Av Machine Learning-Based Lightweight Consensus Framework for Blockchain-Enabled IoT Systems
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Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
The integration of blockchain with the Internet of Things (IoT) offers a promising ap
proach to address the limitations of centralized IoT architectures. However, existing
blockchain consensus mechanisms are often unsuitable for resource-constrained IoT
devices and dynamic network conditions due to high computational demands or re
liance on monetary-based participant selection.. In this work, we propose Dynamic&
Periodic Proof of Evolutionary Model (DP-POEM), a lightweight, machine learning
based consensus mechanism tailored for blockchain-based IoT systems. DP-POEM
selects a group of block producers for defined periods using supervised learning, re
ducing redundant computation while ensuring security through randomized block
productionwithinthegroup. Itincorporatesbothstaticanddynamicallychangingde
vice features, such as battery level and CPU usage, to select capable nodes efficiently,
and implements fair participation mechanisms to balance network involvement over
time. Theoretical analysis and experimental evaluation demonstrate that DP-POEM
achieves high scalability, low latency, enhanced security, and improved applicability
compared to traditional and state-of-the-art consensus protocols in dynamic IoT en
vironments.
Description
Supervised by
Dr. Md. SakhawatHossen,
Professor,
Mr. Faisal Hussain,
Assistant Professor,
Department of Computer Science and Engineering (CSE)
Islamic University of Technology (IUT)
Board Bazar, Gazipur, Bangladesh
This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Computer Science and Engineering, 2025
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