The spatiotemporal data generated by massive sensors in the Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, and stability) in real-time analysis and decision making for different IoT applications. The complexity of IoT data prevents the common people from gaining a deeper understanding of it. Agentized systems help address the lack of data insight for the common people. We propose a generic framework, namely CityGPT, to facilitate the learning and analysis of IoT time series with an end-to-end paradigm. CityGPT employs three agents to accomplish the spatiotemporal analysis of IoT data. The requirement agent facilitates user inputs based on natural language. Then, the analysis tasks are decomposed into temporal and spatial analysis processes, completed by corresponding data analysis agents (temporal and spatial agents). Finally, the spatiotemporal fusion agent visualizes the system's analysis results by receiving analysis results from data analysis agents and invoking sub-visualization agents, and can provide corresponding textual descriptions based on user demands. To increase the insight for common people using our framework, we have agnentized the framework, facilitated by a large language model (LLM), to increase the data comprehensibility. Our evaluation results on real-world data with different time dependencies show that the CityGPT framework can guarantee robust performance in IoT computing.

翻译：物联网（IoT）中海量传感器产生的时空数据具有高度动态性、异构性、大规模且时间依赖性强等特点。这为不同物联网应用的实时分析与决策带来了巨大挑战（如准确性、可靠性与稳定性）。物联网数据的复杂性阻碍了普通用户对其深入理解。智能体化系统有助于解决普通用户缺乏数据洞察力的问题。我们提出一个通用框架——CityGPT，通过端到端范式促进物联网时间序列的学习与分析。CityGPT采用三个智能体完成物联网数据的时空分析：需求智能体基于自然语言处理用户输入；随后分析任务被分解为时空分析流程，由相应的数据分析智能体（时间与空间智能体）完成；最终，时空融合智能体通过接收数据分析智能体的结果并调用子可视化智能体，将系统分析结果可视化，并能根据用户需求提供相应的文本描述。为提升普通用户使用本框架的洞察力，我们在大型语言模型（LLM）的支持下实现了框架的智能体化，以增强数据的可理解性。我们在具有不同时间依赖性的真实数据上的评估结果表明，CityGPT框架能够确保物联网计算中的鲁棒性能。