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The research aims to optimize resource management in 5G networks for Internet of Things (IoT) devices using a bi-objective framework.

March 4, 2026TelecomOpen Access

Bi-Objective Optimization for Scalable Resource Scheduling in Dense IoT Deployments via 5G Network Slicing Using NSGA-II

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The research aims to optimize resource management in 5G networks for Internet of Things (IoT) devices using a bi-objective framework.
Developed a bi-objective optimization problem addressing operational costs and quality-of-service requirements.
Employed a tailored Non-dominated Sorting Genetic Algorithm II (NSGA-II) with specific constraints.
Conducted simulations for scenarios with up to 5000 devices to evaluate the proposed approach.
Achieved 8–13% hypervolume improvements over multi-objective deep reinforcement learning (DRL) approaches.
Realized 15–28% improvements over single-objective DRL baselines and 22–41% over heuristic methods.
Demonstrated algorithm robustness with only 7% performance degradation when validated with real-world traffic traces.

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This analysis demonstrates improved resource scheduling in dense IoT deployments via 5G network slicing, implying enhanced connectivity.

Nucci et al. (2026) studied this question.

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