Towards scalable and secure blockchain in Internet of Things: a preference-driven committee member auction consensus approach

Mathur, Akshaya, Barati, Masoud, Aujla, Gagangeet Singh and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2024. Towards scalable and secure blockchain in Internet of Things: a preference-driven committee member auction consensus approach. Distributed Ledger Technologies: Research and Practice 10.1145/3700149

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Abstract

Blockchain technology is acclaimed for eliminating the need for a central authority while ensuring stability, security, and immutability. However, its integration into Internet of Things (IoT) environments is hampered by the limited computational resources of IoT devices. Consensus algorithms, vital for blockchain safety and efficiency, often require substantial computational power and face challenges related to security, scalability, and resource demands. To address these critical issues, we propose a novel model that significantly enhances the security and performance of blockchain in IoT environments. Our model introduces three key innovations: (1) a bidirectional-linked blockchain system that strengthens security against long-range attacks by exploiting dual reference points for block validation; (2) the integration of user preferences into the Committee Member Auction (CMA) consensus algorithm, optimizing miner selection to balance resource efficiency with security; and (3) a comprehensive performance and frequency analysis that demonstrates the system’s resilience against double-spend, long-range, and eclipse attacks. The proposed model not only reduces block validation delays but also enhances overall system performance, as evidenced by simulations comparing its effectiveness with existing CMA algorithms. These advancements have the potential to significantly impact the deployment of blockchain in resource-constrained IoT environments, offering a more secure and efficient solution.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Computer Science & Informatics
ISSN:	2769-6480
Funders:	EPSRC
Date of First Compliant Deposit:	16 October 2024
Date of Acceptance:	20 September 2024
Last Modified:	25 Nov 2024 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/172969

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