A Q-Learning and Blockchain Framework for Secure Dynamic Bandwidth Allocation in Heterogeneous IoT
DOI:
https://doi.org/10.4108/eetiot.10464Keywords:
Heterogeneous IoT, Dynamic Bandwidth Allocation, Machine Learning, Blockchain, Network SecurityAbstract
The rapid expansion of the Internet of Things (IoT) has intensified the challenge of achieving dynamic bandwidth allocation while maintaining security across heterogeneous devices and communication protocols. Conventional static allocation schemes lack adaptability, while existing learning-based or blockchain-based approaches typically optimize performance or trust in isolation. To address this gap, this paper proposes a hybrid framework that integrates Q-learning–based adaptive bandwidth allocation with a lightweight, permissioned blockchain-based trust mechanism. The framework is evaluated through MATLAB-based simulations involving 100 heterogeneous IoT devices under dynamic traffic conditions and adversarial behavior. Performance is compared against multiple baselines, including static allocation, learning-only and blockchain-only schemes, classical scheduling algorithms (WFQ and DRR), and a deep reinforcement learning approach (DQN). The results reveal clear trade-offs among bandwidth utilization, fairness, energy consumption, and security. Static and classical schedulers provide predictable fairness but remain vulnerable to malicious activity. Learning-only and deep reinforcement learning approaches improve adaptability but lack intrinsic trust awareness, while blockchain-only enforcement enhances security at the expense of responsiveness. By coupling adaptive decision-making with trust validation, the proposed hybrid framework achieves a balanced operating point, offering stable bandwidth utilization, improved energy efficiency, and robust attack resilience under noisy and uncertain conditions. These findings highlight the importance of aligning learning mechanisms with trust-aware constraints for secure and scalable bandwidth management in heterogeneous IoT networks.
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Copyright (c) 2026 Ahmed Saeed Obied, Ahmad Shahidan Abdullah, Hind Mowafaq Taha, Sanaa Abd Tarish
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