BAIoT-EMS: Consortium network for small-medium enterprises management system with blockchain and augmented intelligence of things

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2025-02-01 DOI:10.1016/j.engappai.2024.109838

Abdullah Ayub Khan , Jing Yang , Asif Ali Laghari , Abdullah M. Baqasah , Roobaea Alroobaea , Chin Soon Ku , Roohallah Alizadehsani , U. Rajendra Acharya , Lip Yee Por

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引用次数: 0

Abstract

The rapid adoption of Augmented Intelligence of Things (AIoT) in enterprise management (EM) presents significant challenges in securely managing and exchanging information. This study introduces a blockchain-based platform, BAIoT-EMS, designed to enhance security and efficiency in AIoT-enabled EM systems. The platform leverages a consortium network and InterPlanetary File Storage (IPFS) for secure storage and transaction management, supported by smart contracts to automate and safeguard processes like device registration. A novel multi-proof-of-work consensus mechanism is implemented to analyze, validate, and verify AIoT transactions while minimizing resource consumption. Simulation results demonstrate a 63.51% improvement in performance and an 11.75% reduction in computational power usage, highlighting the effectiveness of the proposed framework.

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来源期刊

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.