Assessing the Efficacy of Distributed Ledger Technology for Immutable Data Logging and Fraud Prevention in IoT Retail Applications

Abstract

This paper presents a comprehensive framework for assessing the efficacy of Distributed Ledger Technology (DLT) in IoT retail applications. The framework integrates five key algorithms: Data Validation Algorithm (DVA), Consensus Mechanism Algorithm (CMA), Fraud Detection Algorithm (FDA), Privacy-Preserving Algorithm (PPA), and Dynamic Smart Contract Algorithm (DSCA). Each algorithm addresses specific challenges, ensuring data integrity, achieving consensus, preventing fraud, preserving privacy, and enhancing smart contract adaptability. The ablation study evaluates the individual contributions of each algorithm, highlighting their significance in the overall framework. Comparative performance evaluations demonstrate the proposed framework's superiority, with higher scores across key parameters compared to existing assessment methods. The framework offers a robust solution for enhancing the security, integrity, and efficiency of DLT in IoT retail applications, addressing multifaceted challenges and requirements.