Resource-Aware Graph Neural Networks for IoT Edge Computing

Abstract

The proliferation of Internet of Things (IoT) devices has generated vast amounts of data, presenting significant challenges for centralized processing. Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing complex, interconnected data, particularly in applications like smart cities, industrial monitoring, and anomaly detection. However, the inherent computational demands of GNNs—specifically their reliance on matrix operations and deep architectures—often exceed the limited resources available at the IoT edge. This research proposes a novel framework for Resource-Aware Graph Neural Networks (RAGNNs) designed to address this disparity. The core idea is to integrate resource awareness directly into the GNN architecture and training process, leveraging techniques such as model pruning, quantization, and distributed computation to minimize the computational footprint while maintaining acceptable accuracy. This work outlines the architecture of RAGNNs, details the optimization strategies employed, and explores their effectiveness within simulated IoT edge computing environments. The primary goal is to enable the practical deployment of GNNs in resource-constrained IoT scenarios, unlocking their potential for real-time, localized data analysis.