Blockchain Non-Fungible Token for Effective Drug Traceability System with Optimal Deep Learning on Pharmaceutical Supply Chain Management
Received: 26 September 2024 | Revised: 16 October 2024, 21 October 2024, and 28 October 2024 | Accepted: 19 November 2024 | Online: 27 November 2024
Corresponding author: Venkatesh Kaliyamurthy
Abstract
In recent times, the number of fake drugs has increased dramatically, which has resulted in millions of victims severely affected by poisoning and treatment failures, resulting in a need for Drug Supply Chain (DSC) traceability. The DSC is generally reluctant to share traceability data and includes several parties having heterogeneous interests. Moreover, existing provenance and traceability systems for DSCs need more trust, data sharing transparency, and separated data storage. By realizing decentralized, trustless systems, a decentralized Blockchain (BC)-based solution is proposed to tackle these constraints. BC is an immutable, decentralized, shared network that allows management directly through a peer-to-peer (P2P) network without the necessity of a central authority to check transactions. This study proposes a new Blockchain Non-Fungible Token-based Drug Traceability with Enhanced Pharmaceutical Supply Chain Management (BNFTDT-EPSCM) model. The proposed BNFTDT-EPSCM model presents transparent and more secure reporting of changes in the operating condition of transported pharmaceutical products to prevent drug recalls. The Ethereum BC enables transactions and computational services using the cryptocurrency Ether (ETH). Simultaneously, an enhanced Byzantine fault-tolerant consensus (RB-BFT) leverages a reputation system to address reliability issues of primary nodes and reduce communication complexity inherent in the Practical Byzantine algorithm (PBFT). The BNFTDT-EPSCM model presents a decentralized solution using Non-Fungible Tokens (NFTs) to improve the traceability and tracking capabilities of the standard serialization process. In addition, the BNFTDT-EPSCM model employs a Deep Belief Network (DBN) approach to perform the inbound logistics task prediction process. Finally, the Tasmanian Devil Optimization (TDO) method is utilized to enhance the hyperparameter tuning of the DBN approach. A detailed set of simulations was executed to examine the effectiveness of the BNFTDT-EPSCM approach, demonstrating a higher throughput at the highest user count of 6000 and achieving 551.22 TPS, significantly outperforming existing models.
Keywords:
drug, blockchain, peer-to-peer, deep learning, non-fungible token, Tasmanian devil optimizationDownloads
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