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Publication
Hybrid B5G-DTN architecture with federated learning for contextual communication offloading
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Advisor(s)
Abstract(s)
In dense urban environments and large-scale events, Internet infrastructure often becomes overloaded due to high communication demand. Many of these communications are local and short-lived, exchanged between users in close proximity but still relying on global infrastructure, leading to unnecessary network stress. In this context, delay-tolerant networks (DTNs) offer an alternative by enabling device-to-device (D2D) communication without requiring constant connectivity. However, DTNs face significant challenges in routing due to unpredictable node mobility and intermittent contacts, making reliable delivery difficult. Considering these challenges, this paper presents a hybrid Beyond 5G (B5G) DTN architecture to provide private context-aware routing in dense scenarios. In this proposal, dynamic contextual notifications are shared among relevant local nodes, combining federated learning (FL) and edge artificial intelligence (AI) to estimate the optimal relay paths based on variables such as mobility patterns and contact history. To keep the local FL models updated with the evolving context, edge nodes, integrated as part of the B5G architecture, act as coordinating entities for model aggregation and redistribution. The proposed architecture has been implemented and evaluated in simulation testbeds, studying its performance and sensibility to the node density in a realistic scenario. In high-density scenarios, the architecture outperforms state-of-the-art routing schemes, achieving an average delivery probability of 77%, with limited latency and overhead, demonstrating relevant technical viability.
Description
Keywords
Beyond 5G Delay-tolerant networks Federated learning Communication offloading Edge AI
Pedagogical Context
Citation
JESÚS-AZABAL, M. ; ZHENG, M. ; SOARES, V.N.G.J. (2025) - Hybrid B5G-DTN Architecture with Federated Learning for Contextual Communication Offloading. Future Internet. Vol. 17:9, 392. DOI: 10.3390/ fi17090392
Publisher
MDPI AG