Traffic-Aware Joint Clustering and Routing in Energy-Harvesting Industrial Internet of Things

Abstract

The energy-harvesting industrial IoT networks exhibit intertwined quasi-periodic dynamics in traffic patterns and energy budgeting. However, conventional myopic approaches optimize only instantaneous states, resulting in a mismatch between traffic demand and energy supply. To address this, we present a traffic-aware joint clustering and routing (TJCR) approach. Firstly, we develop a lightweight forecaster that utilizes learnable basis decomposition to predict future traffic and energy profiles. Then, we establish a collaborative optimization scheme that jointly adapts clustering and routing, iteratively refining the clusters based on routing-induced feedback. Simulations demonstrate that TJCR improves network lifetime over representative baselines while maintaining high throughput.