ORCID
Ayat N. Kadhum: https://orcid.org/0009-0007-1537-6528
Article Type
Original Study
Abstract
The integration of IoT and blockchain enhances security, trust, and data integrity but is hindered by security attacks, scalability, and high latency. In this work, a more efficient method of consensus using Directed Acyclic Graph (DAG)-based Fast Probabilistic Consensus (FPC) and Edwards-Curve Digital Signature Algorithm (EdDSA) is proposed to yield better security, efficiency, and decentralization. By eliminating mining, resource use is optimized, and consensus is hastened. Experimental results show a high throughput of 7228.05 Transactions Per Second (TPS), rapid consensus formation in just 7.62 rounds on average, and high adversary resilience, with the system successfully mitigating 89% of adversarial attacks. IOTA 2.0 introduces a DAG-based, leaderless architecture designed for secure, scalable, and feeless transactions in IoT networks. It achieves high throughput and strong adversary resistance. Compared to existing blockchain-based IoT solutions like Nano, Avalanche, and Hash graph, IOTA 2.0 stands out by removing transaction fees and avoiding energy-intensive mining, making it ideal for resource-limited IoT environments. The results confirm that DAG-based consensus highly enhances the scalability, reliability, and security of blockchain networks in IoT applications. The proposed approach is a promising solution to decentralized, real-time decision systems, making them more efficient and secure in IoT-based blockchain networks.
Keywords
Internet of things, Fast probabilistic consensus, Directed acyclic graph, Distributed ledger technologies, Consensus mechanism, Edwards-curve digital signature algorithm
How to Cite This Article
Kadhum, Ayat N. and Al-Salih, Ahmed M.
(2025)
"Enhancing IoT Decentralization with IOTA 2.0: A DAG-Based Fast Probabilistic Consensus Framework,"
Journal of Intelligent Informatics, Networking, and Cybersecurity: Vol. 1:
Iss.
1, Article 3.
Available at:
https://jiinc.uobabylon.edu.iq/journal/vol1/iss1/3
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.