Edge Computing-Enabled Secure Forecasting Nationwide Industry PM2.5 with LLM in the Heterogeneous Network

Abstract

The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. PM2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry PM2.5, which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio-Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry PM2.5 in China. In particular, We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model.