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Enhancing secure IoT data sharing through dynamic Q-learning and blockchain at the edge
Mustafa Bayat1, Mohammad Ali Jabraeil Jamali2, Mahdi Abbasi3,4,5
1Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran.
Scientific Reports
|November 7, 2025
Summary
This study introduces Blockchain-based Dynamic Edge Q-learning (BDEQ) for secure Industrial Internet of Things (IIoT) data sharing. BDEQ enhances efficiency and resilience by dynamically selecting nodes, outperforming traditional methods.
Area of Science:
- Computer Science
- Electrical Engineering
- Industrial Engineering
Background:
- Industrial Internet of Things (IIoT) faces challenges in secure and efficient data sharing due to static node selection and centralized architectures.
- Traditional systems exhibit high latency, single points of failure, and vulnerability to cyberattacks, hindering dynamic adaptation.
Purpose of the Study:
- To propose a novel framework, Blockchain-based Dynamic Edge Q-learning (BDEQ), for real-time, trust-aware proxy node selection in IIoT.
- To enhance data sharing security, efficiency, and resilience in dynamic IIoT environments.
Main Methods:
- Integration of blockchain smart contracts and deep Q-learning for intelligent, adaptive proxy node selection.
- Development of a reinforcement learning agent that dynamically assesses nodes based on performance, resources, and trust metrics.
Main Results:
- BDEQ demonstrated a 35% reduction in data access latency and a 28% increase in throughput in a simulated gas-industry IIoT setting.
- The framework showed enhanced resilience against security attacks compared to baseline approaches.
Conclusions:
- BDEQ offers a decentralized, adaptive, and secure solution for data sharing in next-generation IIoT applications.
- The proposed method addresses key limitations of static and centralized systems, improving overall system performance and security.
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