CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System

TL;DR

CityGPT is a multi-agent framework that leverages large language models to analyze, visualize, and explain complex IoT spatiotemporal data, making it more accessible for general users.

Contribution

The paper introduces CityGPT, a novel multi-agent system that integrates LLMs for real-time IoT data analysis, visualization, and natural language explanation.

Findings

Effective analysis of real-world IoT data with high accuracy

Enhanced data interpretability for non-experts through natural language descriptions

Robust performance demonstrated on diverse IoT datasets

Abstract

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.