. In real-world deployment, it approaches the reliability upper bound while reducing 4.1 times network bandwidth, providing a foundational framework for LLMs to interact with the physical world with unprecedented reliability and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
