Work

QoS-Aware Routing in Software-Defined Networking-Enabled Internet of Things via LSTM Learning-Based Optimization Algorithm

Year: 2026

Type: conference-paper

Abstract: Ensuring Quality of Service (QoS) in Software Defined Networking (SDN)-enabled Internet of Things (IoT) networks is challenging due to dynamic traffic patterns and time-varying link conditions. This paper proposes a predictive QoSaware routing framework that integrates Long Short-Term Memory (LSTM) learning with a Modified Black-Winged Kite Optimizer (M-BWKO). The LSTM model forecasts future path delay and reliability using historical telemetry, while the optimizer selects routing configurations that minimize delay violations, reliability loss, and link congestion under capacity constraints. Simulation results show that the proposed method improves end-to-end delay, reliability, and network goodput compared with conventional and learning-only routing approaches, particularly under high traffic loads.

Source: Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)

DOI: 10.1109/eucnc/6gsummit68295.2026.11577408

Authors: Seyed Salar Sefati, Octavian Fratu, Atanas Vlahov, Simona Halunga

Institution: Universitatea Națională de Știință și Tehnologie Politehnica București

Language: English

Topic: Software-Defined Networks and 5G

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funder: European Commission