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This paper presents a bi-objective optimization framework for resource scheduling in dense IoT deployments using 5G network slicing, employing a tailored Non-dominated Sorting Genetic Algorithm II (NSGA-II). The approach balances operational costs and quality-of-service (QoS) across heterogeneous network slices, demonstrating significant performance improvements over existing methods in extensive simulations. The framework is validated with real-world traffic, confirming its robustness and scalability for p

Author

reghaouatdouaa

Language

EN