Context-Aware Hybrid Routing in Bluetooth Mesh Networks Using Multi-Model Machine Learning and AODV Fallback

Abstract

Bluetooth-based mesh networks offer a promising infrastructure for offline communication in emergency and resource constrained scenarios. However, traditional routing strategies such as Ad hoc On-Demand Distance Vector (AODV) often degrade under congestion and dynamic topological changes. This study proposes a hybrid intelligent routing framework that augments AODV with supervised machine learning to improve next-hop selection under varied network constraints. The framework integrates four predictive models: a delivery success classifier, a TTL regressor, a delay regressor, and a forwarder suitability classifier, into a unified scoring mechanism that dynamically ranks neighbors during multi-hop message transmission. A simulation environment with stationary node deployments was developed, incorporating buffer constraints and device heterogeneity to evaluate three strategies: baseline AODV, a partial hybrid ML model (ABC), and the full hybrid ML model (ABCD). Across ten scenarios, the Hybrid ABCD model achieves approximately 99.97 percent packet delivery under these controlled conditions, significantly outperforming both the baseline and intermediate approaches. The results demonstrate that lightweight, explainable machine learning models can enhance routing reliability and adaptability in Bluetooth mesh networks, particularly in infrastructure-less environments where delivery success is prioritized over latency constraints.