Refine Security Control Protocols for Block chain in Textile Industry Supply Chain Management

Authors

DOI:

https://doi.org/10.21015/vtse.v14i2.2370

Abstract

Cotton is a vital crop with numerous environmentally friendly applications in our daily lives, making it a globally significant agricultural commodity. Pakistan ranks as the world's third-largest producer and consumer of cotton, utilizing 15% of its land for cultivation. However, challenges like supply chain malpractice and opacity lead to economic losses for farmers, textile industries, and governments. To tackle these issues, a block chain-based framework for supply chain traceability and reliability in the cotton and textile industry has been developed. This innovative solution aims to create transparency and trust among supply chain participants. This solution introduces Cotton Coin (CC) for transaction tracking and utilizes smart contracts for monitoring trading and currency conversion. The Inter Planetary File System (IPFS) securely stores encrypted data of supply chain participants. The experimental results demonstrate the effectiveness of the suggested framework in comparison to existing supply chain projects. Key performance metrics, such as new block latency, transactions per minute, average gas charges, and transaction verification times, have shown significant improvements. These findings highlight the potential of this block chain-based solution to enhance supply chain transparency, security, and efficiency in the cotton and textile industry.

References

H. R. Sarma et al., “Effect of carbonization behaviour of cotton biomass in electrodes for sodium-ion batteries,” ChemElectroChem, Art. no. e202300127, 2023.

M. A. ReHabib, M. Ahmad, S. Jabbar, S. Khalid, J. Chaudhry, K. Saleem, and M. S. Khalil, “Security and privacy based access control model for internet of connected vehicles,” Future Generation Computer Systems, vol. 97, pp. 687–696, 2019.

M. U. Farooq and M. O. Beg, “Big data analysis of Stack Overflow for energy consumption of Android framework,” in Proc. Int. Conf. Innovative Computing (ICIC), Lahore, Pakistan, Nov. 2019.

Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, “An overview of blockchain technology: Architecture, consensus, and future trends,” in Proc. IEEE Int. Congress on Big Data, pp. 557–564, 2017.

X. Li, J. Chen, and Y. Wu, “Blockchain for textile supply chain management: A review,” Journal of Textile Science and Technology, vol. 6, no. 3, pp. 1–9, 2020.

X. Bai, R. Tao, X. Du, and J. Wu, “A blockchain-based textile supply chain traceability system,” Journal of Industrial Information Integration, vol. 17, Art. no. 100127, 2020.

A. Zeb and G. Shabir, “Blockchain technology for supply chain management: A systematic literature review and future research directions,” Journal of Cleaner Production, vol. 259, Art. no. 120785, 2020.

R. Richero and S. Ferrigno, “A background analysis on transparency and traceability in the garment value chain,” European Commission, International Cooperation and Development Report.

P. Rogaway and T. Shrimpton, Cryptographic Hash-Function Basics: Definitions, Implications, and Separations for Preimage Resistance and Collision Resistance. Heidelberg, Germany: Springer, 2004.

K. Toyoda, P. T. Mathiopoulos, I. Sasase, and T. Ohtsuki, “A novel blockchain-based product ownership management system (POMS) for anti-counterfeits in the post supply chain,” IEEE Access, vol. 5, pp. 17465–17477, 2017.

A. Hekmat, Z. Zhang, S. U. R. Khan, I. Shad, and O. Bilal, “An attention-fused architecture for brain tumor diagnosis,” Biomedical Signal Processing and Control, vol. 101, Art. no. 107221, 2025.

S. U. R. Khan, A. Raza, M. Waqas, and M. A. R. Zia, “Efficient and accurate image classification via spatial pyramid matching and SURF sparse coding,” Lahore Garrison University Research Journal of Computer Science and Information Technology, vol. 7, pp. 10–23, 2023.

A. Hekmat, Z. Zhang, O. Bilal, and S. U. R. Khan, “Differential evolution-driven optimized ensemble network for brain tumor detection,” International Journal of Machine Learning and Cybernetics, pp. 1–26, 2025.

P. K. Sharma, N. Kumar, and J. H. Park, “Blockchain-based distributed framework for automotive industry in a smart city,” IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4197–4205, 2018.

S. U. R. Khan, “Multi-level feature fusion network for kidney disease detection,” Computers in Biology and Medicine, vol. 191, Art. no. 110214, 2025.

A. Villalva-Cataño, E. Ramos-Palomino, K. Provost, and E. Casal, “A model in agri-food supply chain costing using ABC costing: An empirical research for Peruvian coffee supply chain,” in Proc. 7th Int. Eng., Sciences and Technology Conf. (IESTEC), pp. 1–6, 2019.

S. U. R. Khan and S. Asif, “Oral cancer detection using feature-level fusion and novel self-attention mechanisms,” Biomedical Signal Processing and Control, vol. 95, Art. no. 106437, 2024.

T. Ferdousi, D. Gruenbacher, and C. M. Scoglio, “A permissioned distributed ledger for the US beef cattle supply chain,” IEEE Access, vol. 8, pp. 154833–154847, 2020.

A. Kumar and A. Mishra, “Blockchain based security and privacy solution for textile supply chain management,” Journal of Cleaner Production, 2021.

S. U. R. Khan and Z. Khan, “Detection of abnormal cardiac rhythms using feature fusion technique with heart sound spectrograms,” Journal of Bionic Engineering, pp. 1–20, 2025.

U. Rahardja, A. N. Hidayanto, N. Lutfiani, D. A. Febiani, and Q. Aini, “Immutability of distributed hash model on blockchain node storage,” Scientific Journal of Informatics, vol. 8, no. 1, pp. 137–143, 2021.

M. A. Al-Khasawneh, A. Raza, S. U. R. Khan, and Z. Khan, “Stock market trend prediction using deep learning approach,” Computational Economics, pp. 1–32, 2024.

U. S. Khan, M. Ishfaque, S. U. R. Khan, F. Xu, L. Chen, and Y. Lei, “Comparative analysis of twelve transfer learning models for crack detection in concrete dams using borehole images,” Frontiers of Structural and Civil Engineering, pp. 1–17, 2024.

S. U. R. Khan et al., “Robust and precise knowledge distillation-based context-aware predictor for disease detection in brain and gastrointestinal systems,” 2025.

Q. Dai, M. Ishfaque, S. U. R. Khan, Y. Luo, Y. Lei, B. Zhang, and W. Zhou, “Image classification for subsurface crack identification in concrete dams using borehole CCTV images with deep dense hybrid model,” Stochastic Environmental Research and Risk Assessment, pp. 1–18, 2024.

Y. P. Tsang, K. L. Choy, C. H. Wu, G. T. S. Ho, and H. Y. Lam, “Blockchain-driven IoT for food traceability with an integrated consensus mechanism,” IEEE Access, vol. 7, pp. 129000–129017, 2019.

H. Treiblmaier, “The impact of blockchain on the supply chain: A theory-based research framework and a call for action,” Supply Chain Management: An International Journal, vol. 23, pp. 545–559, 2018.

U. S. Khan and S. U. R. Khan, “Boosting diagnostic performance in retinal disease classification utilizing deep ensemble classifiers based on OCT,” Multimedia Tools and Applications, pp. 1–21, 2024.

S. U. R. Khan, S. Asif, O. Bilal, et al., “LEAD-CNN: Lightweight enhanced dimension reduction convolutional neural network for brain tumor classification,” International Journal of Machine Learning and Cybernetics, 2025.

A. Raza, M. T. Meeran, and U. Bilhaj, “Enhancing breast cancer detection through thermal imaging and customized 2D CNN classifiers,” VFAST Transactions on Software Engineering, vol. 11, pp. 80–92, 2023.

S. Inzamam, J. Ouyang, and S. Khan, “FedVC-ADDiM: A federated learning framework for diagnosis of Alzheimer disease using deep learning,” Multimedia Systems, vol. 32, no. 3, p. 161, 2026.

S. Khan, M. N. Asim, S. Vollmer, and A. Dengel, “FloraSyntropy-net: Scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis,” Plant Methods, 2026.

M. Hasaan, S. U. R. Khan, S. Vollmer, A. Dengel, and M. N. Asim, “Automated diabetic screening via anterior segment ocular imaging: A deep learning and explainable AI approach,” arXiv preprint arXiv:2603.14727, 2026.

I. Misbah, S. U. R. Khan, A. U. Rehman, S. Vollmer, A. Dengel, and M. N. Asim, “StructDamage: A large scale unified crack and surface defect dataset for robust structural damage detection,” arXiv preprint arXiv:2603.10484, 2026.

H. Arash, O. Bilal, Z. Zhang, S. U. R. Khan, and S. Asif, “FRE-Net: A fuzzy Richards functions-based ensemble network for brain tumor detection,” Journal of Bionic Engineering, pp. 1–23, 2026.

B. Omair, A. Hekmat, S. U. R. Khan, A. Raza, and G. Ali, “MS-STO-Net: A multi-scale state transition optimization-based ensemble network for accurate white blood cell classification,” in Proc. 27th Int. Multitopic Conf. (INMIC), pp. 1–6, IEEE, 2025.

Downloads

Published

2026-04-22

How to Cite

Raza, A., Salahuddin, Latif, S., & Ali, G. (2026). Refine Security Control Protocols for Block chain in Textile Industry Supply Chain Management. VFAST Transactions on Software Engineering, 14(2), 62–80. https://doi.org/10.21015/vtse.v14i2.2370