This REU led to the publication of "Edge-Enabled Scalable Routing via Graph Neural Network Pruning and Metaheuristic Optimization" at the 2025 ACM/IEEE Symposium on Edge Computing.

Abstract

Edge intelligence enables distributed decision-making by executing complex optimization tasks directly on resource-constrained edge nodes, minimizing reliance on centralized cloud infrastructure. This work introduces a hybrid edge-intelligent routing framework that combines Graph Neural Network (GNN)-based graph pruning with metaheuristic optimization to achieve scalable and low-latency routing at the network edge. The GNN functions as a lightweight inference module that identifies and removes low-utility edges from a sensing network, substantially reducing communication and computational overhead. On the resulting sparse subgraph, a Guided Local Search (GLS) algorithm performs localized route refinement to produce near-optimal paths without central coordination. This integrated GNN-GLS design allows simultaneous graph learning and optimization on edge hardware while retaining solution quality comparable to centralized solvers. Experimental results on synthetic datasets demonstrate a 13.2% runtime improvement on 1000-node graphs with only a 0.7% increase in route length, confirming the feasibility of learning-driven pruning and decentralized metaheuristic search for scalable edge deployment.