Department of Computer Science, Sindh Madressatul Islam University, Karachi, 74000, Pakistan.
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Sci Rep. 2023 Jan 30;13(1):1656. doi: 10.1038/s41598-023-28707-9.
Due to digitalization, small and medium-sized enterprises (SMEs) have significantly enhanced their efficiency and productivity in the past few years. The process to automate SME transaction execution is getting highly multifaceted as the number of stakeholders of SMEs is connecting, accessing, exchanging, adding, and changing the transactional executions. The balanced lifecycle of SMEs requires partnership exchanges, financial management, manufacturing, and productivity stabilities, along with privacy and security. Interoperability platform issue is another critical challenging aspect while designing and managing a secure distributed Peer-to-Peer industrial development environment for SMEs. However, till now, it is hard to maintain operations of SMEs' integrity, transparency, reliability, provenance, availability, and trustworthiness between two different enterprises due to the current nature of centralized server-based infrastructure. This paper bridges these problems and proposes a novel and secure framework with a standardized process hierarchy/lifecycle for distributed SMEs using collaborative techniques of blockchain, the internet of things (IoT), and artificial intelligence (AI) with machine learning (ML). A blockchain with IoT-enabled permissionless network structure is designed called "B-SMEs" that provides solutions to cross-chain platforms. In this, B-SMEs address the lightweight stakeholder authentication problems as well. For that purpose, three different chain codes are deployed. It handles participating SMEs' registration, day-to-day information management and exchange between nodes, and analysis of partnership exchange-related transaction details before being preserved on the blockchain immutable storage. Whereas AI-enabled ML-based artificial neural networks are utilized, the aim is to handle and optimize day-to-day numbers of SME transactions; so that the proposed B-SMEs consume fewer resources in terms of computational power, network bandwidth, and preservation-related issues during the complete process of SMEs service deliverance. The simulation results present highlight the benefits of B-SMEs, increases the rate of ledger management and optimization while exchanging information between different chains, which is up to 17.3%, and reduces the consumption of the system's computational resources down to 9.13%. Thus, only 14.11% and 7.9% of B-SME's transactions use network bandwidth and storage capabilities compared to the current mechanism of SMEs, respectively.
由于数字化的发展，中小企业在过去几年中显著提高了效率和生产力。随着中小企业利益相关者的数量不断增加，他们连接、访问、交换、添加和更改交易执行，中小企业交易执行的自动化流程变得高度多样化。中小企业的平衡生命周期需要合作伙伴关系交换、财务管理、制造和生产力稳定性，以及隐私和安全性。互操作性平台问题是设计和管理中小企业安全分布式对等工业开发环境的另一个关键挑战方面。然而，由于当前集中式服务器基础设施的性质，到目前为止，由于两个不同企业之间的当前性质，很难维护中小企业的完整性、透明度、可靠性、出处、可用性和可信度的运营。本文通过使用区块链、物联网 (IoT) 和人工智能 (AI) 与机器学习 (ML) 的协作技术，为分布式中小企业提出了一种新颖且安全的框架和标准化流程层次/生命周期，从而解决了这些问题。设计了一个具有物联网功能的无权限网络结构的区块链，称为“B-SMEs”，它为跨链平台提供了解决方案。在这方面，B-SMEs 还解决了轻量级利益相关者认证问题。为此，部署了三个不同的链码。它处理参与中小企业的注册、节点之间的日常信息管理和交换，以及在将交易详细信息保存到区块链不可变存储之前对合作伙伴关系交换相关交易细节进行分析。而利用人工智能支持的基于机器学习的人工神经网络，目的是处理和优化日常中小企业交易数量；因此，与中小企业服务交付的整个过程相比，所提出的 B-SMEs 在计算能力、网络带宽和保存相关问题方面消耗的资源更少。模拟结果突出了 B-SMEs 的优势，提高了在不同链之间交换信息时的分类帐管理和优化速度，高达 17.3%，并将系统计算资源的消耗降低到 9.13%。因此，与中小企业的当前机制相比，B-SME 的交易仅分别使用 14.11%和 7.9%的网络带宽和存储能力。
