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.

通过智能化系统，结合大规模感知器在物联网中产生的时空数据，我们提出了一种名为 CityGPT 的通用框架，以实现对物联网时间序列数据的学习和分析，从而增加公众对数据的洞察力，并在物联网计算中保证了稳健性能。