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Related Experiment Video

Updated: Jan 12, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Enhancing secure IoT data sharing through dynamic Q-learning and blockchain at the edge.

Mustafa Bayat¹, Mohammad Ali Jabraeil Jamali², Mahdi Abbasi^3,4,5

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.

Keywords:

Blockchain Data distribution Efficiency Internet of things Network edge Scalability

Related Experiment Videos

Last Updated: Jan 12, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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.